Negative Self-Regulation of TLR9 Signaling by Its N-Terminal Proteolytic Cleavage Product

TLR signaling is essential to innate immunity against microbial invaders and must be tightly controlled. We have previously shown that TLR9 undergoes proteolytic cleavage processing by lysosomal proteases to generate two distinct fragments. The C-terminal cleavage product plays a critical role in activating TLR9 signaling; however, the precise role of the N-terminal fragment, which remains in lysosomes, in the TLR9 response is still unclear. In this article, we report that the N-terminal cleavage product negatively regulates TLR9 signaling. Notably, the N-terminal fragment promotes the aspartic protease-mediated degradation of the C-terminal fragment in endolysosomes. Furthermore, the N-terminal TLR9 fragment physically interacts with the C-terminal product, thereby inhibiting the formation of homodimers of the C-terminal fragment; this suggests that the monomeric C-terminal product is more susceptible to attack by aspartic proteases. Together, these results suggest that the N-terminal TLR9 proteolytic cleavage product is a negative self-regulator that prevents excessive TLR9 signaling activity.Korea (South). Ministry of Education, Science and Technology (MEST) (National Research Foundation of Korea. Grant 2011-0015372)Korea (South). Ministry of Education, Science and Technology (MEST) (National Research Foundation of Korea. Grant 2010-0009203)Korea. Ministry of Health and Welfare. National Research and Development Program for Cancer Contro

Learning Design Preferences through Design Feature Extraction and Weighted Ensemble

Design is a factor that plays an important role in consumer purchase decisions. As the need for understanding and predicting various preferences for each customer increases along with the importance of mass customization, predicting individual design preferences has become a critical factor in product development. However, current methods for predicting design preferences have some limitations. Product design involves a vast amount of high-dimensional information, and personal design preference is a complex and heterogeneous area of emotion unique to each individual. To address these challenges, we propose an approach that utilizes dimensionality reduction model to transform design samples into low-dimensional feature vectors, enabling us to extract the key representational features of each design. For preference prediction models using feature vectors, by referring to the design preference tendencies of others, we can predict the individual-level design preferences more accurately. Our proposed framework overcomes the limitations of traditional methods to determine design preferences, allowing us to accurately identify design features and predict individual preferences for specific products. Through this framework, we can improve the effectiveness of product development and create personalized product recommendations that cater to the unique needs of each consumer

Materials Discovery with Extreme Properties via Reinforcement Learning-Guided Combinatorial Chemistry

The goal of most materials discovery is to discover materials that are superior to those currently known. Fundamentally, this is close to extrapolation, which is a weak point for most machine learning models that learn the probability distribution of data. Herein, we develop reinforcement learning-guided combinatorial chemistry, which is a rule-based molecular designer driven by trained policy for selecting subsequent molecular fragments to get a target molecule. Since our model has the potential to generate all possible molecular structures that can be obtained from combinations of molecular fragments, unknown molecules with superior properties can be discovered. We theoretically and empirically demonstrate that our model is more suitable for discovering better compounds than probability distribution-learning models. In an experiment aimed at discovering molecules that hit seven extreme target properties, our model discovered 1,315 of all target-hitting molecules and 7,629 of five target-hitting molecules out of 100,000 trials, whereas the probability distribution-learning models failed. Moreover, it has been confirmed that every molecule generated under the binding rules of molecular fragments is 100% chemically valid. To illustrate the performance in actual problems, we also demonstrate that our models work well on two practical applications: discovering protein docking molecules and HIV inhibitors.Comment: 18 pages, 8 figure

LOHEN: Layer-wise Optimizations for Neural Network Inferences over Encrypted Data with High Performance or Accuracy

