Computers Math

Scientists have developed a method using machine learning to better analyze data from a powerful scientific tool: nuclear magnetic resonance (NMR). One way NMR data can be used is to understand proteins and chemical reactions in the human body. NMR is closely related to magnetic resonance imaging (MRI) for medical diagnosis.
NMR spectrometers allow scientists to characterize the structure of molecules, such as proteins, but it can take highly skilled human experts a significant amount of time to analyze that data. This new machine learning method can analyze the data much more quickly and just as accurately.
In a study recently published in Nature Communications, the scientists described their process, which essentially teaches computers to untangle complex data about atomic-scale properties of proteins, parsing them into individual, readable images.
“To be able to use these data, we need to separate them into features from different parts of the molecule and quantify their specific properties,” said Rafael Brüschweiler, senior author of the study, Ohio Research Scholar and a professor of chemistry and biochemistry at The Ohio State University. “And before this, it was very difficult to use computers to identify these individual features when they overlapped.”
The process, developed by Dawei Li, lead author of the study and a research scientist at Ohio State’s Campus Chemical Instrument Center, teaches computers to scan images from NMR spectrometers. Those images, known as spectra, appear as hundreds and thousands of peaks and valleys, which, for example, can show changes to proteins or complex metabolite mixtures in a biological sample, such as blood or urine, at the atomic level. The NMR data give important information about a protein’s function and important clues about what is happening in a person’s body.
But deconstructing the spectra into readable peaks can be difficult because often, the peaks overlap. The effect is almost like a mountain range, where closer, larger peaks obscure smaller ones that may also carry important information. More

An intrinsic magnetic topological insulator MnBi2Te4 has been discovered with a large band gap, making it a promising material platform for fabricating ultra-low-energy electronics and observing exotic topological phenomena.
Hosting both magnetism and topology, ultra-thin (only several nanometers in thickness) MnBi2Te4 was found to have a large band-gap in a Quantum Anomalous Hall (QAH) insulating state, where the material is metallic (ie, electrically conducting) along its one-dimensional edges, while electrically insulating in its interior. The almost zero resistance along the 1D edges of a QAH insulator, make it promising for lossless transport applications and ultra-low energy devices.
HISTORY OF QAH: HOW TO ACHIEVE THE DESIRED EFFECT
Previously, the path towards realising the QAH effect was to introduce dilute amounts of magnetic dopants into ultra-thin films of 3D topological insulators.
However, dilute magnetic doping results in a random-distribution of magnetic impurities, causing non-uniform doping and magnetisation. This greatly suppresses the temperature at which the QAH effect can be observed and limits possible future applications.
A simpler option is to use materials that host this electronic state of matter as an intrinsic property. More

The EVONANO platform allows scientists to grow virtual tumours and use artificial intelligence to automatically optimise the design of nanoparticles to treat them.
The ability to grow and treat virtual tumours is an important step towards developing new therapies for cancer. Importantly, scientists can use virtual tumours to optimise design of nanoparticle-based drugs before they are tested in the laboratory or patients.
The paper, ‘Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment,’ is published today in the Nature journal Computational Materials. The paper is the result of the European project EVONANO which involves Dr Sabine Hauert and Dr. Namid Stillman from the University of Bristol, and is led by Dr Igor Balaz at the University of Novi Sad.
“Simulations enable us to test many treatments, very quickly, and for a large variety of tumours. We are still at the early stages of making virtual tumours, given the complex nature of the disease, but the hope is that even these simple digital tumours can help us more efficiently design nanomedicines for cancer,” said Dr Hauert.
Dr Hauert said having the software to grow and treat virtual tumours could prove useful in the development of targeted cancer treatments.
“In the future, creating a digital twin of a patient tumour could enable the design of new nanoparticle treatments specialised for their needs, without the need for extensive trial and error or laboratory work, which is often costly and limited in its ability to quickly iterate on solutions suited for individual patients,” said Dr Hauert. More

Researchers from SANKEN (The Institute of Scientific and Industrial Research) at Osaka University and JOANNEUM RESEARCH (Weiz, Austria), have shown how exposing an organic polymer to ultraviolet light can precisely modify its electronic properties. This work may aid in the commercialization of flexible electronics that can be used for real-time healthcare monitoring, along with data processing.
While the integrated circuits inside your smart phone are quite impressive, they lack certain important features. Because the electronics are silicon based, they are very rigid, both in the literal sense of being inflexible, as well as having chemical properties that are not easily altered. Newer devices, including OLED displays, are made from carbon-based organic molecules with chemical properties than can be tuned by scientists to produce the most efficient circuits. However, controlling the characteristics of organic transistors usually requires the integration of complex structures made of various materials.
Now, a team of researchers led by Osaka University have used UV light to precisely change the chemical structure of a dielectric polymer called PNDPE. The light breaks specific bonds in the polymer, which can then rearrange into new versions, or create crosslinks between strands. The longer the light is on, the more the polymer get altered. By using a shadow mask, the UV light is applied to just the desired areas, tuning the circuit behavior. This method can pattern transistors of the desired threshold voltage with high spatial resolution using just a single material.
“We have succeeded in controlling the characteristics of organic integrated circuits using persistent light-induced changes in the molecular structure itself,” study corresponding author Takafumi Uemura explains.
In the future, we may see smart versions of almost everything, from medicine bottles to safety vests. “Meeting the computational demands of ‘the Internet of Things’ will very likely require flexible electronic solutions,” senior author Tsuyoshi Sekitani says. In particular, this technology can be applied to manufacturing methods for ultra-lightweight wearable healthcare devices.
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Materials provided by Osaka University. Note: Content may be edited for style and length. More

