Computers Math

An artificial intelligence (AI)-based technology rapidly diagnoses rare disorders in critically ill children with high accuracy, according to a report by scientists from University of Utah Health and Fabric Genomics, collaborators on a study led by Rady Children’s Hospital in San Diego. The benchmark finding, published in Genomic Medicine, foreshadows the next phase of medicine, where technology helps clinicians quickly determine the root cause of disease so they can give patients the right treatment sooner.
“This study is an exciting milestone demonstrating how rapid insights from AI-powered decision support technologies have the potential to significantly improve patient care,” says Mark Yandell, Ph.D., co-corresponding author on the paper. Yandell is a professor of human genetics and Edna Benning Presidential Endowed Chair at U of U Health, and a founding scientific advisor to Fabric.
Worldwide, about seven million infants are born with serious genetic disorders each year. For these children, life usually begins in intensive care. A handful of NICUs in the U.S., including at U of U Health, are now searching for genetic causes of disease by reading, or sequencing, the three billion DNA letters that make up the human genome. While it takes hours to sequence the whole genome, it can take days or weeks of computational and manual analysis to diagnose the illness.
For some infants, that is not fast enough, Yandell says. Understanding the cause of the newborn’s illness is critical for effective treatment. Arriving at a diagnosis within the first 24 to 48 hours after birth gives these patients the best chance to improve their condition. Knowing that speed and accuracy are essential, Yandell’s group worked with Fabric to develop the new Fabric GEM algorithm, which incorporates AI to find DNA errors that lead to disease.
In this study, the scientists tested GEM by analyzing whole genomes from 179 previously diagnosed pediatric cases from Rady’s Children’s Hospital and five other medical centers from across the world. GEM identified the causative gene as one of its top two candidates 92% of the time. Doing so outperformed existing tools that accomplished the same task less than 60% of the time.
“Dr. Yandell and the Utah team are at the forefront of applying AI research in genomics,” says Martin Reese, Ph.D., CEO of Fabric Genomics and a co-author on the paper. “Our collaboration has helped Fabric achieve an unprecedented level of accuracy, opening the door for broad use of AI-powered whole genome sequencing in the NICU.”
GEM leverages AI to learn from a vast and ever-growing body of knowledge that has become challenging to keep up with for clinicians and scientists. GEM cross-references large databases of genomic sequences from diverse populations, clinical disease information, and other repositories of medical and scientific data, combining all this with the patient’s genome sequence and medical records. To assist with the medical record search, GEM can be coupled with a natural language processing tool, Clinithink’s CLiX focus, which scans reams of doctors’ notes for the clinical presentations of the patient’s disease.
“Critically ill children rapidly accumulate many pages of clinical notes,” Yandell says. “The need for physicians to manually review and summarize note contents as part of the diagnostic process is a massive time sink. The ability of Clinithink’s tool to automatically convert the contents of these notes in seconds for consumption by GEM is critical for speed and scalability.”
Existing technologies mainly identify small genomic variants that include single DNA letter changes, or insertions or deletions of a small string of DNA letters. By contrast, GEM can also find “structural variants” as causes of disease. These changes are larger and are often more complex. It’s estimated that structural variants are behind 10 to 20% of genetic disease.
“To be able to diagnose with more certainty opens a new frontier,” says Luca Brunelli, M.D., neonatologist and professor of pediatrics at U of U Health, who leads a team using GEM and other genome analysis technologies to diagnose patients in the NICU. His goal is to provide answers to families who would have had to live with uncertainty before the development of these tools. He says these advances now provide an explanation for why a child is sick, enable doctors to improve disease management, and, at times, lead to recovery.
“This is a major innovation, one made possible through AI,” Yandell says. “GEM makes genome sequencing more cost-effective and scalable for NICU applications. It took an international team of clinicians, scientists, and software engineers to make this happen. Seeing GEM at work for such a critical application is gratifying.”
