policy

ATLANTA (AP) — Jimmy Carter, the peanut farmer who tried to revive virtue within the White House after the Watergate scandal and the Vietnam War, then recovered from a landslide defeat to turn out to be a world champion of human rights and democracy, has died. He was 100 years old.

The Carter Center said the thirty ninth president died Sunday, greater than a yr after entering hospice care, at his home in Plains, Georgia, where he and his wife, Rosalynn, who died in November 2023, lived most of their lives .

Carter, a moderate Democrat, ran for president in 1976 because the little-known governor of Georgia with a broad grin, exuberant Baptist faith and technocratic plans for efficient government. His promise to never deceive the American people was well received after Richard Nixon's disgrace and the U.S. defeat in Southeast Asia.

“If I ever lie to you, if I ever make a misleading statement, don’t vote for me. “I don’t deserve to be your president,” Carter said.

Carter's victory over Republican Gerald Ford, whose fortunes plummeted after Nixon's pardon, got here amid the pressures of the Cold War, turbulent oil markets and social unrest over race, women's rights and America's role on this planet. His achievements included brokering peace within the Middle East by detaining Egyptian President Anwar Sadat and Israeli Prime Minister Menachem Begin at Camp David for 13 days in 1978.

But his coalition splintered under double-digit inflation and the 444-day hostage crisis in Iran. His negotiations eventually brought all of the hostages home alive, but in a final insult, Iran didn’t release them until the inauguration of Ronald Reagan, who had defeated him within the 1980 election.

Humbled and at home in Georgia, Carter said his faith required him to do every little thing he could for so long as possible to attempt to make a difference. He and Rosalynn co-founded the Carter Center in 1982 and spent the following 40 years traveling the world as peacemakers, human rights activists, and advocates for democracy and public health.

Winner of the Nobel Peace Prize in 2002, Carter helped reduce nuclear tensions in North and South Korea, avert a U.S. invasion of Haiti and negotiate ceasefires in Bosnia and Sudan. As of 2022, the middle had monitored at the least 113 elections worldwide. Carter was determined to eradicate Guinea worm infections as one in every of many public health initiatives. In their 90s, the Carters built homes with Habitat for Humanity.

The general statement that he was higher as a former president annoyed Carter. His allies were pleased that he lived long enough to see biographers and historians revisit his presidency and declare it more impactful than many believed on the time.

Fueled by voters in Iowa after which throughout the South in 1976, Carter ran a no-frills campaign. Americans were captivated by the intense engineer, and while an election yr Playboy interview drew laughter when he said he had “looked at a lot of women with lust.” I actually have often committed adultery in my heart,” voters who were bored with political cynicism found this appealing.

The first family set an off-the-cuff tone on the White House by carrying their very own luggage, attempting to silence the Marine Band's traditional “Hail to the Chief” and enrolling daughter Amy in public schools. Carter was mocked for wearing a cardigan and urging Americans to show down their thermostats.

But Carter set the stage for an economic revival and significantly reduced America's dependence on foreign oil by deregulating the energy industry in addition to airlines, trains and trucking. He created the Departments of Energy and Education, appointed record numbers of girls and non-whites to federal office, preserved tens of millions of acres of Alaskan wilderness, and pardoned many of the draft dodgers in Vietnam.

He emphasized human rights, largely ended support for military dictators and addressed bribery by multinational corporations by signing the Foreign Corrupt Practices Act. He convinced the Senate to ratify the Panama Canal Treaties and normalize relations with China, a results of Nixon's efforts to woo Beijing.

But paralyzing shifts in foreign policy took their toll.

As OPEC raised crude oil prices and drivers had to attend in line to replenish with gas as inflation hit 11%, Carter tried to encourage Americans to beat “a crisis of confidence.” Many voters as a substitute lost faith in Carter after the infamous speech, which the media called his “malaise speech,” though he never used that word.

After Carter reluctantly agreed to confess the exiled Shah of Iran to the United States for medical treatment, the American embassy in Tehran was overrun in 1979. Negotiations to quickly release the hostages failed, after which eight Americans died in a top-secret military rescue attempt gone fallacious.

After the Soviets invaded Afghanistan in 1979, Carter also needed to make a U-turn on the SALT II nuclear weapons treaty. Although historians later credited Carter's diplomatic efforts with hastening the top of the Cold War, Republicans characterised his soft power as weak. Reagan's appeals to “Make America Great Again” were well received, and he defeated Carter in all but six states.

James Earl Carter Jr. was born on October 1, 1924 and married his compatriot Rosalynn Smith in 1946, the yr he graduated from the Naval Academy. He brought his young family back to Plains after his father's death and gave up his Navy profession. They soon turned their ambitions to politics. Carter reached the state Senate in 1962. After rural white and black voters elected him governor in 1970, he gained national attention by declaring that “the time for racial discrimination is over.”

Carter published greater than 30 books and remained influential as his center focused its democracy advocacy on U.S. politics and oversaw an audit of the 2020 presidential election ends in Georgia.

After a cancer diagnosis in 2015, Carter said he was “completely comfortable with whatever comes.”

“I had a wonderful life,” he said. “I had thousands of friends, I had an exciting, adventurous and fulfilling life.”

Max Kim, Los Angeles Times (TNS)

SEOUL, South Korea — It's hard to search out anyone here who would have expected Han Kang to win the Nobel Prize for Literature, the world's highest literary honor, in 2024.

Although the South Korean author has won quite a few other prestigious international awards and is widely read here, she is 53 years old and the prize is traditionally given to writers at the tip of their careers.

“I thought she might win it one day, but I didn't expect it to happen so soon,” said Jeong Kwa-ri, a literary critic and former professor of Korean literature at Yonsei University, Hans Alma Mater. “Most South Korean writers considered top contenders are in their 70s and 80s.”

Honored last week by the Swedish Academy “for her intense poetic prose that grapples with historical trauma and exposes the fragility of human life,” Han is the primary Asian woman to win the Nobel Prize in Literature in its 123-year history, and the second South Korean woman to win the Nobel Prize. Then-President Kim Dae-jung received the Nobel Peace Prize in 2000 for his diplomacy with North Korea.