Fully Homomorphic Encryption (FHE) presents unique challenges in programming due to the contrast between traditional and FHE language paradigms. A key challenge is selecting ciphertext configurations (CCs) to achieve the desired level of security, performance, and accuracy simultaneously. Finding the design point satisfying the goal is often labor-intensive (probably impossible), for which reason previous works settle down to a reasonable CC that brings acceptable performance. When FHE is applied to neural networks (NNs), we have observed that the distinct layered architecture of NN models opens the door for a performance improvement by using layer-wise CCs, because a globally chosen CC may not be the best possible CC for every layer individually. This paper introduces LOHEN, a technique crafted to attain high performance of NN inference by enabling to use layer-wise CC efficiently. Empowered with a cryptographic gadget that allows switching between arbitrary CCs, LOHEN allocates layer-wise CCs for individual layers tailored to their structural properties, while minimizing the increased overhead incurred by CC switching with its capability to replace costly FHE operations. LOHEN can also be engineered to attain higher accuracy, yet deliver higher performance compared to state-of-the-art studies, by additionally adopting the multi-scheme techniques in a layer-wise manner. Moreover, the developers using LOHEN are given the capability of customizing the selection policy to adjust the desired levels of performance and accuracy, subject to their demands. Our evaluation shows that LOHEN improves the NN inference performance in both of these cases when compared to the state-of-the-art. When used to improve the CKKS-only inference, LOHEN improves the NN inference performance of various NNs 1.08--2.88x. LOHEN also improves the performance of mixed-scheme NN inference by 1.34--1.75x without accuracy loss. These two results along with other empirical analyses, advocate that LOHEN can widely help improve the performance of NN inference over FHE

Minimal grid diagrams of the prime knots with crossing number 14 and arc index 13

There are 46,972 prime knots with crossing number 14. Among them 19,536 are alternating and have arc index 16. Among the non-alternating knots, 17, 477, and 3,180 have arc index 10, 11, and 12, respectively. The remaining 23,762 have arc index 13 or 14. There are none with arc index smaller than 10 or larger than 14. We used the Dowker-Thistlethwaite code of the 23,762 knots provided by the program Knotscape to locate non-alternating edges in their diagrams. Our method requires at least six non-alternating edges to find arc presentations with 13 arcs. We obtained 8,027 knots having arc index 13. We show them by their minimal grid diagrams. The remaining 15,735 prime non-alternating 14 crossing knots have arc index 14 as determined by the lower bound obtained from the Kauffman polynomial.11 pages, 8 figures, 200 grid diagrams. Interested readers may typeset for 8,027 grid diagrams following authors\u27 instruction. arXiv admin note: substantial text overlap with arXiv:2402.0271

Ultrahigh strength, modulus, and conductivity of graphitic fibers by macromolecular coalescence

Theoretical considerations suggest that the strength of carbon nanotube (CNT) fibers be exceptional; however, their mechanical performance values are much lower than the theoretical values. To achieve macroscopic fibers with ultrahigh performance, we developed a method to form multidimensional nanostructures by coalescence of individual nanotubes. The highly aligned wet-spun fibers of single- or double-walled nanotube bundles were graphitized to induce nanotube collapse and multi-inner walled structures. These advanced nanostructures formed a network of interconnected, close-packed graphitic domains. Their near-perfect alignment and high longitudinal crystallinity that increased the shear strength between CNTs while retaining notable flexibility. The resulting fibers have an exceptional combination of high tensile strength (6.57 GPa), modulus (629 GPa), thermal conductivity (482 W/m·K), and electrical conductivity (2.2 MS/m), thereby overcoming the limits associated with conventional synthetic fibers

Increased viral load in patients infected with severe acute respiratory syndrome coronavirus 2 Omicron variant in the Republic of Korea

Objectives Coronavirus disease 2019 (COVID-19) has been declared a global pandemic owing to the rapid spread of the causative agent, severe acute respiratory syndrome coronavirus 2. Its Delta and Omicron variants are more transmissible and pathogenic than other variants. Some debates have emerged on the mechanism of variants of concern. In the COVID-19 wave that began in December 2021, the Omicron variant, first reported in South Africa, became identifiable in most cases globally. The aim of this study was to provide data to inform effective responses to the transmission of the Omicron variant. Methods The Delta variant and the spike protein D614G mutant were compared with the Omicron variant. Viral loads from 5 days after symptom onset were compared using epidemiological data collected at the time of diagnosis. Results The Omicron variant exhibited a higher viral load than other variants, resulting in greater transmissibility within 5 days of symptom onset. Conclusion Future research should focus on vaccine efficacy against the Omicron variant and compare trends in disease severity associated with its high viral load