Red blood cells are the most abundant cell type in blood, carrying oxygen throughout the human body. In blood circulation, they repetitively encounter various levels of oxygen tension. Hypoxia, a low oxygen tension condition, is a very common micro-environmental factor in physiological processes of blood circulation and various pathological processes such as cancer, chronic inflammation, heart attacks and stroke. In addition, an interplay between poor cellular deformability and impaired oxygen delivery is found in various pathological processes such as sickle cell disease. Sickle red blood cells simultaneously undergo drastic mechanical deformation during the sickling and unsickling process.
The interactions between hypoxia and cell biomechanics and the underlying biochemical mechanisms of the accelerated damage in diseased red blood cells are well understood, however, the exact biomechanical consequences of hypoxia contributing to red blood cell degradation (aging) remains elusive.
Researchers from Florida Atlantic University’s College of Engineering and Computer Science, in collaboration with the Massachusetts Institute of Technology (MIT), sought to identify the role of hypoxia on red blood cell aging via the biomechanical pathways. In particular, they examined hypoxia-induced impairment of red blood cell deformability at the single cell level, compared the differences between non-cyclic hypoxia and cyclic hypoxia, and documented any cumulative effect vs. hypoxia cycles, such as aspects that have not been studied quantitatively. Red blood cell deformability is an important biomarker of its functionality.
For the study, published in the journal Lab on a Chip, researchers developed a multifaceted microfluidic in vitro assay to precisely control the gaseous environment while probing the mechanical performance of red blood cells, which can be used as a characterization tool for other cell types involved in oxygen-dependent biological processes. The assay holds promise for investigating hypoxic effects on the metastatic potential and relevant drug resistance of cancer cells. Cancer cells are more metastatic in a hypoxic tumor microenvironment and cancer cell stiffness has been shown to be an effective biomarker of their metastatic potential.
Findings from the study indicate an important biophysical mechanism underlying red blood cell aging in which the cyclic hypoxia challenge alone can lead to mechanical degradation of the red blood cell membrane. This process in combination with the deformation-induced mechanical fatigue represents two major fatigue loading conditions that circulating red blood cells experience.
“A unique feature of our system lies in that the cell deformability measurement can be made on multiple, individually tracked red blood cells under a well-controlled oxygen tension environment,” said Sarah Du, Ph.D., senior author, an associate professor in FAU’s Department of Ocean and Mechanical Engineering, and a member of FAU’s Institute for Human Health and Disease Intervention (I-HEALTH). “Our results showed that the deformability of red blood cells decreases under deoxygenation conditions by before-and-after mechanical characterization of individual cells in response to the switching of oxygen levels within a microfluidic device.”
Microfluidics serves as a miniaturized and efficient platform for gas diffusion by interfacing the gas and aqueous solution through flow or a gas-permeable membrane, which also is amenable to the control of the cellular gaseous microenvironment.
For the study, researchers subjected red blood cells to a well-controlled repeated hypoxia microenvironment while allowing simultaneous characterization of the cell mechanical properties. They integrated an electro-deformation technique into a microdiffusion chamber, which was easy to implement and flexible in simultaneous applications of cyclic hypoxia challenge and shear stresses on individual cells in suspension and under quasi-stationary conditions.
Measurements of biomarkers, such as oxidative damage, can provide additional information to establish quantitative relationships between the fatigue loading and the biological processes, allowing a better understanding of red blood cell failure and aging. The microfluidic assay also can be extended to study other types of biological cells for their mechanical performance and response to gaseous environments.
“The unique method developed by professor Du’s lab also can be a useful tool to predict the mechanical performance of natural and artificial red blood cells for transfusion purposes as well as to assess the efficacy of relevant reagents in extending the cellular lifespan in circulation,” said Stella Batalama, Ph.D., dean, College of Engineering and Computer Science. “This promising and cutting-edge assay has the potential to further extend to red blood cells in other blood diseases and other cell types.”
Study co-authors are Ming Dao, Ph.D., Department of Materials Science and Engineering, MIT; Yuhao Qiang, Ph.D., FAU College of Engineering and Computer Science and currently a postdoctoral researcher at MIT; and Jia Liu, Ph.D., FAU College of Engineering and Computer Science.
This research is based on the materials supported by the National Science Foundation.
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Materials provided by Florida Atlantic University. Original written by Gisele Galoustian. Note: Content may be edited for style and length. More