Han kept a low profile after the victory, reportedly refusing a celebration planned by her father, citing the wars still raging Gaza And Ukraine. But the remaining of the country is affected by “Han Kang Syndrome”.

As of Tuesday, the country's booksellers have reported greater than 800,000 sales of Han's works and expect to succeed in the 1 million mark by the tip of the week. Stores combating long queues are selling out quickly and printers are working across the clock to provide more.

Han, who was born in the town of Gwangju in 1970, comes from a literary family. Her father is Han Sung-won, a famous author who has happily noted that his daughter's stature has dwarfed his own.

“Before, Han Kang was known as the daughter of Han Sung-won, but now I have become Han Sung-won, the father of Han Kang,” he said in a 2016 interview.

Many of Han's novels are intimate portraits of violence in on a regular basis life, encompassing each South Korea's long history of authoritarian rule feminist struggles the current.

One of her most famous works in South Korea is “Human acts” a novel concerning the Chun Doo-hwan the massacre of civilians by the military dictatorship in 1980 following pro-democracy protests in the town of Gwangju.

The public debate over the massacre has long troubled South Korean conservatives, who’ve at times tried to downplay the federal government's role or spread conspiracy theories that the protests were an act of North Korean deception.

Under the previous president's conservative government Park Geun-hyeHan, the daughter of one other military dictator, was blacklisted in 2014, barring her from government support like other creatives deemed ideologically undesirable.

Told from multiple perspectives, “Human Acts” is inspired by real-life characters, including Moon Jae-hak, a highschool student who was shot by junta troops stationed in Gwangju.

“I was so happy that I thought my heart would stop,” Kim Kil-ja, Moon's mother, said of Hans Nobel in an interview with local media. “Your book managed to spread the truth about the incident to the world.”

Han's own advice for those just diving into her work is We Do Not Part, a novel that addresses a massacre of civilians carried out by the South Korean government on Jeju Island in 1948, a time of anti-communist paranoia. The English translation of the novel, which won the French Prix Médicis last yr, is scheduled to be published in January 2025.

But Hans' most famous – and infamous – work is “The Vegetarian” a dark, surreal story a couple of woman who goes mad after vowing to present up meat. Hailed as a parable about female resistance to patriarchal South Korean society, the novel won the 2016 Man Booker International Prize, an honor shared by Han and her British translator Deborah Smith.

But the award put the book at the middle of a heated debate about literary translations. Critics said the award-winning English translation by Smith, who had begun learning Korean only a couple of years earlier, not only made fundamental errors – comparable to confusing the Korean word for “foot” with “arm” – but additionally broadened the text Furthermore, acceptable translation parameters are modified.

“Translations of Korean literature have long suffered from many obstacles, and 'pure' translations have not been successful,” said Jeong, the literary critic.

The query has long plagued the country's literary scene, which has watched the South Korean film and tv industry produce global hits like “Parasite” and “Parasite.” “Squid Game” At the identical time, I ponder why South Korean books have did not attract the identical level of worldwide interest.

“For this reason, there is an increasing tendency in translations to overlook distortions of the original text and prefer to adapt to the tastes of foreign readers,” Jeong said. “'The Vegetarian' is a prime example of this.”

Write Writing for The Times in 2016, Charse Yun, a Korean-American literary translator, acknowledged Smith's “exquisite” sentences but said the interpretation had “transformed into a 'new creation.'”

“It's hard for me to find an appropriate analogy, but imagine the sleek, contemporary style of Raymond Carver, topped with the sophisticated diction of Charles Dickens,” he wrote.

Smith, who has translated two of Hans' other books, defended her work in an essay for the Los Angeles Review of Books in 2018, arguing that given the differences between two languages, “there can be no such thing as a translation.” ,creative'.”

For many critics, the question of translation is still open. But for better or worse, Han's most recent and most prestigious award has now laid the foundation for the global success of Korean literature.

Despite his doubts about Smith's translation, Yun today sees many reasons to be optimistic.

“The field opened up tremendously and more people had access to Korean literature,” Yun said of Han's global rise.

“I’m just happy for my former students and other talented translators out there who now have the opportunity to bring other Korean voices into the field.”

How did you begin fascinated with this fundamental query at the center of the invention of microRNA: How cells receive the instructions to do what they do?

The A paper describing this discovery was published in 1993. In the late Nineteen Eighties we worked and studied in the sphere of developmental biology as a model organism for animal development. We used genetic approaches that tracked mutations that caused developmental disorders to know which gene was mutated and what gene product it was.

It was well-known that proteins could mediate changes in gene expression as cells differentiate and divide.

We didn’t search for the involvement of unexpected molecular mechanisms. The proven fact that the microRNA was the product of this gene, which on this context regulated this other gene, was an entire surprise.

There was no reason to consider that there must be such regulators of gene expression. This is one in every of those examples where you're expected to learn more concerning the complexities and nuances of the mechanisms we already know.

But sometimes there are surprises, and in truth surprises are perhaps surprisingly common.

Is there something about these worms, nematodes, that means that you can work with their genetic material more easily? Why are they so necessary for any such science?

was developed as an experimental organism that folks could easily use to first discover mutants after which study development.

It only has a few thousand cells, and all of those cells could be easily seen within the living animal through a microscope. Yet it has all different parts which can be necessary to all animals: intestines, skin, muscles, a brain, sensory systems and sophisticated behavior. So it's a reasonably amazing system to actually study developmental processes and mechanisms at the extent of individual cells and what those cells do as they divide and differentiate during development.

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They checked out this gene. What was your surprising discovery that led to this Nobel Prize?

In our laboratory Rosalind Lee And Rhonda Feinbaum have worked on this project for several years. This is a really labor-intensive process attempting to track down a gene.

And all we would have liked was a mutation that may progressively bring us closer to the DNA sequence that contained the gene. The surprises got here once we found that the pieces of DNA that were enough to transfer the function of this gene and rescue a mutant were really small, only 800 base pairs.

So that suggested that the gene is small, so the product of this gene might be quite small. And then Rosalind worked to further reduce the sequence and mutate potential protein-coding sequences on this small piece of DNA. Through an elimination process, she finally showed that there was no protein that might be expressed from this gene.

