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Gauging the Resilience of Complex Networks

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TROY, N.Y. — Whether a transformer catches fire in a power grid, a species disappears from an ecosystem, or water floods a city street, many systems can absorb a certain amount of disruption. But how badly does a single failure weaken the network? And how much damage can it take before it tips into collapse? Network scientist Jianxi Gao is building tools that can answer those questions, regardless of the nature of the system.

TROY, N.Y. — Whether a transformer catches fire in a power grid, a species disappears from an ecosystem, or water floods a city street, many systems can absorb a certain amount of disruption. But how badly does a single failure weaken the network? And how much damage can it take before it tips into collapse? Network scientist Jianxi Gao is building tools that can answer those questions, regardless of the nature of the system.

“After a certain point, damage to a system is so great that it causes catastrophic failure. But the events leading to a loss of resilience in a system are rarely predictable and often irreversible. That makes it hard to prevent a collapse,” said Dr. Gao, an assistant professor of computer science at Rensselaer Polytechnic Institute, who was awarded a prestigious National Science Foundation CAREER award to tackle the problem. “The mathematical tools we are building will make it possible to evaluate the resilience of any system. And with that, we can predict and prevent failure.”

Imagine the effects of climate change on an ecosystem, Dr. Gao said. A species that can’t adapt will dwindle to extinction, perhaps driving a cascade of other species, which eat the first, to the brink of extinction also. As the climate changes, and more species are stressed, Dr. Gao wants the ability to predict the impact of those dwindling populations on the rest of the ecosystem. 

Predicting resilience starts by mapping the system as a network, a graph in which the players (an animal, neuron, power station) are connected by the relationships between them, and how that relationship affects each of the players and the network overall. In one visualization of a network, each of the players is a dot, a node, connected to other players by links that represent the relationship between them — think who eats whom in a forest and how that impacts the overall population of each species, or how information moving across a social media site influences opinions. Over time, the system changes, with some nodes appearing or disappearing, links growing stronger or weaker or changing relationship to one another as the system as a whole responds to that change.

Mathematically, a changing network can be described by a series of coupled nonlinear equations. And while equations have been developed to map networks in many fields, predicting the resiliency of complex networks or systems with missing information overwhelms the existing ability of even the most powerful supercomputers.

“We’re very limited in what we can do with the existing methods. Even if the network is not very large, we may be able to use the computer to solve the coupled equations, but we cannot simulate many different failure scenarios,” Dr. Gao said.

Dr. Gao debuted a preliminary solution to the problem in a 2016 paper published in Nature. In that paper, he and his colleagues declared that existing analytical tools are insufficient because they were designed for smaller models with few interacting components, as opposed to the vast networks we want to understand. The authors proposed a new set of tools, designed for complex networks, able to first identify the natural state and control parameters of the network, and then collapse the behavior of different networks into a single, solvable, universal function.

The tools presented in the Nature paper worked with strict assumptions on a network where all information is known — all nodes, all links, and the interactions between those nodes and links. In the new work, Dr. Gao wants to extend the single universal equation to networks where some of the information is missing. The tools he is developing will estimate missing information — missing nodes and links, and the relationships between them — based on what is already known. The approach reduces accuracy somewhat, but enables a far greater reward than what is lost, Dr. Gao said.

“For a network of millions or even billions of nodes, I will be able to use just one equation to estimate the macroscopic behavior of the network. Of course, I will lose some information, some accuracy, but I capture the most important dynamics or properties of the whole system,” Dr. Gao said. “Right now, people cannot do that. They cannot test the system, find where it gives way, and better still, improve it so that it will not fail.”

“The ability to analyze and predict weaknesses across a variety of network types gives us a vast amount of power to safeguard vulnerable networks and ecosystems before they fail,” said Curt Breneman, dean of the Rensselaer School of Science. “This is the kind of work that changes the game, and this CAREER award is a recognition of that potential. We congratulate Jianxi and expect great things from his research.”

CAREER: Network Resilience: Theories, Algorithms, and Applications” is funded with a $576,000 grant from the National Science Foundation.

