Aurelia Butler

Without enzymes, an organism would not be able to survive. It is these biocatalysts that facilitate a whole range of chemical reactions, producing the building blocks of the cells. Enzymes are also used widely in biotechnology and in our households, where they are used in detergents, for example.
To describe metabolic processes facilitated by enzymes, scientists refer to what is known as the Michaelis-Menten equation. The equation describes the rate of an enzymatic reaction depending on the concentration of the substrate — which is transformed into the end products during the reaction. A central factor in this equation is the ‘Michaelis constant’, which characterises the enzyme’s affinity for its substrate.
It takes a great deal of time and effort to measure this constant in a lab. As a result, experimental estimates of these constants exist for only a minority of enzymes. A team of researchers from the HHU Institute of Computational Cell Biology and Chalmers University of Technology in Stockholm has now chosen a different approach to predict the Michaelis constants from the structures of the substrates and enzymes using AI.
They applied their approach, based on deep learning methods, to 47 model organisms ranging from bacteria to plants and humans. Because this approach requires training data, the researchers used known data from almost 10,000 enzyme-substrate combinations. They tested the results using Michaelis constants that had not been used for the learning process.
Prof. Lercher had this to say about the quality of the results: “Using the independent test data, we were able to demonstrate that the process can predict Michaelis constants with an accuracy similar to the differences between experimental values from different laboratories. It is now possible for computers to estimate a new Michaelis constant in just a few seconds without the need for an experiment.”
The sudden availability of Michaelis constants for all enzymes of model organisms opens up new paths for metabolic computer modelling, as highlighted by the journal PLOS Biology in an accompanying article.
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Materials provided by Heinrich-Heine University Duesseldorf. Original written by Arne Claussen. Note: Content may be edited for style and length. More

Diagnosing and treating cancer can be a race against time. By the time the disease is diagnosed in a patient, all too often it is advanced and able to spread throughout the body, decreasing chances of survival. Early diagnosis is key to stopping it.
In a new Concordia-led paper published in the journal Biosensors and Bioelectronics, researchers describe a new liquid biopsy method using lab-on-a-chip technology that they believe can detect cancer before a tumour is even formed.
Using magnetic particles coated in a specially designed bonding agent, the liquid biopsy chip attracts and captures particles containing cancer-causing biomarkers. A close analysis can identify the type of cancer they are carrying. This, the researchers say, can significantly improve cancer diagnosis and treatment.
Trapping the messenger
The chip targets extracellular vesicles (EVs), a type of particle that is released by most kinds of organic cells. EVs — sometimes called exosomes — are extremely small, usually measuring between 40 and 200 nanometres. But they contain a cargo of proteins, nucleic acids such as RNA, metabolites and other molecules from the parent cell, and they are taken up by other cells. If EVs contain biomarkers associated with cancer and other diseases, they will spread their toxic cargo from cell to cell.
To capture the cancer-carrying exosomes exclusively, the researchers developed a small microfluidic chip containing magnetic or gold nanoparticles coated with a synthetic polypeptide to act as a molecular bonding agent. When a droplet of organic liquid, be it blood, saliva, urine or any other, is run through the chip, the exosomes attach themselves to the treated nanoparticles. After the exosomes are trapped, the researchers then separate them from the nanoparticles and carry out proteomic and genomic analysis to determine the specific cancer type.
“This technique can provide a very early diagnosis of cancer that would help find therapeutic solutions and improve the lives of patients,” says the paper’s senior author Muthukumaran Packirisamy, a professor in the Department of Mechanical, Industrial and Aerospace Engineering and director of Concordia’s Optical Bio-Microsystems Laboratory.
Alternatives to conventional chemo and exploratory surgeries
“Liquid biopsies avoid the trauma of invasive biopsies, which involve exploratory surgery,” he adds. “We can get all the cancer markers and cancer prognoses just by examining any bodily fluid.”
Having detailed knowledge of a particular form of cancer’s genetic makeup will expose its weaknesses to treatment, notes Anirban Ghosh, a co-author and affiliate professor at Packirisamy’s laboratory. “Conventional chemotherapy targets all kinds of cells and results in significant and unpleasant side effects,” he says. “With the precision diagnostics afforded to us here, we can devise a treatment that only targets cancer cells.”
The paper’s lead author is PhD student Srinivas Bathini, whose academic background is in electrical engineering. He says the interdisciplinary approach to his current area of study has been challenging and rewarding and notes that the technology’s potential could revolutionize medical diagnostics. The researchers used breast cancer cells in this study but are looking at ways to expand their capabilities to include a wide range of disease testing.