And at the identical time, we identified this very, very small transcript of just 22 nucleotides. So I’d say there was probably per week or two time frame where these insights emerged and we knew we had something latest.

You mentioned Rosalind, she is your wife.

Yes, we've been together since 1976. And we began working together within the mid-80s. And that's how we still work together today.

And she was the primary writer of this work.

That's right. It is difficult to precise how wonderful it’s to receive such acknowledgment of our work together. That is just priceless.

As if it were a Nobel Prize for them too?

Yes, every Nobel Prize is subject to this obvious limitation on the number of individuals to whom it’s awarded. But behind it, after all, are the individuals who worked within the lab – the teams actually behind the discoveries are sometimes surprisingly large. In this case, two people in my lab and a number of other people in Gary Ruvkun's lab.

In a way, they are surely the heroes behind all of it. Our job – mine and Gary's – is to act as representatives of this complete scientific enterprise that’s so, so depending on teams, collaboration, brainstorming between multiple people, communication of ideas and necessary data, you understand, all of that is a component of it Process that underlies successful science.

Did you anticipate in the primary week of discoveries that this might be such an enormous step forward in our understanding of genes?

Until more examples of something latest are found, it is rather difficult to say how peculiar this particular phenomenon is likely to be.

We are at all times aware that evolution is amazingly progressive. And so it might be that this special small RNA base pairing with this mRNA of the gene and turning off the production of the protein from messenger RNA might be a special evolutionary innovation.

The second microRNA was identified in Gary Ruvkun's lab in 1999, so it took a superb six years until the second specimen was also found. The turning point was really the invention when Ruvkun showed this the opposite microRNAwas actually preserved in perfect order amongst all of them bilateral animals. So that meant microRNA had been around for about 500 million years?

And so it was immediately clear to the sphere that there needed to be other microRNAs – it wasn't only one thing. There should be others, and that quickly became apparent.

You and Gary Ruvkun were postdoctoral fellows at MIT at the identical time, but by the point you made your respective discoveries you had each established your personal labs. Would you describe them as rival labs in the identical city?

No, I’d definitely not call it a competitive lab. We essentially worked on this problem of development timing as postdocs Bob Horvitz's laboratory.

We principally just divided the work informally. The agreement was, OK, the Ambros lab will give attention to genes and the Ruvkun lab will give attention to, and we assumed that there can be a degree where we’d get together and share details about what now we have learned and see if we could come to a synthesis.

That was the informal plan. It wasn't really a collaboration. It definitely wasn't a rivalry. The expectation was that we’d divide the work after which communicate when the time got here. There was an expectation on this research community that data must be shared freely.

Her lab remains to be working on microRNA. What are you investigating? What questions do you continue to have?

One that I find very interesting is a project where we worked with a clinician, a geneticist, who studies mental disability. She had found that her patients, children with mental disabilities, in certain families carried a mutation that neither of their parents had – a spontaneous mutation – within the protein related to microRNAs in humans, called the Argonaute protein.

Each of our genomes incorporates 4 Argonaut genes, that are the partners of microRNAs. In fact, that is the effector protein that’s guided by the microRNA to its goal messenger RNAs. This Argonaut carries out the regulatory processes that happen once it finds its goal.

These so-called Argonaut syndromes were discovered, wherein mutations occur in Argonauts, i.e. point mutations wherein just one amino acid changes to a different. They have this very profound and comprehensive effect on the event of the person.

And so our lab and other labs, working with these geneticists, extracted those mutations that were essentially gifted to us by the patient. And then We have incorporated these mutations into our systemin our case in 'Argonaut.

I’m passionate about the very organized, lively partnership between the Argonaut Alliance from families with Argonaute syndromes and the essential scientists who study Argonaute.

How can this collaboration potentially help these patients?

We discovered that the mutated protein is a few type of rogue Argonaut. It principally messes up the conventional process that these 4 Argonauts normally have of their bodies. And so this rogue Argonaut could principally be faraway from the system by attempting to use a number of the technologies that folks are developing for Gene knockout or RNA interference of genes.

This is promising and I hope it can repay for patients within the years to come back.

World News

The Nobel Peace Prize was awarded Friday to Nihon Hidankyo, a Japanese organization of survivors of the U.S. atomic bombings of Hiroshima and Nagasaki, for its activism against nuclear weapons.

Jørgen Watne Frydnes, chairman of the Norwegian Nobel Committee, said the award was given because “the taboo against the use of nuclear weapons is coming under pressure.”

Last month, Russian President Vladimir Putin announced a change to his country's nuclear doctrine to stop the West from allowing Ukraine to attack Russia with longer-range weapons. It appeared to significantly lower the brink for the possible use of Russia's nuclear arsenal.

Watne Frydnes said the Nobel Committee “would like to honor all survivors who, despite physical suffering and painful memories, chose to use their costly experience to cultivate hope and commitment to peace.”

Hidankyo Hiroshima Branch Chairman Tomoyuki Mimaki, who was readily available on the town hall for the announcement, cheered and cried when he received the news.

“Is it really true? Unbelievable!” Mimaki screamed.

Efforts to abolish nuclear weapons have already been recognized by the Nobel Committee. The International Campaign to Abolish Nuclear Weapons won the Peace Prize in 2017, and in 1995 Joseph Rotblat and the Pugwash Conferences on Science and World Affairs won for “their efforts to reduce the role of nuclear weapons in international politics and in the longer term.” To eliminate weapons.”

This yr's award was presented against the backdrop of devastating conflicts within the Middle East, Ukraine and Sudan.

“It is very clear that the threat of using nuclear weapons puts pressure on the important international norm, the taboo on the use of nuclear weapons,” Watne Frydnes said in response to a matter about whether Russia's rhetoric around nuclear weapons in its invasion of Ukraine voted had influenced this yr's decision.

“And that’s why it’s alarming to see how threats of use are damaging this norm too. “It is vital for all humanity to maintain a strong international taboo against its use,” he added.

EU Commission President Ursula von der Leyen said on This Nobel Peace Prize sends a powerful message. We have an obligation to recollect. And a fair greater duty to guard future generations from the horrors of nuclear war.”