About Rensselaer Polytechnic Institute

Founded in 1824, Rensselaer Polytechnic Institute is America’s first technological research university. Rensselaer encompasses five schools, 34 research centers, more than 145 academic programs including 25 new programs, and a dynamic community made up of more than 7,600 students and over 104,000 living alumni. Rensselaer faculty and alumni include more than 145 National Academy members, six members of the National Inventors Hall of Fame, six National Medal of Technology winners, five National Medal of Science winners, and a Nobel Prize winner in Physics. With nearly 200 years of experience advancing scientific and technological knowledge, Rensselaer remains focused on addressing global challenges with a spirit of ingenuity and collaboration. To learn more, please visit www.rpi.edu.

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Source: bioengineer.org

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Are Researchers One Step Closer to Developing the Theory of Impulse Circuits?

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Computers play an important role in many aspects of life today. Digital computers are the most widely used, while quantum computers are well known. However, the least known computers are the so-called Stochastic Pulse Computers. Their work is based on highly parallel logical operations between trains of electrical pulses, where the pulses occur at random times, as in neurons, the nerve cells in the brains of humans and mammals.

Computers play an important role in many aspects of life today. Digital computers are the most widely used, while quantum computers are well known. However, the least known computers are the so-called Stochastic Pulse Computers. Their work is based on highly parallel logical operations between trains of electrical pulses, where the pulses occur at random times, as in neurons, the nerve cells in the brains of humans and mammals.

The main motivation for the growing interest in research on RPC computers over the past decade is the hope that they could solve faster and with less energy consumption tasks that are normally easy for living beings, but difficult for digital computers, such as instantaneous responses to stimuli, pattern recognition, robustness to errors and damage in the system, learning, and autonomy.

In a recent study published in Scientific Reports, researchers from the Croatian Centre of Excellence for Advanced Materials and Sensors, Dr Mario Stipčević of the Ruđer Bošković Institute (RBI) and Mateja Batelić, a student at the Faculty of Science at the University of Zagreb (FS), Croatia, describe new or improved versions of RPC circuits that use quantum randomness for the first time, but also go a significant step further and lay the first foundation for RPC circuit theory.

Namely, while circuits for processing information in a digital computer can be assembled from logic circuits as building blocks based on the well-known Boolean theory, a similar theory for RPC circuits does not yet exist. Therefore, the synthesis of circuits for an RPC is limited to trial and error through experimentation or simulation.

‘’The central part of our paper is the formulation and proof of the so-called entropy budget theorem, which can be used to easily verify whether a given mathematical (or logical) operation can be performed or “calculated” by any physical circuit, and if so, how much excess entropy must be available to a circuit in order to perform the given operation.

In this paper, we demonstrate the theorem using several examples of mathematical operations. Perhaps the most interesting proof is the existence of a deterministic half-sum circuit (a + b) / 2. However, this circuit is not yet known, and finding it is a challenge for further research,” says Mario Stipčević, head of the Laboratory of Photonics and Quantum Optics at the Ruđer Bošković Institute.

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PSMA PET Validates EAU Classification System to Determine Risk of Prostate Cancer Recurrence

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Reston, VA (January 20, 2022)—New research has confirmed the accuracy of the novel European Association of Urology (EAU) risk classification system that groups prostate cancer patients based on their risk of recurrence. Prostate-specific membrane antigen (PSMA) PET imaging of men with prostate cancer validated the EAU groupings and provided insights that could further refine risk assessment for patients. This study was published in the January issue of The Journal of Nuclear Medicine.

Credit: Justin Ferdinandus, Wolfgang P. Fendler, Andrea Farolfi, et al.

Reston, VA (January 20, 2022)—New research has confirmed the accuracy of the novel European Association of Urology (EAU) risk classification system that groups prostate cancer patients based on their risk of recurrence. Prostate-specific membrane antigen (PSMA) PET imaging of men with prostate cancer validated the EAU groupings and provided insights that could further refine risk assessment for patients. This study was published in the January issue of The Journal of Nuclear Medicine.

The diagnostic workup of prostate cancer has changed rapidly over the past few years. Recently, the EAU introduced a clinical system separating patients with rising PSA values after first-line therapy (prostate surgery or radiation) into groups of those with high risk and those with low risk for development of metastases. Shortly after this, the U.S. Food and Drug Administration approved 68Ga-PSMA-11 as the first PET drug to target the PSMA for men with prostate cancer.

“Given the growing availability of PSMA-directed PET imaging, our study sought to assess disease in patients based on the EAU classifications while using PSMA PET to identify subgroups of patients, such as those with undetectable, locoregional or distant metastatic disease,” said Justin Ferdinandus, MD, nuclear medicine physician at University Hospital in Essen, Germany.