“Perhaps one day this product could be as readily available as other point-of-care devices, such as home pregnancy tests,” he speculates.
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Materials provided by Concordia University. Original written by Patrick Lejtenyi. Note: Content may be edited for style and length. More

In many parts of the world, the supply of COVID-19 vaccines continues to lag behind the demand. While most vaccines are designed as a two-dose regimen, some countries, like Canada, have prioritized vaccinating as many people as possible with a single dose before giving out an additional dose.
In Chaos, by AIP Publishing, researchers from the Frankfurt School of Finance and Management and the University of California, Los Angeles illustrate the conditions under which a “prime first” vaccine campaign is most effective at stopping the spread of the COVID-19 virus.
The prime first campaign does not suggest people should receive only one dose of the vaccine. Instead, it emphasizes vaccinating large numbers of people as quickly as possible, then doubling back to give out second doses. In comparison, the “prime boost” vaccine campaign prioritizes fully vaccinating fewer people.
Immunologically speaking, the prime boost scenario is always superior. However, under supply constraints, the advantages of vaccinating twice as many people may outweigh the advantages of a double dose.
The scientists simulated the transmission of COVID-19 with a susceptible, exposed, infected, recovered, deceased model. Each of these disease states is associated with a compartment containing individual people. Transitions between compartments depend on disease parameters like virus transmissibility.
Each compartment is further divided to account for unvaccinated, partially vaccinated, and fully vaccinated individuals. The researchers measured how each vaccine group compared to the others under different conditions.
“We have this giant degree of uncertainty about the parameters of COVID-19,” said author Jan Nagler. “We acknowledge that we don’t know these precise values, so we sample over the entire parameter space. We give a nice idea of when prime first campaigns are better with respect to saving lives than prime boost vaccination.”
The team found the vaccine waning rate to be a critically important factor in the decision. If the waning, or decrease in vaccine effectiveness, is too strong after a single dose, the double dose vaccination strategy is often the better option.
However, the vaccine strategy flips if the waning rate after a single dose is more like the waning rate after a double dose.
“Our results suggest that better estimates of immunity waning rates are important to decide if prime first protocols are more effective than prime boost vaccination,” said author Lucas Böttcher.
As the scientific community gathers more data on COVID-19 vaccinations, the scientists hope this model will become more informative for public health experts and politicians who must decide for or against a certain vaccination protocol.
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Along with the use of face masks, social distancing in public remains one of the most practiced front-line defenses against the spread of COVID-19. However, flows of pedestrians, including those practicing the 6-foot rule for distancing, are dynamic and characterized by nuances not always carefully considered in the context of everyday, public spaces.
In Physics of Fluids, by AIP Publishing, researchers from Carnegie Mellon University examine the dynamics of social distancing practices through the lens of particle-based flow simulations. The study models social distance as the distance at which particles, representing pedestrians, repel fellow particles.
“Even at modest pedestrian density levels, a strong preference for 6 feet of social distance can cause large-scale pedestrian ‘traffic jams’ that take a long time to clear up,” said Gerald J. Wang, of Carnegie Mellon University. “This is pretty evident to all of us who have engaged in that ‘awkward dance of social distance’ in a grocery store aisle during the past 18 months, but it has important implications for how we set occupancy thresholds as workplaces, campuses, and entertainment venues return to pre-pandemic densities.”
Motivated by the pandemic, the researchers shed light on the relationship between social distancing and pedestrian flow dynamics in corridors by illustrating how adherence to social distancing protocols affects two-way pedestrian movement in a shared space. The results add to a significant body of recent work around the effects of various factors on pedestrian counterflows and focuses on the characterization of jamming phenomena in relatively narrow corridors, a topic of current interest.
“Dense pedestrian flows plus social distancing recommendations is a recipe for a lot of frustration,” said Wang. “I mean this both in the physics sense of the word ‘frustration,’ with low particle mobilities because a bunch of ‘stuff’ is seemingly in their way, and in the everyday sense of the word ‘frustration,’ with people feeling flustered because, well, a bunch of ‘stuff’ is seemingly in their way!”
Wang noted public health messaging should be aligned with realistic, achievable behavior, adding that “strict adherence to social distancing — a la ‘the 6-foot rule’ — is simply not a practical recommendation in pedestrian flows at densities that are typical of large, shared venues.”