Alfred Nobel stated in his will that the Peace Prize must be awarded for “the greatest or best work for brotherhood among nations, for the abolition or reduction of standing armies, and for the holding and promotion of peace congresses.”

Last yr's award went to the imprisoned Iranian activist Narges Mohammadi for her advocacy for ladies's rights and democracy and against the death penalty. The Nobel Committee said it was also a recognition of “the hundreds of thousands of people” who demonstrated against “the policies of discrimination and oppression of women by Iran's theocratic regime.”

In a yr of conflict, there was speculation that the Norwegian Nobel Committee, which decides the winner, would select to not award a prize in any respect. The prize has been withheld 19 times since 1901, including during each world wars. The last time it was not awarded was in 1972.

Rising violence within the Middle East has killed tens of 1000’s of individuals over the past yr, including women and youngsters. The war, sparked by a bloody raid on Israel by Hamas-led militants on October 7, 2023, that left about 1,200 people, mostly civilians, dead, has spread to the broader region.

Last week, Israel sent ground troops to Lebanon to pursue Hezbollah militants who were firing rockets at Israel, while Iran – which supports each Hamas and Hezbollah – fired ballistic missiles at Israel. Israel has not yet responded, but its defense minister promised this week that its retaliation could be each devastating and surprising.

The war in Gaza has killed greater than 42,000 people, in keeping with the Gaza Health Ministry. The count doesn’t differentiate between civilians and combatants; greater than half of them are women and youngsters. In Lebanon, greater than 1,400 people have been killed, 1000’s more injured and around one million displaced since mid-September, when the Israeli military dramatically expanded its offensive against Hezbollah.

The war in Ukraine, sparked by the Russian invasion, is approaching its third winter with massive lack of life on each side.

The United Nations has confirmed that greater than 11,000 Ukrainian civilians have died. However, this doesn’t consider the as much as 25,000 Ukrainians believed to have been killed in Russia's capture of town of Mariupol, or unreported deaths in occupied territories.

The Nobel Prizes are value 11 million Swedish crowns (US$1 million). In contrast to the opposite prizes, that are chosen and announced in Stockholm, founder Alfred Nobel decreed that the Peace Prize must be decided and awarded in Oslo by the five-member Norwegian Nobel Committee.

The Nobel season ends on Monday with the announcement of the winner of the economics prize, officially often called the Bank of Sweden Prize in Economic Sciences in memory of Alfred Nobel.

John J Hopfield And Geoffrey E. Hinton received the Nobel Prize in Physics on October 8, 2024 for his or her research Machine learning and neural network algorithms that help computers learn. Her work was fundamental to the event of neural network theories underlying generative artificial intelligence.

A neural network is a computational model consisting of layers of interconnected neurons. As the Neurons in your brainThese neurons process and send information. Each neural layer receives a chunk of information, processes it and passes the result to the following layer. At the tip of the sequence, the network has processed the information and refined it into something more useful.

Although it could appear surprising that Hopfield and Hinton received the physics prize for his or her contributions to neural networks utilized in computer science, their work is deeply rooted within the principles of physics, particularly in a subfield called ” Statistical mechanics.

As computational Materials scientistI used to be pleased that this area of ​​research was recognized with the prize. Hopfield and Hinton's work has allowed my colleagues and I to explore a process called generative learning for materials science, a way that underlies many popular technologies comparable to ChatGPT.

What is Statistical Mechanics?

Statistical mechanics is a branch of physics that uses statistical methods to clarify the behavior of systems composed of enormous numbers of particles.

Instead of specializing in individual particles, researchers use statistical mechanics to take a look at the collective behavior of many particles. Seeing how all of them work together helps researchers understand the system's large-scale macroscopic properties, comparable to temperature, pressure and magnetization.

For example, Physicist Ernst Ising developed a statistical-mechanical model for magnetism within the Twenties. Ising imagined magnetism because the collective behavior of Atomic spins interact with their neighbors.

In the Ising modelthere are higher and lower energy states for the system, and the fabric is more prone to exist in the bottom energy state.

A key idea in statistical mechanics is that Boltzmann distributionwhich quantifies how likely a certain condition is. This distribution describes the probability that a system is in a certain state based on its energy and temperature – be it solid, liquid or gaseous.

Ising accurately predicted the phase transition of a magnet using the Boltzmann distribution. He determined the temperature at which the fabric modified from magnetic to non-magnetic.

Phase changes occur at predictable temperatures. Ice melts into water at a certain temperature since the Boltzmann distribution predicts that the water molecules usually tend to assume a disordered – or liquid – state when heated.

Atoms arrange themselves in materials specific crystal structures that use the least energy. When cold, water molecules freeze into ice crystals with low energy states.

The same applies in biology: Proteins fold into low-energy shapeswhich permit them to act as specific antibodies – like a lock and key – against a virus.

Neural networks and statistical mechanics

Basically, all neural networks work based on the same principle – energy minimization. Neural networks use this principle to resolve computational problems.

For example, imagine a picture made up of pixels where you’ll be able to only see a part of the image. Some pixels are visible while the remaining are hidden. To determine which image it’s, consider all of the ways the hidden pixels might fit along with the visible parts. From there, you’ll select amongst all possible options from the almost definitely states based on statistical mechanics.

Hopfield and Hinton developed a theory of neural networks based on the thought of ​​statistical mechanics. Just like Ising before them, who modeled the collective interaction of atomic spins to resolve the photography problem with a neural network, Hopfield and Hinton imagined collective interactions of pixels. They represented these pixels as neurons.

Just like in statistical physics, the energy of a picture refers to how likely a selected pixel configuration is. A Hopfield network would solve this problem by finding the bottom energy arrays of hidden pixels.

But unlike statistical mechanics – where energy is set by known atomic interactions – neural networks learn these energies from data.

Hinton popularized the event of a method called backpropagation. This technique helps the model determine the interaction energies between these neurons, and this algorithm underlies much of contemporary AI learning.

The Boltzmann machine

Building on Hopfield's work, Hinton imagined one other neural network called Boltzmann machine. It consists of visible neurons that we will observe and hidden neurons that help the network learn complex patterns.

