Tyler O'Neal, Staff Editor ACADEMIA

University of Virginia leads fibrosis initiative

Fibrosis is often associated with many of the fatal diseases that pervade our globe, riddling organs with stiff tissue that diminishes their flexibility and leads to their failure. The World Health Organization estimates that fibrosis is directly implicated in, or responsible for, as many as 40 percent of all deaths across the globe. The University of Virginia School of Engineering, in conjunction with the UVA School of Medicine, launched a dedicated Fibrosis Initiative to address this increasingly prevalent threat, drawing from university-wide expertise in extracellular, computational, and quantitative biology.

"In many ways, fibrosis is as significant a problem as cancer, but we have no reliable approaches for early detection or effective treatment," said Thomas Barker, professor of biomedical engineering at UVA Engineering and director at the Fibrosis Initiative.

"Through this initiative, we can leverage the collective expertise of UVA researchers that are conducting fibrosis related studies to establish those groundbreaking approaches." {module In-article}

UVA is taking a significant step forward in shaping those groundbreaking approaches, hosting a first-of-its-kind, dedicated forum on fibroblasts from June 23-35. The American Society for Matrix Biology will co-host the event.

To date, the biomedical field has not firmly established a definition for fibroblasts, often viewing them as a single, catch-all cell type. However, this international meeting aims to help shape this definition--and shed light on the impact fibroblasts have on the formation of scar tissue, proper repair of wounds, and modeling of complex systems.

The meeting, sponsored by Bristol Meyers Squibb, the National Institutes of Health, UVA Fibrosis Initiative and the University of Nebraska Medical Center, will convene more than 100 world leaders and investigators who have contributed to this area of work. Sessions, led by moderators from across the country, will focus on a variety of topics relating to fibroblasts, including their origins and lineages; pathology; imaging; and role in shaping signaling networks.

Over the course of three days, speakers will also share insights from both published and unpublished cutting-edge studies. Jeffrey Holmes, professor of biomedical engineering and medicine and director of the Center for Engineering in Medicine at the University of Virginia, as well as Boris Hinz, distinguished professor of tissue repair and regeneration at the University of Toronto, will deliver keynote addresses.

As the Fibrosis Initiative continues to promote national and international collaborations around fibroblasts, it will also focus on supporting related research at UVA, launching an initial cohort of multi-investigator "seed grants" in the interest of securing a National Institutes of Health "Center of Excellence" designation. Post-doctoral fellows will have the opportunity to shape the next wave of research on fibroblasts, and fibrosis more generally, with these funding supports.

Current projects include:

Stemming the Tide of Lung Transplant Rejections: While lung transplants have transformed the prospects of patients with end-stage lung failure, the overall five- to 10-year survival rate still lags significantly behind patients with other types of organ transplants such as livers and kidneys; the result of a condition known as chronic lung allograft dysfunction (CLAD). With the support of Barker and Dr. Alexander Krupnick, a thoracic and cardiovascular surgeon at UVA Health System, initial research has shown how the loss of expression of Thy-1, an important glycoprotein that defines unique subtypes of fibroblasts, may increase the likelihood of CLAD during lung transplants.

Positioning Cancer-Associated Fibroblasts to Combat Tumor Growth: While cancer-associated fibroblasts (CAFs), which are prominent in the tumor stroma, can inhibit the progress of tumors, others can also curtail the impact of critical therapies and drugs. The principal investigator, Andrew Dudley, associate professor of microbiology, immunology and cancer biology at the UVA School of Medicine, is leveraging a seed grant to develop cutting-edge tools and approaches that identify the different molecular compositions of CAFs; explore how blocking damaging CAFs could delay the progress of tumors; and inform strategies that improve how we treat cancer.

Biomedical engineers, scientists, and clinicians across UVA will continue to come together to lead similar projects, all of which strive to identify the causes of pathological cell behaviors, explore how fibrosis affects the well-being of specific organ systems, and pinpoint new techniques to detect, monitor, and treat this condition.

Over the next several years, the Fibrosis Initiative also aims to launch additional, dedicated meetings and research opportunities (both internally and nationally) around fibroblasts, maintaining the momentum from this convening and related projects.

"Through the upcoming ASMB meeting and our own groundbreaking seed grant projects, we have the opportunity to lead an increasingly imperative global conversation on fibroblasts and fibrosis more generally," said Barker.

"If we are successful, we can make significant strides in the worldwide fight to diagnose and treat a growing cause of death that affects all communities."

ELSI researchers use biological evolution to inspire machine learning

Tyler O'Neal, Staff Editor ACADEMIA

As Charles Darwin wrote in at the end of his seminal 1859 book On the Origin of the Species, "whilst this planet has gone cycling on according to the fixed law of gravity, from so simple a beginning endless forms most beautiful and most wonderful have been, and are being, evolved." Scientists have since long believed that the diversity and range of forms of life on Earth provide evidence that biological evolution spontaneously innovates in an open-ended way, constantly inventing new things. However, attempts to construct artificial simulations of evolutionary systems tend to run into limits in the complexity and novelty which they can produce. This is sometimes referred to as "the problem of open-endedness." Because of this difficulty, to date scientists can't easily make artificial systems capable of exhibiting the richness and diversity of biological systems.