Though conceptually easy to digest, the findings underscore the complications of applying a “one-size-fits-all” policy recommendation to a public sphere characterized by nuanced pedestrian flow dynamics.
“Particle-based flow simulation, powered by high-performance computing, has enormous potential to rapidly explore a broad range of pedestrian flow problems, both during the pandemic and beyond,” said co-author Kelby B. Kramer.
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The COVID-19 pandemic has led to more than 218 million infections and over 4.5 million deaths as of Sept. 3, 2021. Nonpharmaceutical interventions (NPIs), such as case isolation, quarantining contacts, and the complete lockdown of entire countries, were implemented in an effort to contain the pandemic. But these NPIs often come at the expense of economic disruption, harm to social and mental well-being, and costly administration costs to ensure compliance.
Given the slow rollout of vaccination programs worldwide and the rise of several mutations of the coronavirus, the use of these types of interventions will continue for some time. In Chaos, by AIP Publishing, researchers in China use a data-driven agent-based model to identify new and sustainable NPIs to contain outbreaks while minimizing the economic and social costs.
“Based on the proposed model, we proposed targeted interventions, which can contain the outbreak with minimal disruption of society. This is of particular importance in cities like Hong Kong, whose economy relies on international trade,” said author Qingpeng Zhang.
The researchers built a data-driven mobility model to simulate COVID-19 spreading in Hong Kong by combining synthetic population, human behavior patterns, and a viral transmission model. This model generated 7.55 million agents to describe the infectious state and movement for each Hong Kong resident.
Since mobile phone data is difficult to obtain in most countries, the researchers calibrated their model with open-source data, so it could be easily extended to the modeling of other metropolises with various demographic and human mobility patterns.
“With the agent-based model, we can simulate very detailed scenarios in Hong Kong, and based on these simulations, we are able to propose targeted interventions in only a small portion of the city instead of city-level NPIs,” said Zhang.
The researchers found that by controlling a small percentage (top 1%-2%) of grids in Hong Kong, the virus could be largely contained. While such interventions are not as effective as citywide NPIs and compulsory COVID-19 testing, such targeted control has the benefit of a much smaller disruption of society.
The proposed model leading to the targeted interventions has the potential to guide current citywide NPIs to achieve a balance between lowering the risk and preserving human mobility and economy of the city.
“Our findings also apply to other major cities in the world, such as Beijing, New York, London, and Toyko, as COVID-19 is likely to be around indefinitely, and we have to learn how to live with it,” said Zhang.
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A team of physicists from Germany and Sweden working with first author Jens Christian Grauer from Heinrich Heine University Düsseldorf (HHU) has examined a special system of colloidal particles that they activated using laser light. The researchers discovered that self-propelling droplets, which they have named ‘droploids’, formed which contain the particles as an internal motor. They describe these droploids in more detail in the latest edition of the journal Nature Communications.
According to an age-old saying, the whole is often more than the sum of its parts. After all, a sandwich made of bread, lettuce and mayonnaise tastes better than its individual components. A team of physicists from HHU, TU Darmstadt and Sweden’s University of Gothenburg has determined that this adage is also true in the realm of physics, and that combining individual parts can create something with entirely new properties.
The research project involved combining different atoms and larger particles and studying the effects they have on each other. It is ultimately a typical example of what the matter that surrounds us is composed of. The researchers extended this general principle of combination to include additional feedback processes, thus creating new kinds of dynamic structures referred to as ‘positive feedback loops’.
Specifically, they combined two different types of colloid particles — in a water-lutidine heat bath. They irradiated the bath with lasers, and the light from the lasers brought the liquid near the particles to the critical point. The fluctuations are particularly strong at this point, allowing droplet-like structures to form that in turn surround the particles.
Inside the droplets, the two types of colloid particles heat up to different temperatures. This results in effective forces that contradict Newton’s fundamental law of motion (actio = reactio) to propel the droplets forwards. This means that the colloid particles induce the formation of droplets that encapsulate the colloids and are in turn propelled by the particles. This feedback loop results in novel superstructures with a self-organised colloidal motor. The researchers adopted the term ‘droploids’, a portmanteau of the words ‘droplets’ and ‘colloids’, to describe these superstructures.
The research team combined theoretical and experimental approaches, with the system modelling performed in Düsseldorf and Darmstadt, while the colleagues in Gothenburg verified the findings using real-life experiments, thus confirming the theoretical models.