In a Boltzmann machine you’ll be able to determine the probability that the image will look a certain way. To find this probability, you’ll be able to summarize all possible states that the hidden pixels might be in. This gives you the general probability that the visible pixels are in a selected arrangement.

My group worked on it Implementing Boltzmann machines in quantum computers for generative learning.

In generative learning, the network learns to generate latest data samples which are just like the information that researchers fed to the network for training. For example, it could generate latest images of handwritten numbers after being trained on similar images. The network can generate these by sampling from the learned probability distribution.

Generative learning is the premise of contemporary AI – it enables the generation of AI art, videos and text.

Hopfield and Hinton have significantly influenced AI research through the use of tools from statistical physics. Her work draws parallels between the best way nature determines the physical states of a fabric and the best way neural networks predict the probability of solutions to complex computer science problems.

The 2024 Nobel Prize in Chemistry recognized Demis Hassabis, John Jumper And David Baker for the usage of machine learning to tackle certainly one of the largest challenges in biology: predicting the 3D shape of proteins and designing them from the bottom up.

This 12 months's award stood out for recognizing research that comes from a technology company: DeepMind, an AI research startup acquired by Google in 2014. Most of the Nobel Prizes in Chemistry thus far have gone to academic researchers. Many award winners then founded startup firms to further expand and commercialize their groundbreaking work – for instance CRISPR gene editing technology And Quantum dots – however the research, from start to complete, was not conducted in a business setting.

Although the Nobel Prizes for Physics and Chemistry are awarded individually, there’s an interesting connection between the winning research in these fields in 2024. The Physics Prize went to 2 computer scientists who laid the inspiration for machine learning, while the chemistry laureates were rewarded for his or her use of machine learning to resolve certainly one of biology's best mysteries: how proteins fold.

The 2024 Nobel Prizes underscore each the importance of such a artificial intelligence and the indisputable fact that science today often crosses traditional boundaries and combines different fields to realize groundbreaking results.

The challenge of protein folding

Proteins are the molecular machines of life. They make up a major a part of our body, including muscles, enzymes, hormones, blood, hair and cartilage.

It is significant to know the structures of proteins because their shape determines their functions. In 1972 Christian Anfinsen received the Nobel Prize in chemistry for detecting the sequence of amino acid constructing blocks of a protein determines the form of the proteinwhich in turn affects its function. If a protein folds incorrectly, it might not function properly and might result in diseases equivalent to: Alzheimer, Cystic fibrosis or diabetes.

The overall shape of a protein will depend on the tiny interactions, attractions and repulsions between all of the atoms of the amino acids that compose it. Some wish to be together, some don't. The protein twists and folds into its final shape based on many 1000’s of those chemical interactions.

For many years, certainly one of the best challenges in biology has been predicting the form of a protein based solely on its amino acid sequence. Although researchers can now predict shape, we still don't understand how the proteins maneuver themselves into their specific shapes and minimize the repulsions of all interatomic interactions in a number of microseconds.

To understand how proteins work and stop misfolding, scientists needed a approach to predict how proteins fold, but solving this puzzle was no easy task.

In 2003, he became a biochemist on the University of Washington David Baker wrote Rosettaa pc program for designing proteins. In doing so, he showed that it was possible to reverse the issue of protein folding Designing a protein shape after which predicting it the amino acid sequence needed for production.

It was an exceptional breakthrough, however the form chosen for the calculation was easy and the calculations complex. To routinely design recent proteins with desired structures required a serious paradigm shift.

A brand new era of machine learning

Machine learning is a type of AI by which computers learn to resolve problems by analyzing large amounts of information. It has been utilized in various fields, from Play And Speech recognition To autonomous vehicles And scientific research. The idea behind machine learning is to take advantage of hidden patterns in data to reply complex questions.

This approach took an enormous leap in 2010 when Demis Hassabis was a co-founder DeepMindan organization that goals to mix neuroscience with AI to resolve real-world problems.

Hassabis, a 4-year-old chess prodigy, quickly made headlines AlphaZeroan AI that has taught itself to play chess at a superhuman level. In 2017, AlphaZero significantly beat the world's best computer chess program Stockfish-8. AI's ability to learn from its own gameplay quite than counting on pre-programmed strategies marked a turning point within the AI ​​world.

Soon after, DeepMind applied similar techniques to Go, an ancient board game known for its immense complexity. In 2016, his AI program AlphaGo defeated certainly one of the world's best players, Lee Sedol, in a single widely seen game that amazed tens of millions.

In 2016, Hassabis shifted DeepMind's focus to a brand new challenge: the issue of protein folding. Under the direction of John Jumpera chemist with a background in protein science, began the AlphaFold project. The team used a big database of experimentally determined protein structures to coach the AI ​​to learn the principles of protein folding. The result was AlphaFold2, an AI that might predict the 3D structure of proteins based on their amino acid sequences with remarkable accuracy.

This was a serious scientific breakthrough. AlphaFold has since predicted the structures of over 200 million proteins – essentially all of the proteins that scientists have sequenced thus far. The extensive database of protein structures is now freely available and accelerates research in biology, medicine and drug development.

Designer proteins to fight disease

Understanding the folding and performance of proteins is crucial for developing recent drugs. Enzymesa style of protein, act as catalysts in biochemical reactions and might speed up or regulate these processes. To treat diseases equivalent to cancer or diabetes, researchers often goal specific enzymes involved in disease pathways. By predicting the form of a protein, scientists can determine where small molecules—potential drug candidates—might bind to it. This is step one in developing recent drugs.

In 2024, DeepMind was launched AlphaFold3an updated version of the AlphaFold program that not only predicts protein shapes but additionally identifies potential binding sites for small molecules. This advance makes it easier for researchers to develop drugs that focus on exactly the correct proteins.

Google bought Deepmind for supposedly around Half a billion dollars in 2014. Google DeepMind has now launched a brand new company: Isomorphic labsto collaborate with pharmaceutical firms on real-world drug development using these AlphaFold3 predictions.