In a new study published in the journal Artificial Life, a research team led by Nicholas Guttenberg and Nathaniel Virgo of the Earth-Life Science Institute (ELSI) at Tokyo Institute of Technology, Japan, and Alexandra Penn of The Centre for Evaluation of Complexity Across the Nexus (CECAN), University of Surrey UK (CRESS), examine the connection between biological evolutionary open-endedness and recent studies in machine learning, hoping that by connecting ideas from artificial life and machine learning, it will become possible to combine neural networks with the motivations and ideas of artificial life to create new forms of open-endedness. CAPTION This image shows a close-up of one of the generated results. Here, the bird-like patterns result from the 'eye' of the critic -- a network known as VGG19 -- used to compare the outputs of the competing networks, which is itself a model trained on classifying different natural images. CREDIT Nicholas Guttenberg{module In-article}

One source of open-endedness in evolving biological systems is an "arms race" for survival. For example, faster foxes may evolve to catch faster rabbits, which in turn may evolve to become even faster to get away from the faster foxes. This idea is mirrored in recent developments involving placing networks in competition with each other to produce things such as realistic images using generative adversarial networks (GANs), and to discover strategies in games such as Go, which can now easily beat top human players. In evolution, factors such as mutation can limit the extent of an arms race. However, as neural networks have been scaled up, no such limitation seems to exist and the network can continue to improve as additional data is fed to their algorithms.

Guttenberg had been studying evolutionary open-endedness since graduate school, but it was only in the last few years that his focus shifted to artificial intelligence and neural networks. Around that time, methods such as GANs were invented, which struck him as very similar to the open-ended co-evolutionary systems he had previously worked on. Suddenly, he saw an opportunity to tear down a barrier between the communities to help make progress on something which had for him been a persistently important and interesting problem.

The researchers showed that while they can use scaling analyses to demonstrate open-endedness in evolutionary and cognitive contexts, there is a difference between making something which, for example, becomes infinitely good at making cat pictures and something which, having tired of making cat pictures, decides to go on to making music instead. In artificial evolutionary systems, these sorts of major qualitative leaps have to be anticipated by the programmer - they'd need to make an artificial world in which music is possible for the "organisms" to decide to be musicians. In systems such as neural networks, concepts such as abstraction are more easily captured, and so one can start to imagine ways in which populations of interacting agents could create new problems to be solved among themselves. 

{media id=231,layout=solo} {module In-article}

This work raises some deep and interesting questions. For example, if the drive for qualitatively different novelty in a computational system arises internally from abstraction, what determines the "meaning" of the novelty artificial systems generate? Machine learning has been shown to sometimes lead to the creation of artificial languages by interacting computational agents, but these languages are still grounded in the task the agents are cooperating to solve. If the agents really do rely on the interactions within the system to drive open-endedness far from whatever was provided as starting material, would it even be possible to recognize or interpret the things that come out, or would one have to be an organism living in such a system in order to understand its richness?

Ultimately, this study suggests it may be possible to make artificial systems that autonomously and continuously invent or discover new things, which would constitute a significant advance in artificial intelligence, and may help in understanding the evolution and origin of life.

UVA scientists use machine learning to improve gut disease diagnosis

Tyler O'Neal, Staff Editor ACADEMIA

A study published in the open access journal JAMA Open Network June 14 by scientists at the University of Virginia schools of Engineering and Medicine and the Data Science Institute says machine learning algorithms applied to biopsy images can shorten the time for diagnosing and treating a gut disease that often causes permanent physical and cognitive damage in children from impoverished areas.

In places where sanitation, potable water and food are scarce, there are high rates of children suffering from environmental enteric dysfunction, a disease that limits the gut's ability to absorb essential nutrients and can lead to stunted growth, impaired brain development and even death.

The disease affects 20 percent of children under the age of 5 in low- and middle-income countries, such as Bangladesh, Zambia and Pakistan, but it also affects some children in rural Virginia. {module In-article} 

For Dr. Sana Syed, an assistant professor of pediatrics in the UVA School of Medicine, this project is an example of why she got into medicine. "You're talking about a disease that affects hundreds of thousands of children, and that is entirely preventable," she said.

Syed is working with Donald Brown, founding director of the UVA Data Science Institute and W.S. Calcott Professor in the Department of Engineering Systems and Environment, to incorporate machine learning into the diagnostic process for health officials combating this disease. Syed and Brown are using a deep learning approach called "convolutional neural networks" to train computers to read thousands of images of biopsies. Pathologists can then learn from the algorithms how to more effectively screen patients based on where the neural network is looking for differences and where it is focusing its analysis to get results.

"These are the same types of algorithms Google is using in facial recognition, but we're using them to aid in the diagnosis of disease through biopsy images," said Brown.

The machine learning algorithm can provide insights that have evaded human eyes, validate pathologists' diagnoses and shorten the time between imaging and diagnosis, and from a technical engineering perspective, might be able to offer a look into data science's "black boxes" by giving clues into the thinking mechanism of the machine.

But for Syed, it is still about saving lives.

"There is so much poverty and such an unfair set of consequences," she said. "If we can use these cutting-edge technologies and ways of looking at data through data science, we can get answers faster and help these children sooner."