Fabric and Yandell’s team at the Utah Center for Genetic Discovery have had their collaborative research supported by several national agencies, including the National Institutes of Health and American Heart Association, and by the U of U’s Center for Genomic Medicine. Yandell will continue to advise the Fabric team to further optimize GEM’s accuracy and interface for use in the clinic. More

As the world’s data storage needs grow, new strategies for preserving information over long periods with reduced energy consumption are needed. Now, researchers reporting in ACS Central Science have developed a data storage approach based on mixtures of fluorescent dyes, which are deposited onto an epoxy surface in tiny spots with an inkjet printer. The mixture of dyes at each spot encodes binary information that is read with a fluorescent microscope. 
Current devices for data storage, such as optical media, magnetic media and flash memory, typically last less than 20 years, and they require substantial energy to maintain stored information. Scientists have explored using different molecules, such as DNA or other polymers, to store information at high density and without power, for thousands of years or longer. But these approaches are limited by factors such as high relative cost and slow read/write speeds. George Whitesides, Amit Nagarkar and colleagues wanted to develop a molecular strategy that stores information with high density, fast read/write speeds and acceptable cost.
The researchers chose seven commercially available fluorescent dye molecules that emit light at different wavelengths. They used the dyes as bits for American Standard Code for Information Interchange (ACSII) characters, where each bit is a “0” or “1,” depending on whether a particular dye is absent or present, respectively. A sequence of 0s and 1s was used to encode the first section of a seminal research paper by Michael Faraday, the famous scientist. The team used an inkjet printer to place the dye mixtures in tiny spots on an epoxy surface, where they became covalently bound. Then, they used a fluorescence microscope to read the emission spectra of dye molecules at each spot and decode the message. The fluorescent data could be read 1,000 times without a significant loss in intensity. The researchers also demonstrated the technique’s ability to write and read an image of Faraday. The strategy has a read rate of 469 bits/s, which is the fastest reported for any molecular information storage method, the researchers say.
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In underwater acoustics, deep learning is gaining traction in improving sonar systems to detect ships and submarines in distress or in restricted waters. However, noise interference from the complex marine environment becomes a challenge when attempting to detect targeted ship-radiated sounds.
In the Journal of the Acoustical Society of America, published by the Acoustical Society of America through AIP Publishing, researchers in China and the United States explore an attention-based deep neural network (ABNN) to tackle this problem.
“We found the ABNN was highly accurate in target recognition, exceeding a conventional deep neural network, particularly when using limited single-target data to detect multiple targets,” co-author Qunyan Ren said.
Deep learning is a machine-learning method that uses artificial neural networks inspired by the human brain to recognize patterns. Each layer of artificial neurons, or nodes, learns a distinct set of features based on the information contained in the previous layer.
ABNN uses an attention module to mimic elements in the cognitive process that enable us to focus on the most important parts of an image, language, or other pattern and tune out the rest. This is accomplished by adding more weight to certain nodes to enhance specific pattern elements in the machine-learning process.
Incorporating an ABNN system in sonar equipment for targeted ship detection, the researchers tested two ships in a shallow, 135-square-mile area of the South China Sea. They compared their results with a typical deep neural network (DNN). Radar and other equipment were used to determine more than 17 interfering vessels in the experimental area.
They found the ABNN increases its predictions considerably as it gravitates toward the features closely correlated with the training goals. Detection becomes more pronounced as the network continually cycles through the entire training dataset, accentuating the weighted nodes and disregarding irrelevant information.
While the ABNN accuracy of detecting ships A and B separately was slightly higher than the DNN (98% and 97.4%, respectively), the ABNN accuracy of detecting both ships in the same vicinity was significantly higher (74% and 58.4%).
For multiple-target identification, a traditional ABNN model is generally trained using multiship data, but this can be a complicated and computationally costly process. The researchers trained their ABNN model to detect each target separately. The individual-target datasets then merge as the output layer of the network is extended.
“The need to detect multiple ships at one time is a common scenario, and our model significantly exceeds DNN in detecting two ships in the same vicinity,” Ren said. “Moreover, our ABNN focused on the inherent features of the two ships simultaneously.”
