Computers Math

Researchers are developing a deep learning network capable of detecting disease biomarkers with a much higher degree of accuracy.
Experts at the University of Waterloo’s Cheriton School of Computer Science have created a deep neural network that achieves 98 per cent detection of peptide features in a dataset. That means scientists and medical practitioners have a greater chance of discovering possible diseases through tissue sample analysis.
There are multiple existing techniques for detecting diseases by analyzing the protein structure of bio-samples. Computer programs increasingly play a part in this process by examining the large amount of data produced in such tests to pinpoint specific markers of disease.
“But existing programs are often inaccurate or can be limited by human error in their underlying functions,” said Fatema Tuz Zohora, a PhD researcher in the Cheriton School of Computer Science.
“What we’ve done in our research is to create a deep neural network that achieves 98 percent detection of peptide features in a dataset. We’re working to make disease detection more accurate to provide healthcare practitioners with the best tools.”
Peptides are the chains of amino acids that make up proteins in human tissue. It is these small chains that often display the specific markers of disease. Having better testing means it will be possible to detect diseases earlier and with greater accuracy.
Zohora’s team calls their new deep learning network PointIso. It is a form of machine learning or artificial intelligence that was trained on an enormous database of existing sequences from bio-samples.
“Other methods for disease biomarker detections usually have lots of parameters which have to be manually set by field experts,” Zohora said. “But our deep neural network learns the parameters itself, which is more accurate, and makes the disease biomarker discovery approach automated.”
The new program is also unique in that it is not trained to only look for one kind of disease but to identify the biomarkers associated with a range of diseases, including heart disease, cancer and even COVID-19.
“It’s applicable for any kind of disease biomarker discovery,” Zohora said. “And because it is essentially a pattern recognition model, it can be used for detection of any small objects within a large amount of data. There are so many applications for medicine and science; it’s exciting to see the possibilities opening up through this research and how it can help people.”
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When it comes to the design of a novel vaccine against viral infection, vaccine developers have to make several major decisions. One of them is the choice of what type of immune response they wish to induce.
In a recent Forum article in Trends in Immunology a group of researchers at UPF and the Marchuk Institute of Numerical Mathematics in Moscow, Russia, led by Andreas Meyerhans and Gennady Bocharov, provides a theoretical paper that might help with this issue. The researchers have used a mathematical model to better understand the immune response to vaccines. This could help improve vaccine design and simplify the associated technical challenges.
Viruses are intracellular parasites that need host cells to multiply. Thus, for a virus to infect a human, it has to get access to some of the body’s cells that will enable viruses to multiply. Progeny viruses will be assembled within the infected cells and, upon release, will infect other target cells in the surroundings. Without any immune response to counteract the virus, it will continue to spread and may cause organ damage.
The researchers have used a mathematical model to better understand the immune response to vaccines. This could help improve vaccine design and simplify the associated technical challenges.
Vaccines are the most cost-effective way to provide a host with virus-specific immunity that will then help it to keep an infectious virus below pathogenic levels. To do so, vaccines may induce antibodies that help to neutralize assembled free viruses and virus-specific cytotoxic T cells that will kill infected cells and thus reduce the number of virus-producing cells.
While both arms of the immune response are considered of major importance for vaccine efficacy, the question is how do they cooperate? Are their actions simply additive or more than additive? The researchers have now addressed these fundamental questions by examining the contribution of antibodies and cytotoxic T cells using a model based on virus infection dynamics. They show that these two primary control factors of virus infection are cooperating multiplicatively rather than additively. While this relationship might appear rather abstract, it has very practical consequences for vaccine development.
For example, f to be efficient a virus vaccine needs to increase the basic immune response by a factor of 10,000, this may be achieved in two ways. Either antibodies or cytotoxic T cells are increased by a factor of 10,000 or each of these responses is increased by only a factor of 100. The latter might be easier to obtain in practical terms and thus provide vaccine developers with different options for their design.