Prof. Dr. Hartmut Löwen, Head of the Institute of Theoretical Physics II at HHU, had this to say: “It’s important here that the process can be controlled entirely by laser illumination. This makes it possible to steer the system externally so that it is flexible for different applications.”
Prof. Dr. Benno Liebchen, leader of the “Theory of Soft Matter” working group at TU Darmstadt, explained the actual use of the droploids as follows: “Besides justifying a novel concept for micromotors, the droploids and the non-reciprocal interactions involved could serve as important ingredients for generating future biomimetic materials.”
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A research collaboration between Associate Professor MISHIMA Tomokazu (Kobe University Graduate School of Maritime Sciences) and Associate Professor LAI Ching-Ming (National Chung Hsing University, Taiwan) has successfully developed a new power controller system for wireless power transfer. The developed system is highly precise and efficient, and the circuitry is simpler than existing systems. This technical proposal will effectively reduce the amount of circuit components in wireless power transfer devices, as well as their cost and weight. These research results were given online advanced publication in the international scientific journal ‘IEEE Journal of Emerging and Selected Topics in Industrial Electronics’ on August 6, 2021.
Wireless transfer systems are used to transfer electric energy in a contactless manner to the batteries inside electrified vehicles, such as automated guided vehicles in factories, electric cars, and ships. Consequently, wireless transfer systems have been gathering much attention from various fields in terms of improving the convenience of electrical energy utilization and the advancement of clean energy. In a wireless transfer system, contactless power transfer occurs between the transferring (Tx) coils and the receiving (Rx) coils. However, a large amount of the transferred power is lost if the distance (gap) between the two coils increases and they are no longer in their optimum position. To prevent power losses and reduced efficiency resulting from these occurrences, it is necessary to control electrical parameters, such as the frequency of Tx and Rx coils’ currents, in accordance with the battery capacity. Consequently, the structures of power conversion and controller devices have become more complex.
Research Findings
To tackle the technical issue mentioned above, Associate Professor Mishima et al. have developed a novel control strategy that applies resonant frequency tracking and load impedance regulation to a high frequency inverter in the Tx side. Resonant frequency tracking automatically adjusts the operation of the high frequency inverter via the phase difference between the current and voltage of the Tx coils in a highly efficient manner. In addition, applying delta sigma transformation (a technique for processing electrical signals) into the pulse density modulation of the high frequency inverter eliminates the need for a complicated extra controller in the Rx side. In this way, the researchers developed a novel, practical and cost-effective power control scheme that enables a wireless power transfer system to be operated with high precision and efficiency from the Tx side.
Further Research
The researchers have successfully simplified the structure of the power conversion circuitry in the Rx side as well as the logical scheme of the power controller. This development and its experimental verification demonstrate that it is possible to reduce the number of components, which will contribute towards the implementation of highly reliable and cost-effective wireless transfer systems. For example, this technology could be especially beneficial for electric cars, drones and other such vehicles for which a light weight and compact size are important. Furthermore, the research results could also be applied to biomedical wireless power transfers for implantable medical devices such as pacemakers.
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Blood transfusions save lives, yet the precious fluid is in desperately short supply, not just in the U.S. but around the globe. But what if transfusions don’t always require blood?
A new mathematical model of the body’s interacting physiological and biochemical processes — including blood vessel expansion, blood thickening and flow-rate changes in response to the transfusion of red blood cells — shows that patients with anemia, or blood with low oxygen levels, can be effectively treated with transfusions of blood substitutes that are more readily available.
The research, co-authored by scientists at Stanford University and the University of California, San Diego (UCSD), was published on Oct. 14 in the Journal of Applied Physiology.
Using a different fluid could also eliminate a harmful consequence of blood transfusion: Blood use has been observed to lower lifespan by 6 percent per unit transfused per decade because of its adverse side effects.
“Instead of real blood, we can use a substitute that can lower the costs and eliminate blood transfusion’s negative effects,” said lead study author Weiyu Li, a PhD student in energy resources engineering at Stanford’s School of Earth, Energy & Environmental Sciences (Stanford Earth).
Transfusion is a common procedure for transferring blood components directly to anemic patients’ circulation. Red blood cells are uniquely equipped to perform the function of carrying oxygen, which is why they are used for transfusions for patients experiencing anemia. But the process of obtaining, storing and delivering the correct, sanitary blood type for each patient is also intensive and costly. Moreover, the supply of blood that is available falls far short of the demand: The global deficit across all countries without enough supply totals about 100 million units of blood per year. More