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Scientists from Nara Institute of Science and Technology (NAIST) used the mathematical method called automatic differentiation to find the optimal fit of experimental data up to four times faster. This research can be applied to multivariable models of electronic devices, which may allow them to be designed with increased performance while consuming less power.
Wide bandgap devices, such as silicon carbide (SiC) metal-oxide semiconductor field-effect transistors (MOSFET), are a critical element for making converters faster and more sustainable. This is because of their larger switching frequencies with smaller energy losses under a wide range of temperatures when compared with conventional silicon-based devices. However, calculating the parameters that determine how the electrical current in a MOSFET responds as a function of the applied voltage remains difficult in a circuit simulation. A better approach for fitting experimental data to extract the important parameters would provide chip manufacturers the ability to design more efficient power converters.
Now, a team of scientists led by NAIST has successfully used the mathematical method called automatic differentiation (AD) to significantly accelerate these calculations. While AD has been used extensively when training artificial neural networks, the current project extends its application into the area of model parameter extraction. For problems involving many variables, the task of minimizing the error is often accomplished by a process of “gradient descent,” in which an initial guess is repeatedly refined by making small adjustments in the direction that reduces the error the quickest. This is where AD can be much faster than previous alternatives, such as symbolic or numerical differentiation, at finding direction with the steepest “slope.” AD breaks down the problem into combinations of basic arithmetic operations, each of which only needs to be done once. “With AD, the partial derivatives with respect to each of the input parameters are obtained simultaneously, so there is no need to repeat the model evaluation for each parameter,” first author Michihiro Shintani says. By contrast, symbolic differentiation provides exact solutions, but uses a large amount of time and computational resources as the problem becomes more complex.
To show the effectiveness of this method, the team applied it to experimental data collected from a commercially available SiC MOSFET. “Our approach reduced the computation time by 3.5× in comparison to the conventional numerical-differentiation method, which is close to the maximum improvement theoretically possible,” Shintani says. This method can be readily applied in many other areas of research involving multiple variables, since it preserves the physical meanings of the model parameters. The application of AD for the enhanced extraction of model parameters will support new advances in MOSFET development and improved manufacturing yields.
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Stress enhances the properties of a promising material for future technologies.
UNSW researchers find a new exotic state of one of the most promising multiferroic materials, with exciting implications for future technologies using these enhanced properties.
Combining a careful balance of thin-film strain, distortion, and thickness, the team has stabilised a new intermediate phase in one of the few known room-temperature multiferroic materials.
The theoretical and experimental US-Australian study shows that this new phase has an electromechanical figure of merit over double its usual value, and that we can even transform between this intermediate phase to other phases easily using an electric field.
As well as providing a valuable new technique to the toolkit of all international material scientists working with multiferroics and epitaxy, the results finally shed light on how epitaxial techniques can be used to enhance functional response of materials for future application in next -generation devices.
STRESS CHANGES EVERYTHING
If 2020-21 has taught us anything, it’s that stress changes everything. Even the most ‘together’ person can struggle and change given enough stress in their life. More

In high-performance CPUs used in large servers and power semiconductors used in inverters for hybrid electric vehicles (HEVs), as the integration density rises and the higher the power consumption becomes, the semiconductor package is also becoming smaller. Therefore, the power consumption per area of the semiconductor increases. As a result, the heat generation density increases, and the current situation is that the heat removal limit from the device is approaching.
JST commissioned the company-led phase NexTEP-B type development project of Adaptable and Seamless Technology Transfer Program through Target-driven R&D (A-STEP*) “high-performance in-vehicle cooler with spontaneous cooling promotion mechanism” to Lotus Thermal Solutions Co., Ltd. to proceed the practical development based on the research results of Professor Kazuhisa Yuki et al. of Sanyo-Onoda City University.