The multicenter, international study analyzed PSMA PET scans of nearly 2,000 patients with prostate cancer and rising PSA levels. Patterns of disease spread on PSMA PET imaging were used to classify prostate cancer patients into both low- and high-risk groups. High-risk groups were found to have higher rates of metastatic disease on PSMA PET compared to low-risk groups. However, PSMA PET also found metastatic disease in low-risk and no disease in high-risk patients.

“Our study underscores the utility of the EAU risk groups to determine risk of metastasis in biochemically recurrent prostate cancer. But not every high-risk patient has metastases and not every low-risk patient has locoregional or no disease,” said Wolfgang Fendler, MD, nuclear medicine physician at University Hospital in Essen.

He continued, “The ultimate aim of imaging is to provide the right treatment for each patient. As evidenced in this research, the accuracy of PSMA PET is essential to improve stratification and potentially outcomes both in low-risk and high-risk settings.” 

The authors of “PSMA PET validates higher rates of metastatic disease for European Association of Urology Biochemical Recurrence Risk Groups: an international multicenter study” include Justin Ferdinandus, Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany; Wolfgang P. Fendler and Ken Hermann, Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany, and Ahmanson Translational Imaging Division, Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California; Andrea Farolfi, Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany, and Division of Nuclear Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy; Samuel Washington, Department of Urology, University of California San Francisco, San Francisco, California, and Department of Epidemiology and Statistics, University of California San Francisco, San Francisco, California; Osama Mohamad, Department of Radiation Oncology, University of California San Francisco, San Francisco, California; Miguel H. Pampaloni and Thomas A. Hope, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California; Peter J.H. Scott, Melissa Rodnick, Benjamin L. Viglianti and Morand Piert, Department of Radiology, University of Michigan, Ann Arbor, Michigan; Matthias Eiber, Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University Munich, Munich, Germany; and Johannes Czernin, Wesley R. Armstrong and Jeremie Calais, Ahmanson Translational Imaging Division, Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California.

Visit JNM’s new website for the latest research, and follow our new Twitter and Facebook pages @JournalofNucMed.

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Please visit the SNMMI Media Center for more information about molecular imaging and precision imaging. To schedule an interview with the researchers, please contact Rebecca Maxey at (703) 652-6772 or [email protected].
 

About JNM and the Society of Nuclear Medicine and Molecular Imaging
The Journal of Nuclear Medicine (JNM) is the world’s leading nuclear medicine, molecular imaging and theranostics journal, accessed more than 13 million times each year by practitioners around the globe, providing them with the information they need to advance this rapidly expanding field. Current and past issues of The Journal of Nuclear Medicine can be found online at http://jnm.snmjournals.org.

JNM is published by the Society of Nuclear Medicine and Molecular Imaging (SNMMI), an international scientific and medical organization dedicated to advancing nuclear medicine and molecular imaging—precision medicine that allows diagnosis and treatment to be tailored to individual patients in order to achieve the best possible outcomes. For more information, visit www.snmmi.org.

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UCLA Researchers Develop Novel Microscopic Picoshell Particles

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Production of high-energy fats by microalgae may provide a sustainable, renewable energy source that can help tackle climate change. However, microalgae engineered to produce lipids rapidly usually grow slowly themselves, making it difficult to increase overall yields. 

Production of high-energy fats by microalgae may provide a sustainable, renewable energy source that can help tackle climate change. However, microalgae engineered to produce lipids rapidly usually grow slowly themselves, making it difficult to increase overall yields. 

UCLA bioengineers have created a new type of petri dish in the form of microscopic, permeable particles that can dramatically speed up research and development (R&D) timelines of biological products, such as fatty acids for biofuels. Dubbed PicoShells, the picoliter (trillionth of a liter), porous, hydrogel particles can enable more than one million individual cells to be compartmentalized, cultured in production-relevant environments, and selected based on growth and biomass accumulation traits using standard cell-processing equipment. 

Proceedings of the National Academy of Sciences recently published a study detailing how PicoShells work and their potential applications.

PicoShells consist of a hollow inner cavity where cells are encapsulated and a porous outer shell that allows for continuous solution exchange with the external environment so that nutrients, cell-communication molecules and cytotoxic cellular byproducts can transport freely in and out of the inner cavity. The shell also keeps the small groups of growing cells penned in, allowing researchers to study and compare their behaviors — what they do, how fast they grow, what they produce — to those of other groups inside various PicoShells. 

