Researchers have determined the structure of human leukotriene B4 receptor 1 (hBLT1), a protein involved in inflammation and disease. The analysis reveals how the receptor recognizes its binding partners and interacts with them, opening up avenues for designing better drugs.
Researchers at Skoltech developed a mathematical model for thermoplastic composite materials, reducing conservatism in strength calculations. The model allows for virtual testing of structures, minimizing manufacturing costs while ensuring safety and quality requirements.
Researchers have developed a neural network model called BiteNetPp to detect protein-peptide binding sites, enabling the design of peptide-based drugs. The model consistently outperforms existing methods and can analyze a single protein structure in under a second, making it suitable for large-scale studies.
Scientists at Skoltech and KTH Royal Institute of Technology predict the existence of antichiral ferromagnetism, a nontrivial property of some magnetic crystals. This phenomenon could lead to unique magnetic domains and skyrmions, distinct from conventional chiral textures.
Researchers at Skoltech have discovered structures called apical bulkheads in liver cells that are responsible for the narrow shape of bile canaliculi. The discovery reveals a key role for the Rab35 protein in regulating hepatocyte lumina formation and suggests potential avenues for medical applications in fatty liver disease and fibrosis
Researchers from Skoltech and their colleagues developed a neural network that can efficiently generate IUPAC names for organic compounds in accordance with the IUPAC nomenclature system. The network, trained using the Transformer architecture, achieved an accuracy of nearly 99%, outperforming traditional rule-based solutions.
A new neural model can detect 'inappropriateness' in chatbot messages, which are defined as content that may harm the reputation of the speaker's company without being toxic. The study provides a large collection of labeled datasets for further research and has been made publicly available.
Researchers from Skoltech have developed a new augmentation technique called MixChannel to help train computer vision algorithms with limited data. This approach outperformed state-of-the-art solutions in testing with three neural networks and can be combined with other methods for even more training data.
Skoltech researchers create a neural network that can guide the controlled deformation of semiconductor crystals, enabling superior properties for next-gen chips and solar cells. The approach combines various data sources and active learning to boost accuracy and convergence.
A Skoltech team developed a model to assess infection risks for supermarket customers. The composite model incorporates social forces and retail space layout, suggesting that social distancing is the primary factor in reducing infection rates. Filling stores to capacity has little economic sense during the pandemic.
Researchers identified genetic markers to control fatty acid content in sunflower oil, a key source of vegetable fats. The study used genomic selection to create new crop varieties with tailored properties suitable for different uses.
Researchers from Skoltech and their colleagues discovered promising compounds among pest control chemicals that inhibit the synthesis of hyaluronic acid, a key component in connective tissue. These compounds show potential as anti-fibrotic drugs for treating liver fibrosis.
Researchers have found 22 lipids linked to lower symptom improvement in people with schizophrenia during treatment. The study's findings suggest a complex interplay between metabolic abnormalities and psychiatric health, highlighting the potential of lipidomics as a promising field for new discoveries.
Researchers at Skoltech have successfully synthesized two new ternary hydrides, LaH10 and YH10, which are expected to exhibit high-temperature superconductivity. The study reveals that alloying is an effective strategy for stabilizing these otherwise unstable phases.
Researchers developed new chemistry for antisense oligonucleotides to treat SMA, a debilitating genetic disease. The compounds demonstrate reduced toxicity and potential for longer-lasting treatment with fewer injections needed.
A team of researchers used phylogenetics to investigate recurrent adaptations in bird mitochondria, finding that most convergence events can be explained by random coincidences rather than adaptation. The study confirms the scientific opinion that distant species choose different ways of similar trait evolution, but challenges the idea...
Researchers analyzed over 900 patents to identify key technological trends in the New Space economy. The study found that data is the most valuable asset in this ecosystem, with 62% of patents related to data products and services. Emerging topics include active constellation management and new satellite systems design.
Researchers at Skoltech have designed a labeling system for individual cells using polymer multilayer microcapsules that can be easily reproducible and non-toxic. The system allows for the tracking of single-cell behavior and migration with extreme precision, facilitating studies on cell movement and communication in populations.
Skoltech researchers have successfully created stable giant vortices in polariton condensates, addressing a known challenge in fluid dynamics. This breakthrough opens possibilities for uniquely structured coherent light sources and exploring many-body physics under extreme conditions.
Researchers from Skoltech conducted experiments measuring gas permeability under various conditions for ice-containing sediments mimicking permafrost. The study showed high probability of increasing permeability coupled with dissociation of gas hydrates in permafrost, leading to methane emissions into the atmosphere.
The study reveals an intricate connection between composition, light-induced lattice dynamics, and stability of the materials. It also found that energy transfer between vibrational modes in iodine-based perovskite nanocrystals is more pronounced than in bromine-based ones.
Researchers at Skoltech have identified a type of hydroxyl defect in LiFePO4, a widely used cathode material, which can degrade its performance. Studying these defects may lead to improving the manufacturing process and enhancing battery performance.
Researchers at Skolkovo Institute of Science and Technology used the Zhores supercomputer to develop a new method for generating gamma-ray combs, which are essential for nuclear spectroscopy and medicine. The method uses polarization-gated pulses to reduce ponderomotive spectral broadening, allowing for high-brightness gamma-ray sources.
Researchers from Skoltech developed a simple redox-active polyimide with promising features in various energy storage devices. The new material showed high specific capacities, relatively high redox potentials, and decent cycling stability.
Researchers have shown that CRISPR-Cas components are physically linked, enabling efficient updates of immune memory when infected by mutant viruses. This link allows bacteria to efficiently adapt and interfere with viral infections.