For his part, David Baker has continued to make significant contributions to protein science. His team on the University of Washington developed an AI-based method called “family-wide hallucination“, which they used to develop completely recent proteins from scratch. Hallucinations are recent patterns—on this case, proteins—which are plausible, meaning they fit well with patterns within the AI's training data. These recent proteins included a light-emitting enzyme, showing that machine learning may help develop novel synthetic proteins. These AI tools offer recent opportunities to engineer functional enzymes and other proteins that might never have evolved naturally.

AI will enable the following chapter of research

The Nobel Prize-winning achievements of Hassabis, Jumper and Baker show that machine learning will not be only a tool for computer scientists – it’s now a vital a part of the long run of biology and medicine.

By tackling certainly one of biology's most difficult problems, the 2024 Prize winners have opened up recent possibilities in drug discovery, personalized medicine, and even our understanding of the chemistry of life itself.

Two Google DeepMind scientists shared the 2024 Nobel Prize in Chemistry with a US professor for his or her groundbreaking protein research.

Half of the 11 million kronor ($1.1 million) prize might be split between Demis Hassabis, CEO of DeepMind, and John M. Jumper, a senior research scientist at the corporate, a Google subsidiary, The Royal The Swedish Academy of Sciences in Stockholm announced this in a press release on Wednesday.

RELATED: Former Google engineer and 'Godfather of AI' Geoffrey Hinton amongst Nobel Prize winners in physics

Hassabis and Jumper “developed an AI model to solve a 50-year-old problem: predicting the complex structures of proteins” from their amino acid sequences, the academy said. The rest goes to David Baker, a professor on the University of Washington who developed entirely recent forms of proteins, which the academy called “an almost impossible feat.”

Hassabis, 48, was a young chess prodigy who studied neuroscience before co-founding DeepMind in London in 2010. Google acquired the AI ​​lab in 2014 and invested resources into the unit. DeepMind was known for large advances in machine intelligence, corresponding to its AI model that defeated champions in the sport of Go.

The scientist has long pushed his researchers to pursue AI applications in chemistry and biology. His lab published necessary research on AlphaFold in 2020, demonstrating algorithms that would predict the structure of proteins – a breakthrough with major implications for drug discovery and understanding of disease.

This work has improved understanding of antibiotic resistance and enabled the creation of images of enzymes that may break down plastic.

In 2021, Alphabet founded Isomorphic Labs, a brand new subsidiary spun out of DeepMind focused on using AI for pharmaceuticals. Hassabis also runs this company. Last 12 months, the chief was tapped to guide all of Google's AI efforts. As Bloomberg News previously reported, he had previously told colleagues that he was aiming for a Nobel Prize.

Baker, a Seattle native, has developed computational methods for making proteins that didn't exist before, a lot of which have entirely recent functions. These may be used, for instance, as medicines, vaccines, nanomaterials and tiny sensors.

Names leaked

Last 12 months's chemistry prize went to Moungi G. Bawendi, Louis E. Brus and Alexei I. Ekimov for the invention and development of quantum dots, that are tiny nanoparticles that may be utilized in televisions and LED lights. The announcement was met with high drama because the honorees' names were leaked in an email that seemed to be sent by mistake that morning.

Annual prizes for achievements in physics, chemistry, medicine, literature and peace were established in the desire of Alfred Nobel, the Swedish inventor of dynamite, who died in 1896. In 1968 the Swedish central bank added a prize in economics.

The winners might be announced in Stockholm by October 14, apart from the Peace Prize, whose recipients might be chosen by the Norwegian Nobel Committee in Oslo.

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Originally published: October 9, 2024 at 7:53 am

If your jaw dropped while watching the most recent AI-generated video, your account balance was shielded from criminals by a fraud detection system, or your day was made a bit of easier because you might dictate a text message on the go, then that's the case with many scientists, mathematicians, and Thanks to engineers.

But two names stand out as fundamental contributions to the deep learning technology that makes these experiences possible: physicists at Princeton University John Hopfield and computer scientist from the University of Toronto Geoffrey Hinton.

The two researchers were awarded the Nobel Prize in Physics on Oct. 8, 2024 for her pioneering work in the sphere of artificial neural networks. Although artificial neural networks are modeled on biological neural networks, each researchers' work was based on statistical physics, which is why the prize was awarded in physics.

How a neuron calculates

Artificial neural networks owe their origins to the study of biological neurons in living brains. In 1943, neurophysiologist Warren McCulloch and logician Walter Pitts proposed one easy model of how a neuron works. In the McCulloch-Pitts model, a neuron is connected to its neighboring neurons and may receive signals from them. It can then mix these signals to send signals to other neurons.

But there's a twist: signals coming from different neighbors may be weighted otherwise. Imagine you're considering buying a brand new best-selling phone. You discuss with your folks and ask them for his or her recommendations. An easy strategy is to gather all recommendations from friends and choose to follow what the bulk say. For example, you ask three friends, Alice, Bob and Charlie, and so they say yes, yes and no. This leads you to the choice to purchase the phone as you will have two yes and one no votes.

However, it’s possible you’ll trust some friends more because they’ve in-depth knowledge of technological devices. So possibly you select to offer more weight to their recommendations. For example, if Charlie could be very knowledgeable, you would possibly count his no 3 times and now you select not to purchase the phone – two yeses and three nos. If you're unlucky enough to have a friend who you completely distrust relating to technical matters, you may even assign them a negative weight. So your “yes” is taken into account a “no” and your “no” is taken into account a “yes.”

Once you've decided for yourself whether the brand new phone is a superb alternative, other friends can ask you in your suggestion. Likewise, neurons in artificial and biological neural networks can collect signals from their neighbors and send a signal to other neurons. This ability results in a vital distinction: Is there a cycle within the network? For example, if I ask Alice, Bob and Charlie today and tomorrow Alice asks me for my suggestion, then there’s a cycle: from Alice to me and from me back to Alice.

When the connections between neurons should not have a cycle, computer scientists speak of a feedforward neural network. The neurons in a feedforward network may be arranged in layers. The first layer consists of the inputs. The second layer receives its signals from the primary layer and so forth. The last layer represents the outputs of the network.

However, when there’s a cycle within the network, computer scientists call it a recurrent neural network, and the arrangement of neurons may be more complicated than in feedforward neural networks.