Although these considerations are based only on theoretical grounds and require experimental validation, the first data in this direction are emerging. “We hope that our conceptional work will positively help with vaccine design,” says Bocharov. And Meyerhans, the last author of the study, adds that “our considerations may help to simplify the technical challenges for novel vaccines and thus be of some practical use for healthcare.”
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No two human beings are the same, a biologic singularity encoded in the unique arrangement of the molecules that make up our individual DNA.
Variation is a cardinal feature of biology, the driver of diversity, and the engine of evolution, but it has a dark side. Alterations in DNA sequences and the resulting proteins that build our cells can sometimes lead to profound disruptions in physiologic function and cause disease.
But which gene alterations are normal or at least inconsequential, and which ones portend disease?
The answer is clear for a handful of well-known genetic mutations, yet despite dramatic leaps in genome sequencing technology over the past 20 years, our ability to interpret the meaning of millions of genetic variations identified through such sequencing still lags behind.
To make sense of it all, researchers at Harvard Medical School and Oxford University have designed an AI tool called EVE (Evolutionary model of Variant Effect), which uses a sophisticated type of machine learning to detect patterns of genetic variation across hundreds of thousands of nonhuman species and then use them to make predictions about the meaning of variations in human genes.
In an analysis published Oct. 27 in Nature, the researchers used EVE to assess 36 million protein sequences and 3,219 disease-associated genes across multiple species. More

An ethical framework developed by government, road users and other stakeholders must steer the introduction of new road rules for connected and automated vehicles (CAVs), international experts say.
They warn that strictly forbidding CAVs of various kinds to break existing traffic rules may hamper road safety, contrary to what most people may claim. However, this requires close scrutiny so these high-tech vehicles can meet their potential to reduce road casualties.
“While they promise to minimise road safety risk, CAVs like hybrid AI systems can still create collision risk due to technological and human-system interaction issues, the complexity of traffic, interaction with other road users and vulnerable road users,” says UK transport consultant Professor Nick Reed, from Reed Mobility, in a new paper in Ethics and Information Technology.
“Ethical goal functions for CAVs would enable developers to optimise driving behaviours for safety under conditions of uncertainty while allowing for differentiation of products according to brand values.”
This part is important since it does not state that all vehicle brands should drive in exactly the same manner, which still allows brand differentiation, researchers say.
Around the world, transport services are already putting CAVs, including driverless cars, on the road to deliver new services and freight options to improve road safety, alleviate congestion and increase drive comfort and transport system productivity. More

Traffic congestion has been worsening since the 1950s in large cities thanks to the exorbitant number of cars sold each year. Unfortunately, the figurative price tag attached to excessive traffic includes higher carbon dioxide emissions, more collectively wasted time, and exacerbated health problems. Many municipalities have tackled the problem of traffic by implementing bicycle sharing systems, in which people can borrow bikes from strategically placed ports and ride wherever they want, as long as they eventually return the bikes to a port, although not necessarily the one from where the bike was originally obtained.
As one may or may not immediately notice, this last permission creates a new problem by itself. Whenever someone borrows a bike and does not make a round trip with it, an additional bike crops up at the destination port just as there’s a loss of one bike at the origin port. As time passes, the distribution of bikes across ports becomes unbalanced, causing both an excessive accumulation of bikes at certain ports and a dearth of bikes in others. This issue is generally addressed by periodically sending out a fleet of vehicles capable of transporting multiple bikes in order to restore ports to their ‘ideal’ number of bikes.
Much research has been dedicated to the bicycle rebalancing problem using a fleet of vehicles. Finding the optimal routing paths for the vehicles is in and of itself a highly complex mathematical problem in the field of combinatorial optimization. One must make sure that the optimization algorithms used can reach a good-enough solution in a reasonable time for a realistically large number of ports and vehicles. Many methods, however, fail to find feasible solutions when multiple constrains are considered simultaneously, such as time, capacity, and loading/unloading constraints for the vehicles.
