Computers Math

The Human Cell Atlas is the world’s largest, growing single-cell reference atlas. It contains references of millions of cells across tissues, organs and developmental stages. These references help physicians to understand the influences of aging, environment and disease on a cell — and ultimately diagnose and treat patients better. Yet, reference atlases do not come without challenges. Single-cell datasets may contain measurement errors (batch effect), the global availability of computational resources is limited and the sharing of raw data is often legally restricted.
Researchers from Helmholtz Zentrum München and the Technical University of Munich (TUM) developed a novel algorithm called “scArches,” short for single-cell architecture surgery. The biggest advantage: “Instead of sharing raw data between clinics or research centers, the algorithm uses transfer learning to compare new datasets from single-cell genomics with existing references and thus preserves privacy and anonymity. This also makes annotating and interpreting of new data sets very easy and democratizes the usage of single-cell reference atlases dramatically,” says Mohammad Lotfollahi, the leading scientist of the algorithm.
Example COVID-19
The researchers applied scArches to study COVID-19 in several lung bronchial samples. They compared the cells of COVID-19 patients to healthy references using single-cell transcriptomics. The algorithm was able to separate diseased cells from the references and thus enabled the user to pinpoint the cells in need for treatment, for both mild and severe COVID-19 cases. Biological variation between patients did not affect the quality of the mapping process.
Fabian Theis: “Our vision is that in the future we will use cell references as easily as we nowadays do for genome references. In other word, if you want to bake a cake, you usually do not want to try coming up with your own recipe — instead you just look one up in a cookbook. With scArches, we formalize and simplify this lookup process.”
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Increasingly, movies featuring humanoid robots, like Terminator or Ex Machina, are showing the titular “robot” akin to humans not only in intelligence but also appearance. What if Terminator-esque robots became the norm, making it difficult for us to tell them apart from actual human beings?
This is the premise of a new study published in PLOS ONE, which evaluated how human beings respond to images of people with the same face. It is not too far-fetched to imagine a future where human-like androids are mass-produced and are indistinguishable from flesh-and-blood human beings. Robotics and artificial intelligence are advancing at an unprecedented rate, with very closely human-like robots and CG characters, such as Geminoid, Saya, and Sophia already having been produced. Developers are optimistic they will one day create robots that surpass the uncanny valley — a well-known phenomenon where humanoids elicit unpleasant and negative emotions in viewers when their appearance becomes similar to that of humans.
In such a future, how would we react?
A team of researchers from Kyushu University, Ritsumeikan University, and Kansai University, collaboratively conducted a series of six experiments involving different batches of hundreds of people to try and find that answer.
The first experiment involved rating the subjective eeriness, emotional valence, and realism of a photoshopped photograph of six human subjects with the exact same face (clone image), six people with different faces (non-clone image), and one person (single image). The second experiment comprised rating another set of clone images and non-clone images, while the third experiment consisted of rating clone and non-clone images of dogs. The fourth experiment had two parts: rating clone images of two sets of twins and then rating clone faces of twins, triplets, quadruplets, and quintuplets. The fifth experiment involved clone images of Japanese animation and cartoon characters. And the sixth and final experiment involved evaluating the subjective eeriness and realism of a different set of clone and non-clone images while also answering the Disgust Scale Revised to analyze disgust sensitivity.
The results were striking. Participants from the first study rated individuals with clone faces as eerier and more improbable than those with different faces and a single person’s face.
The researchers termed this negative emotional response as the clone devaluation effect.
“The clone devaluation effect was stronger when the number of clone faces increased from two to four,” says lead author Dr. Fumiya Yonemitsu from Graduate School of Human-Environment Studies at Kyushu University, who is also a Research Fellow of Japan Society for the Promotion of Science. “This effect did not occur when each clone face was indistinguishable, like animal faces in experiment three involving dogs.”