Hopfield network

The initial inspiration for artificial neural networks got here from biology, but other fields soon began to shape their development. These included logic, mathematics and physics. Physicist John Hopfield used ideas from physics to review a particular thing Type of recurrent neural networknow called the Hopfield network. In particular, he examined their dynamics: What happens to the network over time?

Such dynamics are also vital when information spreads via social networks. Everyone is accustomed to the viral spread of memes and the formation of echo chambers in online social networks. These are all collective phenomena that ultimately arise from the easy exchange of data between people within the network.

Hopfield was a pioneer in with models from physicsparticularly those designed to review magnetism to know the dynamics of recurrent neural networks. He also showed that their dynamic can do it Give such neural networks a type of memory.

Boltzmann machines and backpropagation

In the Eighties, Geoffrey Hinton, computational neurobiologist Terrence Sejnowski, and others expanded Hopfield's ideas to create a brand new class of models called ” Boltzmann machinesnamed after the 19th century physicist Ludwig Boltzmann. As the name suggests, the design of those models relies on statistical physics developed by Boltzmann. Unlike Hopfield networks, which could store patterns and proper errors in patterns – like a spell checker does – Boltzmann machines could generate recent patterns, laying the inspiration for the trendy generative AI revolution.

Hinton was also a part of one other breakthrough that occurred within the Eighties: Backpropagation. If artificial neural networks are to tackle interesting tasks, one must in some way select the correct weights for the connections between artificial neurons. Backpropagation is a key algorithm that enables weights to be chosen based on the network's performance on a training data set. However, training artificial neural networks with many layers remained a challenge.

In the 2000s, Hinton and his staff took a clever approach used Boltzmann machines to coach multilayer networks by first pre-training the network layer by layer after which using one other fine-tuning algorithm on the pre-trained network to further adjust the weights. Multilayer networks were renamed deep networks and the deep learning revolution had begun.

AI pays back physics

The Nobel Prize in Physics shows how ideas from physics contributed to the rise of deep learning. Now deep learning has begun to make a difference in physics, enabling accurate and rapid simulations of systems starting from molecules and materials to Earth's entire climate.

In awarding the Nobel Prize in Physics to Hopfield and Hinton, the Prize Committee expressed its hope in humanity's potential to make use of these advances to advance human well-being and construct a sustainable world.

STOCKHOLM – Two scientists have won Nobel Prize in Physiology or Medicine on Monday for his or her discovery of microRNA, tiny pieces of genetic material that function on-and-off switches in cells and help control what the cells do and after they do it.

If scientists higher understand how they work and how one can manipulate them, it could in the future result in effective treatments for diseases like cancer.

The work is by Americans Victor Ambros and Berkeley natives Gary Ruvkun “proves to be of fundamental importance for the development and function of organisms,” said a panel that awarded the prize in Stockholm.

Ambros and Ruvkun were initially considering genes that control the timing of varied genetic developments, ensuring that cell types develop at the correct time.

Their discovery ultimately revealed “a new dimension of gene regulation that is essential for all complex life forms,” the panel said.

What is the Nobel Prize for?

RNA is best known for passing instructions for making proteins from DNA within the cell nucleus to tiny cell factories that truly construct the proteins. MicroRNA doesn't produce proteins, but it surely helps control what cells do, including turning on and off critical genes that make proteins.

Last 12 months's Nobel Prize in Medicine went to scientists who discovered how one can manipulate one in every of all these RNA, called messenger RNA or mRNA, which is now used to make vaccines against COVID-19.

Ambros and Ruvkun's revolutionary discovery was first made on worms; They wanted to search out out why some sorts of cells didn’t evolve in two mutant worm strains commonly utilized in science as a research model.

“Their groundbreaking discovery revealed an entirely new principle of gene regulation that proved essential for multicellular organisms, including humans,” says the citation, explaining the importance of their work.

This mechanism has been at work for a whole bunch of hundreds of thousands of years and has enabled the event of complex organisms, it said.

Ambros, currently a professor of natural sciences on the University of Massachusetts Medical School, conducted the research at Harvard University. Ruvkun's research was conducted at Massachusetts General Hospital and Harvard Medical School, where he’s a professor of genetics.

Why is microRNA vital?

Fletcher said there are two principal areas where microRNA might be helpful: in developing drugs to treat diseases and as possible indicators of disease by tracking microRNA levels within the body.

“If we take the example of cancer, we have a particular gene that is working overtime, it could be mutated and working at full speed,” Fletcher said. She said scientists may in the future find a way to make use of microRNA to stop such effects.

Eric Miska, a geneticist on the University of Cambridge, said Ambros and Ruvkun's discovery was an entire surprise and upended what scientists had long understood about how cells work.

Their discovery of microRNA shocked many scientists, Miska said, explaining that such small pieces of genetic material had never been seen before. It later emerged that the tiny RNA fragments – the human genome has no less than 800 – play a vital role in the event of our bodies.

Miska said that work is currently underway on the role of microRNA in infectious diseases comparable to hepatitis and that it may be helpful in treating neurological diseases.

Fletcher said essentially the most advanced studies up to now are examining how microRNA approaches could help treat skin cancer, but no drugs have been approved yet. She predicted this might occur in the approaching years, adding that almost all treatments currently goal cellular proteins.

“If we can intervene at the microRNA level, it opens up a whole new opportunity for us to develop drugs,” she said.

How did Gary Ruvkun and Victor Ambros react?

The call from the Nobel Prize panel is usually a surprise, but there are specific signs that recipients and their families notice.

“Well, when a phone rings at 4:30 in the morning. … That never happens here,” Ruvkun said.

“Natasha actually responded,” Ruvkun added, referring to his wife. “And she says, 'He has a Swedish accent.'”

It took just a little longer to wake Ambros.

“Someone called my son, who called my wife because my phone was downstairs,” he said.

Ruvkun immediately knew the impact the award would have on his life.

“Well, I kept repeating it in my head, this changes everything because the Nobel Prize is just mythical when it comes to how it changes the lives of the people chosen,” Ruvkun said. “The Nobel Prize is a recognition that generates about 100 times as much press and cheers as any other award. So it's not part of a continuum. It’s a quantum leap.”