But what if we allowed the optimization strategy to change the strategies a little bit to make the best out of difficult situations? In a recent study published in MDPI’s Applied Sciences, a team of scientists suggested an innovative twist to the routing problem of bicycle sharing systems using this concept. Led by Professor Tohru Ikeguchi of Tokyo University of Science, the team comprising PhD student Honami Tsushima from Tokyo University of Science and Associate Professor Takafumi Matsuura from Nippon Institute of Technology, Japan, proposed a new formulation of the routing problem in which the constraints imposed on the routings can be violated. This enabled using the optimization algorithm for exploring what is known as the space of “infeasible solutions.” Prof. Ikeguchi explains their reasoning, “In real life, if a work can be completed through overtime within a few minutes, we would work beyond the time limit. Similarly, if we are only carrying four bikes and need to supply five, we would still supply the four we have.”
Following this line of thought, the researchers formulated the “soft constraints” variant of the routing problem in bicycle rebalancing. Using this approach, instead of outright excluding solutions that violate constraints, these can be considered valid paths that incur dynamically adjusted penalties and taken into consideration when assessing possible routings. This approach enabled the team to devise an algorithm that can make use of the space of infeasible solutions to speed up the search for optimal or near-optimal solutions.
The researchers evaluated the performance of their method through numerical experiments with benchmark problems including up to 50 ports and three vehicles. The results show that their strategy could find optimal or near-optimal solutions in all cases, and that the algorithm could search both the feasible and infeasible solution spaces efficiently. This paints a brighter future for people in cities with congested traffic in which bicycle sharing systems could become an attractive solution. As Prof. Ikeguchi remarks, “It is likely that bike sharing systems will spread worldwide in the future, and we believe that the routing problem in bicycle rebalancing is an important issue to be solved in modern societies.”
Hopefully, further efforts to improve bicycle sharing systems will alleviate traffic congestion and make people’s lives in big cities healthier and more enjoyable.
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Scientists from Queen’s University Belfast have developed a computer-aided data tool that could improve treatment for a range of illnesses.
The computer modelling tool will predict novel sites of binding for potential drugs that are more selective, leading to more effective drug targeting, increasing therapeutic efficacy and reducing side effects.
The data tool or protocol will uncover a novel class of compounds — allosteric drugs in G protein-coupled receptors (GPCRs).
GPCRs are the largest membrane protein family that transduce a signal inside cells from hormones, neurotransmitters, and other endogenous molecules. As a result of their broad influence on human physiology, GPCRs are drug targets in many therapeutic areas such as inflammation, infertility, metabolic and neurological disorders, viral infections and cancer. Currently over a third of drugs act via GPCRs. Despite the substantial therapeutic success, the discovery of GPCR drugs is challenging due to promiscuous binding and subsequent side effects.
Recent studies point to the existence of other binding sites, called allosteric sites that drugs can bind to and provide several therapeutic benefits. However, the discovery of allosteric sites and drugs has been mostly serendipitous. Recent X-ray crystallography, that determines the atomic and molecular structure, and cryo-electron microscopy that offers 3D models of several GPCRs offer opportunities to develop computer-aided methodologies to search for allosteric sites.
The researchers developed a computer-aided protocol to map allosteric sites in GPCRs with a view to start rational search of allosteric drugs, presenting the opportunity for new solutions and therapies for a range of diseases.
Dr Irina Tikhonova from the School of Pharmacy at Queen’s University and senior author, explains: “We have developed a novel, cost-effective and rapid pipeline for the discovery of GPCRs allosteric sites, which overcomes the limitations of current computational protocols such as membrane distortion and non-specific binding.
“Our pipeline can identify allosteric sites in a short time, which makes it suitable for industry settings. As such, our pipeline is a feasible solution to initiate structure-based search of allosteric drugs for any membrane-bound drug targets that have an impact on cancer, inflammation, and CNS diseases.”
This research published in ACS Central Science is a collaboration with Queen’s University Belfast and Queen Mary University of London. It is supported by the European Union ‘s Horizon 2020 research and innovation programme under the Marie-Sklodowska-Curie grants agreement and Biotechnology and Biological Science Research Council.
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When most people think of wearable devices, they think of smart watches, smart glasses, fitness trackers, even smart clothing. These devices, part of a fast-growing market, have two things in common: They all need an external power source, and they all require exacting manufacturing processes. Until now.