According to him, “We also noticed that the duplication of identity, that is the personality and mind unique to a person, rather than their facial features, has an important role in this effect. Clone faces with the duplication of identity were eerier, as the fourth experiment showed. The clone devaluation effect became weaker when clone faces existed in the lower reality of the context, such as in the fifth experiment. Furthermore, the eeriness of clone faces stemming from improbability could be positively predicted by disgust, in particular animal-reminder disgust, as noticed in the sixth experiment. Taken together, these results suggest that clone faces induce eeriness and that the clone devaluation effect is related to realism and disgust reaction.”
These results show that human faces provide important information for identifying individuals because human beings have a one-to-one correspondence between face and identity. Clone faces violate this principle, which may make humans misjudge the identity of people with clone faces as being the same.
So, what does this mean for a future in which humanoids are inevitable? According to the researchers, we need to think critically about introducing new technology in robotics or human cloning because of the potential for unpleasant psychological reactions other than the uncanny valley phenomenon.
“Our study clearly shows that uncomfortable situations could occur due to the rapid development of technology. But we believe our findings can play an important role in the smooth acceptance of new technologies and enhance people’s enjoyment of their benefits”, observes co-author Dr. Akihiko Gobara, Senior Researcher from BKC Research Organization of Social Science at Ritsumeikan University.
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Researchers from the University of Kent, the Research Institute for Environment Treatment and Vita-Market Ltd have discovered a universal mathematical formula that can describe any bird’s egg existing in nature, a feat which has been unsuccessful until now.
Egg-shape has long attracted the attention of mathematicians, engineers, and biologists from an analytical point of view. The shape has been highly regarded for its evolution as large enough to incubate an embryo, small enough to exit the body in the most efficient way, not roll away once laid, is structurally sound enough to bear weight and be the beginning of life for so many species. The egg has been called the “perfect shape.”
Analysis of all egg shapes used four geometric figures: sphere, ellipsoid, ovoid, and pyriform (conical or pear-shaped), with a mathematical formula for the pyriform yet to be derived.
To rectify this, researchers introduced an additional function into the ovoid formula, developing a mathematical model to fit a completely novel geometric shape characterized as the last stage in the evolution of the sphere-ellipsoid, which it is applicable to any egg geometry.
This new universal mathematical formula for egg shape is based on four parameters: egg length, maximum breadth, shift of the vertical axis, and the diameter at one quarter of the egg length.
This long sought-for universal formula is a significant step in understanding not only the egg shape itself, but also how and why it evolved, thus making widespread biological and technological applications possible.
Mathematical descriptions of all basic egg shapes have already found applications in food research, mechanical engineering, agriculture, biosciences, architecture and aeronautics. As an example, this formula can be applied to engineering construction of thin walled vessels of an egg shape, which should be stronger than typical spherical ones.
This new formula is an important breakthrough with multiple applications including: Competent scientific description of a biological object. Now that an egg can be described via mathematical formula, work in fields of biological systematics, optimization of technological parameters, egg incubation and selection of poultry will be greatly simplified. Accurate and simple determination of the physical characteristics of a biological object. The external properties of an egg are vital for researchers and engineers who develop technologies for incubating, processing, storing and sorting eggs. There is a need for a simple identification process using egg volume, surface area, radius of curvature and other indicators for describing the contours of the egg, which this formula provides. Future biology-inspired engineering. The egg is a natural biological system studied to design engineering systems and state-of-the-art technologies. The egg-shaped geometric figure is adopted in architecture, such as London City Hall’s roof and the Gherkin, and construction as it can withstand maximum loads with a minimum consumption of materials, to which this formula can now be easily applied.Darren Griffin, Professor of Genetics in the University of Kent and PI on the research, said: “Biological evolutionary processes such as egg formation must be investigated for mathematical description as a basis for research in evolutionary biology, as demonstrated with this formula. This universal formula can be applied across fundamental disciplines, especially the food and poultry industry, and will serve as an impetus for further investigations inspired by the egg as a research object.”