In the research by Professor Yuki et al., they realized the structure hard for occurring the film boiling by engraving about 1 mm wide grooves at a regular interval on a heat conductor, such a copper, which contacts with a heating element, and combining it with a lotus metal. Lotus Thermal Solution has established a method that determines the appropriate groove cross-sectional areas and pore diameters according to the refrigerant, and developed a highly efficient boiling immersion cooler (1) using lotus metals (2). Silicon carbide (SiC), which is expected as a next-generation power semiconductor, has a heat generation density of 300 to 500 watts per square centimeter (W/cm2). Thus, to use SiC in devices, a cooler with a critical heat flux (CHF) (3) larger than this heat generation density is required. In this development, we succeeded in increasing CHF from about 200 W/cm2 of the conventional cooler to 530 W/cm2 or more by using the boiling promotion technology using lotus metals.
The boiling immersion cooler prototyped in this development has the capability to cool the inverter with Si semiconductors and SiC semiconductors and is expected as a technology to solve the heat concentration problem of in-vehicle power semiconductors with increasingly high heat generation density. Furthermore, this technology is considered as a highly efficient cooling technology for CPUs for conventional workstations and large-scale servers.
(1) Boiling immersion cooling
This is a method that a liquid refrigerant is boiled with the heat of a heat source and then cooled. The conventional cooling method uses the temperature difference for heat transfer from the heat source to the refrigerant such as water or air, and it cools by natural convection or forced convection. However boiling cooling can utilize latent heat of evaporation (heat of vaporization) when vaporizing; therefore, it is said to have cooling capacity several times that of the conventional method.
(2) Lotus metal
This is a lotus root-like porous metal in which many elongated pores are arranged in the same direction. It has cooling characteristics owing to a refrigerant flowing through the pores. When a molten metal containing hydrogen is solidified, pores are formed by hydrogen that cannot completely be dissolved in the molten metal. Utilizing this phenomenon, lotus metals can be produced at a low cost.
(3) Critical Heat Flux CHF
When the heat load increases in boiling immersion cooling, the nucleate boiling with a good heat transfer efficiency cannot be maintained at certain point, and suddenly transitions to film boiling where the heating surface is covered with a vapor film. The heat flux (heat flow per unit area, unit [W/cm2]) at the transition point is called the critical heat flux.
*A-STEP is a technology transfer support program whose aim is to put the research results by public research institutes into practical applications as important technology in the national economy, and thus to give some of their profit back to society.
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A novel electronic component from TU Wien (Vienna) could be an important key to the era of quantum information technology: Using a tailored manufacturing process, pure germanium is bonded with aluminium in a way that atomically sharp interfaces are created. This results in a so-called monolithic metal-semiconductor-metal heterostructure.
This structure shows unique effects that are particularly evident at low temperatures. The aluminium becomes superconducting — but not only that, this property is also transferred to the adjacent germanium semiconductor and can be specifically controlled with electric fields. This makes it excellently suited for complex applications in quantum technology, such as processing quantum bits. A particular advantage is that using this approach, it is not necessary to develop completely new fabrication technologies. Instead, well established semiconductor fabrication techniques can be used to enable germanium-based quantum electronics. The results have now been published in the journal Advanced Materials.
Germanium: difficult to form high-quality contacts
“Germanium is a material which will definitely play an important role in semiconductor technology for the development of faster and more energy-efficient components,” says Dr. Masiar Sistani from the Institute for Solid State Electronics at TU Wien. However, if it is used to produce components on a nanometre scale, major problems arise: the material makes it extremely difficult to produce high-quality electrical contacts. This is related to the high impact of even smallest impurities at the contact points that significantly alter the electrical properties. “We have therefore set ourselves the task of developing a new manufacturing method that enables reliable and reproducible contact properties,” says Masiar Sistani.
Diffusing atoms
The key is temperature: when nanometre-structured germanium and aluminium are brought into contact and heated, the atoms of both materials begin to diffuse into the neighbouring material — but to very different extents: the germanium atoms move rapidly into the aluminium, whereas aluminium hardly diffuses into the germanium at all. “Thus, if you connect two aluminium contacts to a thin germanium nanowire and raise the temperature to 350 degrees Celsius, the germanium atoms diffuse off the edge of the nanowire. This creates empty spaces into which the aluminium can then easily penetrate,” explains Masiar Sistani. “In the end, only a few nanometre area in the middle of the nanowire consists of germanium, the rest has been filled up by aluminium.”