Accepting his award in December might be the third time he has attended a Nobel ceremony in Stockholm, having watched his mentor Robert Horvitz in 2002 and his 2009 winning pal Jack Szostak.

“A journey is coming. It will be the third, possibly the best,” Ruvkun said.

Ambros said he didn’t expect the award because he believed the Nobel Committee had already highlighted RNA within the 2006 prize, which went to his friends Andrew Fire and Craig Mello.

“It represents a recognition of how wonderful and unexpected discoveries arise from a curiosity in basic research funded by taxpayer dollars.” “It's an extremely important message, probably the most important message, that this investment is really paying off,” he said.

Last 12 months, The Nobel Prize in Physiology or Medicine went to Hungarian-American Katalin Karikó and American Drew Weissman for discoveries that enabled the event of mRNA vaccines against COVID-19, which were crucial to slowing the pandemic.

The price is endowed with a money prize of 11 million Swedish crowns ($1 million) from a bequest from the prize's initiator, the Swedish inventor Alfred Nobel.

The Nobel announcements proceed with the Physics Prize on Tuesday, the Chemistry Prize on Wednesday and the Literature Prize on Thursday. The Nobel Peace Prize might be announced on Friday and the Nobel Prize in Economic Sciences on October 14th.

___

This story has been updated to make clear that microRNA helps regulate gene activity slightly than containing instructions for making proteins.

___

Corder reported from The Hague, Netherlands, Cheng reported from London. Associated Press journalists Steven Senne and Rodrique Ngowi in Newton, Massachusetts, and Adithi Ramakrishnan in New York.

Originally published: October 7, 2024 at 10:24 am

From 225 people received the Nobel Prize in Physicsonly five were women. This is a really small number and definitely lower than 50% – the proportion of girls within the human population.

Despite several studies uncover the barriers for ladies in science and the numerous efforts To increase its representation, physics continues to be a male-dominated field. Only one in five physicists is female, a number that has not modified since 2010.

Three of the five Nobel Prizes in Physics awarded to women were awarded within the last decade. As a physicistIt is beyond exciting to see three women joining the ranks of Nobel Prize winners in physics in only a couple of years.

Nobel Prize-winning work

The three female physicists to receive the Nobel Prize within the twenty first century are Donna Strickland, who won in 2018, Andrea Ghez, who won in 2020, and Anne L'Huillier, who won in 2023. All three made necessary contributions to science.

Stricklanda physicist from the University of Waterloo, won the prize For her work on lasers, she implemented a way called Chirp pulse amplification.

Ghezan astrophysicist from UCLA, received the Nobel Prize for her work Observing stars, especially those near the middle of the Milky Way.

L'Huillier, a physicist from Lund University, received the Nobel Prize in 2023also for his or her work with lasers.

What similarities are there of their lives?

Being a minority in a research area isn’t easy. To stick around long enough to have storied careers, because the three winners have, is a significant achievement. Since the award ceremony, the three winners have spoken in various interviews about their research trips and offered advice to the following generation of physicists. I noticed a couple of common threads.

A profession in science is a protracted journey. All three women emphasize the timeframe required from the primary steps of their research to recognition by the Nobel Committee. L'Huillier refers to this as a protracted journey.

Although winning a Nobel Prize comes with some glamor and celebrity, this profession will not be the correct one should you are on the lookout for a fast reward. It now takes average time 28 years between publishing a discovery and receiving a Nobel Prize in Physics.

You can't predict which fundamental scientific topic will result in a Nobel Prize – nor which one will ultimately have any impact. The neatest thing a physicist can do early of their profession is to explore different topics, try latest things, embrace discomfort, and find something they're keen about.

All three women speak about how often they got into trouble. Before she got the chirped-pulse amplification method working, Strickland had begun to wonder whether she would ever get a doctorate after experiencing so many dead ends. When Ghez first proposed the project that may result in her acclaimed work, she was rejected.

All three thought of quitting in some unspecified time in the future. So don't be discouraged should you get rejected or others say you may't do it.

“Carry on,” says L'Huillier. “You have to be persistent.”

Ghez recommends viewing experiments that don't work not as failures, but as opportunities.

Films and tv shows portray the scientist as a social outsider, a person who works alone within the laboratory. But that's not how it really works. All of those women work in teams.

“Science is a team sport. You need to know what you don’t know and seek help for what’s missing.” says Strickland.

The seek for help often results in collaborations with other research groups. As Ghez puts it“Science is a very social enterprise.”

And above all, the three medalists cited luck as an important prerequisite for achievement. The world is filled with equally committed and equally smart physicists who don't win the Nobel Prize.

Topics specifically for ladies

Strickland, Ghez and L'Huillier are sometimes asked about their experiences as women in science and their views on diversity and equity in physics. They all emphasize the importance of diversity.

The three award winners recognized how necessary female role models were of their lives. To imagine that a profession as a physicist is even possible You must see individuals who appear like you on the sector.

They also mention the importance of a support network, especially for ladies. When you're feeling discouraged, it might probably be helpful to have a gaggle of individuals you trust and cheering you on.

The three women also speak about theirs Experience with balancing work and personal life. It's not at all times easy.

After a postdoctoral fellowship, Strickland left the standard academic path to change into a technician so she may very well be near her husband and lift a family. L'Huillier quit her job and moved from France to Sweden, where she was unemployed for some time. Ghez waited years to have children. There is not any consistent trajectory. But taking a break from research can provide you with latest perspective and inspiration for next steps.

They also speak about how diversity enriches research itself. So a team open to different perspectives is more creative. It's also more fun to work in.

These women noted that the culture for ladies in science has improved over the course of their careers, and so they are optimistic concerning the future. If you calculate the share of Nobel Prizes in Physics awarded to women within the last decade alone, about one in ten Nobel Prize winners were women. To me, this implies that things could actually be recuperating.

And possibly The Nobel Committee is looking into itno less than partially, possible gender inequalities of their processes. For example, the dearth of nominations from women And the influence that stereotypes have could play a task of their evaluations. So I await this yr’s announcement with great anticipation.