Dr Michael Romanov, Visiting Researcher at the University of Kent, said: “This mathematical equation underlines our understanding and appreciation of a certain philosophical harmony between mathematics and biology, and from those two a way towards further comprehension of our universe, understood neatly in the shape of an egg.”
Dr Valeriy Narushin, former visiting researcher at the University of Kent, said: “We look forward to seeing the application of this formula across industries, from art to technology, architecture to agriculture. This breakthrough reveals why such collaborative research from separate disciplines is essential.”
The paper “Egg and math: introducing a universal formula for egg shape” is published in Annals of the New York Academy of Sciences (Valeriy G. Narushin, Research Institute for Environment Treatment and Vita-Market Ltd, Ukraine; Dr Michael N. Romanov, University of Kent; Professor Darren K. Griffin, University of Kent).
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From small committees to national elections, group decision-making can be complicated — and it may not always settle on the best choice. That’s partly because some members of the group do research on their own, and others take their cues from the people around them.
That distinction is readily observed around election time. “Many voters couldn’t tell you the policy platforms for the candidates they’re voting for,” says applied mathematician Vicky Chuqiao Yang at the Santa Fe Institute. “Many individuals are uninformed, and they’re most likely to rely on information they get from others.”
Social scientists have long sought ways to study the phenomenon of group decision-making, but that’s a tricky undertaking. Researchers in a range of disciplines have tried to tackle the problem, with parallel efforts often leading to conflicting conclusions. Most existing models examine the effect of a single variable, which means they don’t capture the whole picture.
“The outcome of collective decision making is the result of complex interactions of many variables,” says Yang, “and those interactions are rarely taken into account” in previous work.
To overcome that challenge, Yang recently led the development of a mathematical framework that captures the influence of multiple interactions among members of a group. “You can plug in multiple effects and see their behavior and how they manifest in the group at the same time,” she explains.
Those effects include the influence of social learners. The model predicted, for example, that decision-making groups have a critical threshold of people who get their information from others. Below that threshold, the group chooses the high-quality outcome. Above it, the group can end up choosing the better or worse option.
The model also predicted a significant role for “committed minorities,” or people who refuse to change their minds, no matter the evidence. These committed minorities can be bolstered, Yang says, by social learners, though every group is different.
The mathematical model is both simple and general, and can accurately reflect the multitude of moving parts within a system. Yang’s collaborators include psychologist and SFI Professor Mirta Galesic, economist Ani Harutyunyan at the Sunwater Institute, and Harvey McGuinness, an undergraduate at Johns Hopkins University and former student researcher at SFI. (The whole project began, said Yang, with a question from McGuinness.) The group reported on the framework in a paper published in Proceedings of the National Academy of Sciences.
Yang says she hopes the model will help bring together parallel work from different disciplines. These disciplines have found separate effects at work in collective decision-making, “but we don’t yet have a holistic understanding that gives a recipe for good collective decision making,” she said. “Our work brings us one step closer to it.”
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Ultrafast electron microscope opens up new avenues for the development of sensors and quantum devices.
Everyone who has ever been to the Grand Canyon can relate to having strong feelings from being close to one of nature’s edges. Similarly, scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have discovered that nanoparticles of gold act unusually when close to the edge of a one-atom thick sheet of carbon, called graphene. This could have big implications for the development of new sensors and quantum devices.
This discovery was made possible with a newly established ultrafast electron microscope (UEM) at Argonne’s Center for Nanoscale Materials (CNM), a DOE Office of Science User Facility. The UEM enables the visualization and investigation of phenomena at the nanoscale and on time frames of less than a trillionth of a second. This discovery could make a splash in the growing field of plasmonics, which involves light striking a material surface and triggering waves of electrons, known as plasmonic fields.