Yanliang Zhang, associate professor of aerospace and mechanical engineering at the University of Notre Dame, and doctoral student Yipu Du have created an innovative hybrid printing method — combining multi-material aerosol jet printing and extrusion printing — that integrates both functional and structural materials into a single streamlined printing platform. Their work was recently published in Nano Energy.
Zhang and Du, in collaboration with a team at Purdue University led by professor Wenzhuo Wu, also have developed an all-printed piezoelectric (self-powered) wearable device.
Using their new hybrid printing process, the team demonstrated stretchable piezoelectric sensors, conformable to human skin, with integrated tellurium nanowire piezoelectric materials, silver nanowire electrodes and silicone films. The devices printed by the team were then attached to a human wrist, accurately detecting hand gestures, and to an individual’s neck, detecting the individual’s heartbeat. Neither device used an external power source.
Piezoelectric materials are some of the most promising materials in the manufacture of wearable electronics and sensors because they generate their own electrical charge from applied mechanical stress instead of from a power source.
Yet printing piezoelectric devices is challenging because it often requires high electric fields for poling and high sintering temperatures. This adds to the time and cost of the printing process and can be detrimental to surrounding materials during sensor integration.
“The biggest advantage of our new hybrid printing method is the ability to integrate a wide range of functional and structural materials in one platform,” said Zhang.
“This streamlines the processes, reducing the time and energy needed to fabricate a device, while ensuring the performance of printed devices.”
Vital to the design, said Zhang, are nanostructured materials with piezoelectric properties, which eliminate the need for poling or sintering, and the highly stretchable silver nanowire electrodes, which are important for wearable devices attached to bodies in motion.
“We’re excited to see the wide range of opportunities that will open up for printed electronics and wearable devices because of this very versatile printing process,” said Zhang.
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Materials provided by University of Notre Dame. Original written by Nina Welding. Note: Content may be edited for style and length. More

In physics, “universality” refers to properties of systems that are independent of their details. Establishing the universality of quantum dynamics is one of the key interests of theoretical physicists. Now, researchers from Japan have identified such a universality in disordered quantum systems, characterized by a one-parameter scaling for surface roughness and entanglement entropy (a measure of quantum entanglement).
Many-particle systems in the real world are often imbued with “disorder” or “randomness.” This, in turn, leads to the occurrence of phenomena unique to such systems. For instance, electrons in strongly disordered systems can become localized due to destructive interference, a phenomenon known as “Anderson localization.”
Anderson localization has been studied extensively in terms of one-parameter scaling, where system properties are scaled based on one specific parameter. But while most studies have focused on static properties, disorder can also significantly influence quantum dynamics such as entanglement dynamics and transport phenomena.
In a recent study published in Physical Review Letters, a team of physicists led by Prof. Kazuya Fujimoto from Nagoya University has now demonstrated numerically a dynamical one-parameter scaling called “Family-Vicsek (FV) scaling” for disordered quantum systems.
“While the FV scaling is originally known from classical surface growth, we found the scaling in random quantum systems by introducing a ‘quantum surface height operator’,” explains Prof. Fujimoto.
In their study, the physicists considered a system of non-interacting spinless fermions in a disordered one-dimensional potential for three common models. They found that the surface roughness followed FV scaling characterized with three exponents. Further numerical analysis showed that the surface roughness could be related to the entanglement entropy (EE), thus indicating an FV-type scaling for EE. In addition, they observed anomalous scaling exponents for one of the models and attributed it to the presence of localized states in a delocalized phase, a classic signature of quantum disordered systems.
Importantly, surface roughness can be measured experimentally for cold-atomic systems using microscopy techniques, which makes the experimental estimation of EE viable in non-interacting fermions.
“These findings will deepen our understanding of nonequilibrium physics and provide a novel viewpoint to classify the universal non-equilibrium phenomena emerging in random quantum systems,” says Prof. Fujimoto.
While the findings of the study do not have a direct influence on our daily lives, they certainly pave the way for a better understanding of real-world quantum systems.
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