Lifeboat Foundation

The Tasmanian devil (Sarcophilus harrisii), the largest marsupial carnivore, is endangered due to a transmissible facial cancer spread by direct transfer of living cancer cells through biting. Here we describe the sequencing, assembly, and annotation of the Tasmanian devil genome and whole-genome sequences for two geographically distant subclones of the cancer. Genomic analysis suggests that the cancer first arose from a female Tasmanian devil and that the clone has subsequently genetically diverged during its spread across Tasmania. The devil cancer genome contains more than 17,000 somatic base substitution mutations and bears the imprint of a distinct mutational process. Genotyping of somatic mutations in 104 geographically and temporally distributed Tasmanian devil tumors reveals the pattern of evolution and spread of this parasitic clonal lineage, with evidence of a selective sweep in one geographical area and persistence of parallel lineages in other populations.

Circa 2013

In a feat of “molecular time travel,” the researchers resurrected and analyzed the functions of the ancestors of genes that play key roles in modern human reproduction, development, immunity and cancer. By re-creating the same DNA changes that occurred during those genes’ ancient history, the team showed that two mutations set the stage for hormones like estrogen, testosterone and cortisol to take on their crucial present-day roles.

“Changes in just two letters of the genetic code in our deep evolutionary past caused a massive shift in the function of one protein and set in motion the evolution of our present-day hormonal and reproductive systems,” said Joe Thornton, PhD, professor of human genetics and ecology & evolution at the University of Chicago, who led the study.

“If those two mutations had not happened, our bodies today would have to use different mechanisms to regulate pregnancy, libido, the response to stress, kidney function, inflammation, and the development of male and female characteristics at puberty,” Thornton said.

Circa 2011 essentially cancer could help with evolution as it can challenge the immune system to be more strong. Essentially a symbiotic relationship to evolve with it and grow stronger with it then like it can be used as a good thing to make sure that evolution has stronger genetic code.

Evolutionary theories are critical for understanding cancer development at the level of species as well as at the level of cells and tissues, and for developing effective therapies. Animals have evolved potent tumor suppressive mechanisms to prevent cancer development. These mechanisms were initially necessary for the evolution of multi-cellular organisms, and became even more important as animals evolved large bodies and long lives. Indeed, the development and architecture of our tissues were evolutionarily constrained by the need to limit cancer. Cancer development within an individual is also an evolutionary process, which in many respects mirrors species evolution. Species evolve by mutation and selection acting on individuals in a population; tumors evolve by mutation and selection acting on cells in a tissue. The processes of mutation and selection are integral to the evolution of cancer at every step of multistage carcinogenesis, from tumor genesis to metastasis. Factors associated with cancer development, such as aging and carcinogens, have been shown to promote cancer evolution by impacting both mutation and selection processes. While there are therapies that can decimate a cancer cell population, unfortunately, cancers can also evolve resistance to these therapies, leading to the resurgence of treatment-refractory disease. Understanding cancer from an evolutionary perspective can allow us to appreciate better why cancers predominantly occur in the elderly, and why other conditions, from radiation exposure to smoking, are associated with increased cancers. Importantly, the application of evolutionary theory to cancer should engender new treatment strategies that could better control this dreaded disease.

We expect that the public generally views evolutionary biology as a science about the past, with stodgy old professors examining dusty fossils in poorly lit museum basements. Evolution must certainly be a field well-separated from modern medicine and biomedical research, right? If the public makes a connection between evolution and medicine, it is typically in the example of bacteria acquiring antibiotic resistance. But what does evolution have to do with afflictions like heart disease, obesity, and cancer? As it turns out, these diseases are intricately tied to our evolutionary histories, and understanding evolution is essential for preventing, managing and treating these diseases (1, 2). This review will focus on cancer: how evolutionary theories can be used to understand cancer development at the level of species as well as at the level of cells and tissues. We will also discuss the implications and benefits of an evolutionary perspective towards cancer prevention and therapies.

Continue reading “How cancer shapes evolution, and how evolution shapes cancer” »

Circa 2017

Chess isn’t an easy game, by human standards. But for an artificial intelligence powered by a formidable, almost alien mindset, the trivial diversion can be mastered in a few spare hours.

In a new paper, Google researchers detail how their latest AI evolution, AlphaZero, developed “superhuman performance” in chess, taking just four hours to learn the rules before obliterating the world champion chess program, Stockfish.

Continue reading “In Just 4 Hours, Google’s AI Mastered All The Chess Knowledge in History” »

Circa 2017

Thousands of years of human knowledge has been learned and surpassed by the world’s smartest computer in just 40 days, a breakthrough hailed as one of the greatest advances ever in artificial intelligence.

Google DeepMind amazed the world last year when its AI programme AlphaGo beat world champion Lee Sedol at Go, an ancient and complex game of strategy and intuition which many believed could never be cracked by a machine.

Continue reading “AlphaGo Zero: Google DeepMind supercomputer learns 3,000 years of human knowledge in 40 days” »

Proteins function by folding into myriad, precise 3D structures. Image: Mohammed AlQuraishi.

We need to slash emissions from agriculture while at the same time feeding billions more people. It’s a difficult task, but there’s a path forward to make it work.

[Photo: Jevtic/iStock]

Circa 2014

According to one recent study, there’s at least 5 trillion pieces of plastic in the ocean. That’s more than 250 tons. So what to do with mountains of plastic waste with nowhere to go? Katharina Unger thinks we should eat it.

The Austrian designer partnered with Julia Kaisinger and Utrecht University to develop a system that cultivates edible plastic-digesting fungi. That’s right, you can eat mushrooms that eat plastic. In 2012, researchers at Yale University discovered a variety of mushroom (Pestalotiopsis microspora) that is capable of breaking down polyurethane. It kicked off a craze of research exploring how various forms of fungi can degrade plastic without retaining the toxicity of the material. The findings got Unger thinking: What if we could turn an environmental problem (waste) into an environmental solution (food)?

Continue reading “A Mini Farm That Produces Food From Plastic-Eating Mushrooms” »

As others have pointed out, voxel-based games have been around for a long time; a recent example is the whimsical “3D Dot Game Hero” for PS3, in which they use the low-res nature of the voxel world as a fun design element.

Voxel-based approaches have huge advantages (“infinite” detail, background details that are deformable at the pixel level, simpler simulation of particle-based phenomena like flowing water, etc.) but they’ll only win once computing power reaches an important crossover point. That point is where rendering an organic world a voxel at a time looks better than rendering zillions of polygons to approximate an organic world. Furthermore, much of the effort that’s gone into visually simulating real-world phenomena (read the last 30 years of Siggraph conference proceedings) will mostly have to be reapplied to voxel rendering. Simply put: lighting, caustics, organic elements like human faces and hair, etc. will have to be “figured out all over again” for the new era of voxel engines. It will therefore likely take a while for voxel approaches to produce results that look as good, even once the crossover point of level of detail is reached.

I don’t mean to take anything away from the hard and impressive coding work this team has done, but if they had more academic background, they’d know that much of what they’ve “pioneered” has been studied in tremendous detail for two decades. Hanan Samet’s treatise on the subject tells you absolutely everything you need to know, and more: (http://www.amazon.com/Foundations-Multidimensional-Structure…sr=8-1) and even goes into detail about the application of these spatial data structures to other areas like machine learning. Ultimately, Samet’s book is all about the “curse of dimensionality” and how (and how much) data structures can help address it.