David and Jen on their wedding day, and Jen undergoing treatment for her rare sarcoma.
It was 15 years ago this month, but I'll never forget those words. When my wife Jen and I asked her oncologist about our plans to start a family, he calmly replied, "Well, I wouldn't do so unless Dave is prepared to be a single father."
About 50 percent of all people with cancer have a rare type, like the one Jen was fighting.
Time stood still. The danger crystalized — we were in a battle for my beautiful bride's life, and the odds were not in our favor.
We felt every emotion expected. Anger, sadness, confusion, frustration, and especially fear. But we made a very intentional choice to take that fear, put it to the side, and do everything we could to live our lives together to the fullest.
We focused first on Jen's health and learned everything we could about MFH Sarcoma. I was with her every step of the way — for hundreds of medical appointments, six intense surgeries, and twenty different types of chemotherapy. During such a challenging time, our choice to reject fear allowed us to live our best lives. Our careers blossomed, we enjoyed several international vacations, and Jen inspired thousands of fellow patients through her blog and speeches.
When we researched treatment options we learned that Jen was not alone. About 50 percent of all people with cancer have a rare type, like the one Jen was fighting. However, rare cancers don't get the funding they desperately need so effective treatment options are hard to find. The lack of funding felt unfair — and urgent. We didn't worry about everything that can go wrong when starting a new venture. Instead, we jumped in head first and convinced a small group of friends and family to ride stationary bikes with us to raise money for rare cancer research.
Jen Goodman Linn, riding a stationary bike for Cycle for Survival.
(Courtesy David Linn)
From those humble beginnings, Cycle for Survival grew steadily. After starting from scratch, Jen and I ran Cycle for Survival on our own for two years. We quickly realized that if we wanted to help as many people as possible, we needed the best partners. In 2009, we agreed that Memorial Sloan Kettering Cancer Center would take over the ownership of Cycle for Survival and Equinox officially became the Founding Partner. Flash forward to today, and Cycle for Survival has raised more than $220 million! I'm proud that 100% of every donation, yes every penny, goes directly into life-saving rare cancer research within six months of the annual indoor cycling events, which now take place in 17 cities nationwide.
While Cycle for Survival's trajectory was heading straight up, Jen's health struggle was devastatingly swinging up and down. With her incredible spirit and tenacity, Jen would beat the cancer through chemo and surgery, but then it would frustratingly come back again and again. After going into remission six times, it returned with such a vengeance in 2011 that even the world's leading doctors were forced to say, "I'm sorry, there's nothing more we can do."
Those were the most difficult words I've ever heard, by far. I hope no other family has to hear these crushing words.
When Jen died soon after, I didn't know what would happen to me, to my life, and to Cycle for Survival. I do remember making two very important choices at the time. First, I chose to get out of bed and put one foot in front of the other. It wasn't easy. Tears, pain, and grief would hit at any hour of the day or night. I did have a great support network of family and friends who kept me moving forward. One friend in particular changed the route of her morning runs so that I would join her and start getting back to exercising.
My second key choice was to stay involved with Cycle for Survival. At times, it was an excruciatingly difficult decision because I felt the depth of my loss each and every time I stepped into one of the events. However, it was also rewarding and energizing because I could see firsthand how many people it was helping, even though it was too late for Jen.
I began to travel across the country with the Cycle for Survival staff. My hope was to spread the word about rare cancers; along the way I met a lot of wonderful people who shared their stories with me. What I soon realized is that each of us faces obstacles in our lives. For me, it was losing the person who I wanted to spend my life with. For others, it might be challenges with their kids or in their professional lives. The common theme is that we don't have control over the fact that we have to face these challenges. But the biggest lesson I've learned is that we very much do have a choice in how we react.
I made the choice to do everything I can to help rare cancer patients and their families and it has been transformative and healing for me. The small group who rode in the first Cycle for Survival event has grown into a powerful movement of nearly 40,000 riders making a real difference. If Jen were diagnosed today, there are new treatments available– including genomic sequencing, targeted therapies, and immunotherapies – that could help her. Those weren't even options a short time ago. That's the result of funding research.
A recent Cycle for Survival event shows the passion and power of the community.
(Courtesy David Linn)
I also want to share one more choice I made. Remember that friend who changed the route of her morning runs so I could start exercising after Jen died? Well, over the years friendship grew into love, and we're now building a home together and can't wait to see what the future holds for us.
So with all that in mind I ask – when you face those inevitable challenges in your life, how will you choose to react? Remember that even in the midst of hopelessness, you can find choices. Those will be the decisions that define and guide you.
Artificial Intelligence is already helping to grow some of the food we eat.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