Normally, aluminium is made up of tiny crystal grains, but this novel fabrication method forms a perfect single crystal in which the aluminium atoms are arranged in a uniform pattern. As can be seen under the transmission electron microscope, a perfectly clean and atomically sharp transition is formed between germanium and aluminium, with no disordered region in between. In contrast to conventional methods where electrical contacts are applied to a semiconductor, for example by evaporating a metal, no oxides can form at the boundary layer.
Quantum transport in Grenoble
In order to take a closer look at the properties of this monolithic metal-semiconductor heterostructure of germanium and aluminium at low temperature, we collaborated with Dr. Olivier Buisson and Dr. Cécile Naud from the quantum electronics circuits group at Néel Institute — CNRS-UGA in Grenoble. It turned out that the novel structure indeed has quite remarkable properties: “Not only were we able to demonstrate superconductivity in pure, undoped germanium for the first time, we were also able to show that this structure can be switched between quite different operating states using electric fields. Such a germanium quantum dot device can not only be superconducting but also completely insulating, or it can behave like a Josephson transistor, an important basic element of quantum electronic circuits,” explains Masiar Sistani.
This new heterostructure combines a whole range of advantages: The structure has excellent physical properties needed for quantum technologies, such as high carrier mobility and excellent manipulability with electric fields, and it has the additional advantage of fitting well with already established microelectronics technologies: Germanium is already used in current chip architectures and the temperatures required for heterostructure formation are compatible with well-established semiconductor processing schemes. The novel structures not only have theoretically interesting quantum properties, but also opens up a technologically very realistic possibility of enabling further novel and energy-saving devices.
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Materials consisting of a few atomic layers display properties determined by quantum physics. In a stack of such layers, vibrations of the atoms can be triggered by infrared light. New experimental and theoretical work shows that atomic vibrations within the layers of hexagonal boron nitride, the so-called transverse optical phonons, couple directly to motions of the layers against each other. For a period of some 20 ps, the coupling results in a frequency down-shift of the optical phonons and their optical resonance. This behavior is a genuine property of the quantum material and of interest for applications in high-frequency optoelectronics.
Hexagonal boron nitride consist of layers in which covalently bonded boron and nitrogen atoms form a regular array of six-rings. Neighboring layers are coupled via the much weaker van der Waals interaction. Vibrations of boron and nitrogen atoms in the layer, the so-called transverse optical (TO) phonons, show an oscillation frequency on the order of 40 Terahertz (THz, 4×1013 vibrations per second) which is ten to hundred times higher than that of shear and breathing motions of the layers relative to each other. So far, there was nearly no insight into the lifetime of such motions after optical excitation and into their coupling.
An international collaboration of scientists from Berlin, Montpellier, Nantes, Paris and Ithaca (USA) now presents detailed experimental and theoretical results on ultrafast dynamics of coupled phonons in few-layer hexagonal boron nitride. Transverse optical (TO) phonons in a stack of 8 to 9 boron nitride layers display a lifetime of 1.2 ps (1 ps = 10-12 s), while shear and breathing modes show a decay time of 22 ps. Such lifetimes were directly measured in femtosecond pump-probe experiments and are in very good agreement with values derived from a theoretical analysis of the phonon decay channels.
Excitations of shear and breathing modes induce a characteristic spectral down-shift of the TO phonon resonance in the optical spectra . Theoretical calculations give the coupling energy between the different modes of the layer stack and show that the corresponding coupling is negligibly small in a bulk boron nitride crystal consisting of many layers. Thus, the observed coupled vibrational dynamics represent a genuine property of the quantum material.
The spectral shift of the TO phonon resonance in the optical spectra is a nonlinear optical effect which can be induced by light of moderate power. This is of interest for applications in optoelectronics and holds potential for optical modulators and switches in the giga- to terahertz frequency range.
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