The Skoltech team created a genome-wide atlas of developmental alternative splicing changes in seven organs across six mammal species and chicken. Alternative splicing plays a crucial role in forming organs like the brain, heart, and testes, but is less significant for others like the liver and kidneys.
Researchers at Skoltech developed a new algorithm to identify over 200 previously unknown single-atom-alloy catalysts with improved stability and performance. The AI-powered approach uses machine learning models to extract key parameters from computational data, providing a recipe for finding the best SAACs for specific applications.
Researchers at Skoltech have developed an enriched approach to boost the capacity of next-generation metal-ion battery cathode materials, applicable to lithium-ion and alternative batteries. The scalable method uses reducing agents, which can be recycled after use, making it suitable for large-scale applications.
Researchers developed a method called RESONANCE to predict solar radio flux activity for 1-24 months, improving satellite orbit specification and re-entry predictions. The technique uses state-of-the-art physics-based models and advanced data assimilation methods.
Researchers at Skoltech have developed an AI-driven drone system to detect and track Sosnovsky's hogweed, a plant causing severe burns and ecosystem damage. The system uses real-time image segmentation on board drones to identify individual plants, increasing localization efficiency by multifold.
Researchers found that processing additives significantly impact the speed of polymerization in pultrusion, enabling faster production and improved efficiency. The study's findings have potential applications for enhancing profitability while maintaining quality in composite structures.
Researchers from Skoltech, Italy, discovered a new axis for preventing liver fibrosis by targeting the GILZ protein. The study used mice models and verified findings with human clinical data, suggesting that controlling the signaling pathway involving GILZ could lead to treating inflammatory liver diseases.
Researchers at Skoltech propose a new methodology for analyzing startup growth, leveraging Google Trends' big data. The approach enables building accurate and real-time data-driven growth paths for startups, offering an X-ray scan into technology-based ventures.
Researchers developed a mathematical model to predict ice accretion on ships, helping improve safety in the Arctic climate. The model simulates the freezing of water droplets moving in cold air and their interaction with ship surfaces.
A team of researchers has developed a method to create biodegradable polymer microcapsules with non-spherical shapes, which can enhance targeted drug delivery. The capsules are designed using soft lithography and have shown promising results in retaining hydrophilic molecules and being internalized by cells without causing toxic effects.
Researchers from Skoltech, University of Arizona, and Los Alamos National Laboratory developed an approach to quickly stabilize power grids after demand response perturbation. The study analyzes relaxation of energy consumption dynamics in thermostatically controlled loads, demonstrating super-relaxation and efficient load management.
Researchers from Skoltech identified the role of distant RNA regions in regulating gene expression, revealing their impact on splicing and gene regulation.
A neural network developed by Skoltech researchers improves credit scoring using transactional banking data, surpassing existing models. The EWS-GCN model processes large-scale temporal graphs directly and aggregates information to predict target client credit ratings.
Researchers developed a fluorescence-based sensor for continuous cortisol detection, overcoming existing laboratory methods. The new sensor showed low levels of detection and reversible response in vitro, paving the way for an implantable sensor for real-time monitoring.
Researchers at Skoltech's Space Center have developed an algorithm to measure the geomagnetic field using CubeSats in a tetrahedral orbital formation. The system uses Kriging interpolation to predict magnetic field values, enabling improved attitude control and station-keeping systems.
Researchers identified Mycobacterium tuberculosis' use of rubredoxin B to survive in iron-deficient conditions, helping the bacterium evade the immune system. The study provides new insights into the development of drug resistance and potential targets for therapeutic agents.
Researchers at Skoltech created a new electronegativity scale, improving Pauling's original scale with a formula that treats molecule stabilization as a multiplicative effect. The new scale works for both small and large differences in electronegativity, accurately predicting chemical bond energies and reactions.
Researchers at Skoltech have designed and synthesized new compounds that can serve as catholytes and anolytes for organic redox flow batteries, offering high cell voltage, solubility, and electrochemical properties. The materials have been tested for scalability and performance in large-scale energy storage applications.
The Skoltech team used ultra-high resolution mass spectrometry to study the molecular composition of carbonaceous chondrites from Murchison and Allende meteorites. They discovered a wide diversity of chemical compounds and unexpected similarities between meteorites from different groups.
Researchers from Skoltech have created a theoretical method to study electronic properties of 2D materials like silicene under high pressure. This approach could help create pressure sensors using these materials, which are promising candidates due to their unique properties.
Researchers developed a classification algorithm that uses orchard data to predict internal browning, surface cavities and fruit firmness in apples with high accuracy. The method shows promise for improving yield and reducing losses in the fruit industry.
Researchers have created an early prototype of a medical imaging system using neural networks to analyze near-infrared images of veins and project a venous pattern onto a patient's body. The system can detect vein contours accurately, fully automatically, and independently, reducing discomfort for patients with difficult access to veins.
The study proposes an alternative model where the effects of sex, genes, and environment are multiplied, not added, to account for complex interactions. This reveals consistent differences in average heights across countries and sexes.
Researchers at Skoltech have discovered new gene clusters that employ a 'Trojan horse' strategy to kill bacterial cells by preventing protein synthesis. These findings suggest the possibility of developing novel antimicrobial agents, which could be used to combat pathogenic bacteria.
A Skoltech researcher has discovered a new model of quantum computation, the variational model, which enables universal computation using limited control over a quantum simulator. This breakthrough bridges the gap between traditional quantum simulators and quantum computers.