“With ultrafast capabilities, there’s no telling what we might see as we tweak different materials and their properties.” — Haihua Liu, Argonne nanoscientist
For years, scientists have been pursuing development of plasmonic devices with a wide range of applications — from quantum information processing to optoelectronics (which combine light-based and electronic components) to sensors for biological and medical purposes. To do so, they couple two-dimensional materials with atomic-level thickness, such as graphene, with nanosized metal particles. Understanding the combined plasmonic behavior of these two different types of materials requires understanding exactly how they are coupled.
In a recent study from Argonne, researchers used ultrafast electron microscopy to look directly at the coupling between gold nanoparticles and graphene. More

“Skin electronics” are thin flexible electronics that could be mounted onto the skin. While it may sound like something out of science fiction, it is anticipated that soon such devices can serve as next-generation devices with a wide range of applications such as health monitoring, health diagnosis, virtual reality, and human-machine interface.
As it is expected, creating such devices requires components that are soft and stretchable to be mechanically compatible with the human skin. One of the vital components of skin electronics is an intrinsically stretchable conductor that transmits electrical signals between devices. For reliable operation and high-quality performance, a stretchable conductor which features ultrathin thickness, metal-like conductivity, high stretchability, and ease of patternability is required. Despite extensive research, it was not yet possible to develop a material that possesses all of these properties simultaneously, due to the fact that they often have trade-offs between one another.
Led by professor HYEON Taeghwan and KIM Dae-Hyeong, researchers at the Center for Nanoparticle Research within the Institute for Basic Science (IBS) in Seoul, South Korea unveiled a new method to fabricate a composite material in a form of nanomembrane, which comes with all of the above-mentioned properties. The new composite material consists of metal nanowires that are tightly packed in a monolayer within ultrathin rubber film.
This novel material was made using a process that the team developed called a “float assembly method.” The float assembly takes advantage of the Marangoni effect, which occurs in two liquid phases with different surface tensions. When there is a gradient in surface tension, a Marangoni flow is generated away from the region with lower surface tension towards the region with higher surface tension. This means that dropping a liquid with lower surface tension on the water surface lowers the surface tension locally, and the resulting Marangoni flow causes the dropped liquid to spread thinly across the surface of the water.
The nanomembrane is created using a float assembly method which consists of a three-step process. The first step involves dropping a composite solution, which is a mixture of metal nanowires, rubber dissolved in toluene, and ethanol, on the surface of the water. The toluene-rubber phase remains above the water due to its hydrophobic property, while the nanowires end up on the interface between the water and toluene phases. The ethanol within the solution mixes with the water to lower the local surface tension, which generates Marangoni flow that propagates outward and prevents the aggregation of the nanowires. This assembles the nanomaterials into a monolayer at the interface between water and a very thin rubber/solvent film. In the second step, the surfactant is dropped to generate a second wave of Marangoni flow which tightly compacts the nanowires. Finally, in the third step, the toluene is evaporated and a nanomembrane with a unique structure in which a highly compacted monolayer of nanowires is partially embedded in an ultrathin rubber film is obtained.
Its unique structure allows efficient strain distribution in ultrathin rubber film, leading to excellent physical properties, such as a stretchability of over 1,000%, and a thickness of only 250 nm. The structure also allows cold welding and bi-layer stacking of the nanomembrane onto each other, which leads to a metal-like conductivity over 100,000 S/cm. Furthermore, the researchers demonstrated that the nanomembrane can be patterned using photolithography, which is a key technology that is widely used for manufacturing commercial semiconductor devices and advanced electronics. Therefore, it is expected that the nanomembrane can serve as a new platform material for skin electronics.
The implications of this study may go well beyond the development of skin electronics. While this study showcased a composite material consisting of silver nanowires within styrene-ethylene-butylene-styrene (SEBS) rubber, it is also possible to use the float assembly method on various nanomaterials such as magnetic nanomaterials and semiconducting nanomaterials, as well as other types of elastomers such as TPU and SIS. Therefore, it is expected that the float assembly can open new research fields involving various types of nanomembranes with different functions.