This new class of lab tool allows researchers to grow living, single-cell microorganisms — including algae, fungi and bacteria — under the same industrial-production conditions, such as in a bioreactor filled with wastewater or an outdoor cultivation pond. 

“PicoShells are like very tiny mesh balloons. The growing cells inside them are effectively fenced in but not sealed off,” said study leader Dino Di Carlo, UCLA’s Armond and Elena Hairapetian Professor in Engineering and Medicine at the UCLA Samueli School of Engineering. “With this new tool, we can now study the individual behaviors of millions of living cells in the relevant environment. This could shorten R&D-to-commercial production timelines for bioproducts from a few years to a few months. PicoShells could also be a valuable tool for fundamental biology studies.” 

PicoShells’ permeability can bring the lab to the industrial environment, allowing testing at a sectioned-off area of a working facility. Growth can occur more quickly and cell strains that perform well can be identified and selected for further screening. 

According to the researchers, another advantage of this new tool is that the analysis of millions of PicoShells is automated since they are also compatible with standard lab equipment used for high-volume cell processing.

Massive groups of cells, up to 10 million in one day, can be sorted and organized by certain characteristics. Continuous analysis could result in ideal sets of cells — ones that are already performing well in the environment with suitable temperature, nutrient composition and other properties that could be used in mass production — in just a few days rather than the several months it would take using current technologies.

The shells can be engineered to burst when the cells inside have divided and grown beyond their peak volume. Those free cells are still viable and can be recaptured for continued research or further selection. The researchers can also create shells with chemical groups that break down when exposed to biocompatible reagent, enabling a multifaceted approach to release selected cells.

“If we want to zero in on algae that are the best at producing biofuels, we can use PicoShells to organize, grow and process millions of single algal cells,” said lead author Mark van Zee, a bioengineering graduate student at UCLA Samueli. “And we can do that in machines that sort them using fluorescent tags that light up to signify fuel levels.”

Currently, cultivating and comparing such microorganisms are done mostly using traditional lab tools, such as microwell plates — cartons that hold several dozen small test tube-like volumes. However, these methods are slow and it is difficult to quantify their effectiveness because it can take weeks or months to grow large colonies for study. Other approaches, such as water-in-oil droplet emulsions, can be used to analyze cells in smaller volumes, but surrounding oils prevent the free exchange of medium into the water drops. Even cells or microorganisms that perform well in lab conditions may not do as well once they are placed in industrial environments, such as bioreactors or outdoor cultivation farms. As a result, cell strains that are developed in the lab often do not exhibit the same beneficial characteristic behavior when transferred to industrial production. 

Microwell plates also are limited in the number of experiments that can be performed, resulting in a great deal of trial and error in finding cell strains that work sufficiently well for mass production.

The researchers demonstrated the new tool by growing colonies of algae and yeast, comparing their growth and viability against other colonies grown in water-in-oil emulsions. For the algae, the team found that PicoShell colonies accumulated biomass rapidly while algae did not grow at all in water-in-oil emulsions. Similar results were found in their yeast experiments. By selecting the top growing algae in PicoShells, the researchers could increase the production of chlorophyll biomass by 8% after only a single cycle. 

The authors said PicoShells could offer a faster alternative to develop new algae and yeast strains, leading to improved biofuels, plastics, carbon-capture materials and even food products and alcoholic beverages. Further refinements to the technology, such as coating the shells with antibodies, could also lead to developing new types of protein-based medicines.

Di Carlo, van Zee and study co-author Joseph de Rutte Ph.D. ’20, a former member of Di Carlo’s research group, are named inventors on a patent application filed by the UCLA Technology Development Group.

The other UCLA authors on the paper are Rose Rumyan, Cayden Williamson, Trevor Burnes, Andrew Sonico Eugenio, Sara Badih, Dong-Hyun Lee and Maani Archang. Randor Radakovits from Synthetic Genomics of San Diego is also an author.

The study was supported by the Presidential Early Career Award for Scientists and Engineers and a planning award from the California NanoSystems Institute (CNSI) at UCLA.

Di Carlo holds faculty appointments in bioengineering, and mechanical and aerospace engineering at UCLA Samueli. He is a member of CNSI and the Jonsson Comprehensive Cancer Center at UCLA.

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