A relatively new type of computing that mimics the way the human brain works was already transforming how scientists could tackle some of the most difficult information processing problems.
Now, researchers have found a way to make what is called reservoir computing work between 33 and a million times faster, with significantly fewer computing resources and less data input needed.
In fact, in one test of this next-generation reservoir computing, researchers solved a complex computing problem in less than a second on a desktop computer.
Using the now current state-of-the-art technology, the same problem requires a supercomputer to solve and still takes much longer, said Daniel Gauthier, lead author of the study and professor of physics at The Ohio State University.
“We can perform very complex information processing tasks in a fraction of the time using much less computer resources compared to what reservoir computing can currently do,” Gauthier said.
“And reservoir computing was already a significant improvement on what was previously possible.”
The study was published today (Sept. 21, 2021) in the journal Nature Communications. More

Researchers at the University of Liverpool have created a collaborative artificial intelligence tool that reduces the time and effort required to discover truly new materials.
Reported in the journal Nature Communications, the new tool has already led to the discovery of four new materials including a new family of solid state materials that conduct lithium. Such solid electrolytes will be key to the development of solid state batteries offering longer range and increased safety for electric vehicles. Further promising materials are in development.
The tool brings together artificial intelligence with human knowledge to prioritise those parts of unexplored chemical space where new functional materials are most likely to be found.
Discovering new functional materials is a high-risk, complex and often long journey as there is an infinite space of possible materials accessible by combining all of the elements in the periodic table, and it is not known where new materials exist.
The new AI tool was developed by a team of researchers from the University of Liverpool’s Department of Chemistry and Materials Innovation Factory, led by Professor Matt Rosseinsky, to address this challenge.
The tool examines the relationships between known materials at a scale unachievable by humans. These relationships are used to identify and numerically rank combinations of elements that are likely to form new materials. The rankings are used by scientists to guide exploration of the large unknown chemical space in a targeted way, making experimental investigation far more efficient. Those scientists make the final decisions, informed by the different perspective offered by the AI.
Lead author of the paper Professor Matt Rosseinsky said: “To date, a common and powerful approach has been to design new materials by close analogy with existing ones, but this often leads to materials that are similar to ones we already have.
“We therefore need new tools that reduce the time and effort required to discover truly new materials, such as the one developed here that combines artificial intelligence and human intelligence to get the best of both.
“This collaborative approach combines the ability of computers to look at the relationships between several hundred thousand known materials, a scale unattainable for humans, and the expert knowledge and critical thinking of human researchers that leads to creative advances.
“This tool is an example of one of many collaborative artificial intelligence approaches likely to benefit scientists in the future.”
Society’s capacity to solve global challenges such as energy and sustainability is constrained by our capability to design and make materials with targeted functions, such as better solar absorbers making better solar panels or superior battery materials making longer range electric cars, or replacing existing materials by using less toxic or scarce elements.
These new materials create societal benefit by driving new technologies to tackle global challenges, and they also reveal new scientific phenomena and understanding. All modern portable electronics are enabled by the materials in lithium ion batteries, which were developed in the 1980s, which emphasises how just one materials class can transform how we live: defining accelerated routes to new materials will open currently unimaginable technological possibilities for our future.
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Materials provided by University of Liverpool. Note: Content may be edited for style and length. More

As a first-year college student in an introductory chemistry class sits down for their midterm, which might be their first stepping stone toward a career in academia, research or medicine, a thought may swirl through their head alongside valence numbers, molar masses and oxidation states — an anxiety that forms itself into a supposition: “Maybe people like me don’t belong in this class.”
That thought is called belonging uncertainty, a feeling of social insecurity related to a person’s identity. New research from the University of Utah shows that belonging insecurity in a STEM course, specifically a first-year chemistry course, can affect a student’s midterm scores, which can then feed back into the student’s belonging uncertainty. For students in groups that are underrepresented in STEM, there’s a danger that such a feedback loop could cause them to decide that science isn’t for them, deterring potential scientists from even entering a STEM field.
“Students in these early STEM courses face many struggles and challenges, such as learning to adjust their study strategies, that are normal for this academic transitional period from high school to college,” says chemistry professor Gina Frey. “The concern is that a student with a high belonging uncertainty has a less stable sense of belonging and will believe the struggles they encounter in these courses are due to their identities as opposed to a normal part of the academic transition that everyone faces in their early years at college.”
The research is published in the Journal of Chemical Education in a special issue on diversity, equity, inclusion and respect in chemistry education research and practice.
Sense of belonging and belonging uncertainty
Belonging uncertainty is different than simply a sense of belonging. A sense of belonging is an individual feeling, Frey says, (i.e. “Do I belong here?”) while belonging uncertainty is tied to the groups in which a person identifies. More