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Scientists from the Division of Sustainable Energy and Environmental Engineering at Osaka University used an established machine learning technique called generative adversarial networks to digitally remove clouds from aerial images. By using the resulting data as textures for 3D models, more accurate datasets of building image masks can be automatically generated. When setting two artificial intelligence networks against each other, the team was able to improve the data quality without the need for previously labeled images. This work may help automate computer vision jobs critical to civil engineering.
Machine learning is a powerful method for accomplishing artificial intelligence tasks, such as filling in missing information. One popular application is repairing images that are obscured, for example, when aerial images of buildings are blocked by clouds. While this can be done by hand, it is very time consuming, and even the machine learning algorithms that are currently available require many training images in order to work. Thus, improving the representation of buildings in virtual 3D models using aerial photographs requires additional steps.
Now, researchers at Osaka University have improved the accuracy of automatically generated datasets by applying the existing machine learning method called generative adversarial networks (GANs). The idea of GANs is to pit two different algorithms against each other. One is the “generative network,” that proposes reconstructed images without clouds. Competing against it is the “discriminative network,” that uses a convolutional neural network to attempt to tell the difference between the digitally repaired pictures and actual images without clouds. Over time, both networks get increasingly better at their respective jobs, leading to highly realistic images with the clouds digitally erased. “By training the generative network to ‘fool’ the discriminative network into thinking an image is real, we obtain reconstructed images that are more self-consistent,” first author Kazunosuke Ikeno explains.
The team used 3D virtual models with photographs from an open-source dataset as input. This allowed for the automatic generation of digital “masks” that overlaid reconstructed buildings over the clouds. “This method makes it possible to detect buildings in areas without labeled training data,” senior author Tomohiro Fukuda says. The trained model could detect buildings with an “intersection over union” value of 0.651, which measures how accurately the reconstructed area corresponds to the actual area. This method can be extended to improving the quality of other datasets in which some areas are obscured, such as medical images.
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Researchers from the University of Technology Sydney (UTS) and TMOS, an Australian Research Council Centre of Excellence, have taken the fight to online hackers with a giant leap towards realizing affordable, accessible quantum communications, a technology that would effectively prevent the decryption of online activity. Everything from private social media messaging to banking could become more secure due to new technology created with a humble piece of adhesive tape.
Quantum communication is still in its early development and is currently feasible only in very limited fields due to the costs associated with fabricating the required devices. The TMOS researches have developed new technology that integrates quantum sources and waveguides on chip in a manner that is both affordable and scalable, paving the way for future everyday use.
The development of fully functional quantum communication technologies has previously been hampered by the lack of reliable quantum light sources that can encode and transmit the information.
In a paper published today in ACS Photonics, the team describes a new platform to generate these quantum emitters based on hexagonal boron nitride, also known as white graphene. Where current quantum emitters are created using complex methods in expensive clean rooms, these new quantum emitters can be created using $20 worth of white graphene pressed on to a piece of adhesive tape.
These 2D materials can be pressed onto a sticky surface such as the adhesive tape and exfoliated, which is essentially peeling off the top layer to create a flex. Multiple layers of this flex can then be assembled in a Lego-like style, offering a new bottom up approach as a substitute for 3D systems.
TMOS Chief Investigator Igor Aharonovich said: “2D materials, like hexagonal boron nitride, are emerging materials for integrated quantum photonics, and are poised to impact the way we design and engineer future optical components for secured communication.”
In addition to this evolution in photon sources, the team has developed a high efficiency on-chip waveguide, a vital component for on-chip optical processing.
Lead author Chi Li said: “Low signal levels have been a significant barrier preventing quantum communications from evolving into practical, workable models. We hope that with this new development, quantum comms will become an everyday technology that improves people’s lives in new and exciting ways.”
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