Reconstruction of the repetitive antifreeze glycoprotein genomic loci in the cold-water gadids Boreogadus saida and Microgadus tomcod.

Abstract Antifreeze glycoproteins (AFGPs) are a novel evolutionary innovation in members of the northern cod fish family (Gadidae), crucial in preventing death from inoculative freezing by environmental ice in their frigid Arctic and sub-Arctic habitats. However, the genomic origin and molecular mechanism of evolution of this novel life-saving adaptive genetic trait remained to be definitively determined. To this end, we constructed large insert genomic DNA BAC (bacterial artificial chromosome) libraries for two AFGP-bearing gadids, the high-Arctic polar cod Boreogadus saida and the cold-temperate Atlantic tomcod Microgadus tomcod, to isolate and sequence their AFGP genomic regions for fine resolution evolutionary analyses. The BAC library construction encountered poor cloning efficiency initially, which we resolved by pretreating the agarose-embedded erythrocyte DNA with a cationic detergent, a method that may be of general use to BAC cloning for teleost species and/or where erythrocytes are the source of input DNA. The polar cod BAC library encompassed 92,160 clones with an average insert size of 94.7 kbp, and the Atlantic tomcod library contained 73,728 clones with an average insert size of 89.6 kbp. The genome sizes of B. saida and M. tomcod were estimated by cell flow cytometry to be 836 Mbp and 645 Mbp respectively, thus their BAC libraries have approximately 10- and 9.7-fold genome coverage respectively. The inclusiveness and depth of coverage were empirically confirmed by screening the libraries with three housekeeping genes. The BAC clones that mapped to the AFGP genomic loci of the two gadids were then isolated by screening the BAC libraries with gadid AFGP gene probes. Eight minimal tiling path (MTP) clones were identified for B. saida, sequenced, and assembled. The B. saida AFGP locus reconstruction produced both haplotypes, and the locus comprises three distinct AFGP gene clusters, containing a total of 16 AFGP genes and spanning a combined distance of 512 kbp. The M. tomcod AFGP locus is much smaller at approximately 80 kbp, and contains only three AFGP genes. Fluorescent in situ hybridization with an AFGP gene probe showed the AFGP locus in both species occupies a single chromosomal location. The large AFGP locus with its high gene dosage in B. saida is consistent with its chronically freezing high Arctic habitats, while the small gene family in M. tomcod correlates with its milder habitats in lower latitudes. The results from this study provided the data for fine resolution sequence analyses that would yield insight into the molecular mechanisms and history of gadid AFGP gene evolution driven by northern hemisphere glaciation

The interaction between microcapsules with different sizes and propagating cracks

The microcapsule-contained self-healing materials are appealing since they can heal the cracks automatically and be effective for a long time. Although many experiments have been carried out, the influence of the size of microcapsules on the self-healing effect is still not well investigated. This study uses the two-dimensional discrete element method (DEM) to investigate the interaction between one microcapsule and one microcrack. The influence of the size of microcapsules is considered. The potential healing time and the influence of the initial damage are studied. The results indicate that the coalescence crack is affected by the size of holes. The elastic modulus, the compressive strength and the coalescence stress decrease with the rising radius of holes. The initial damage in experiments should be greater than 95% of the compressive strength to enhance the self-healing effect. The large microcapsules require slight initial damage. Both a new type of displacement field near the crack and a new category of coalescence crack are observed. The influence of sizes of holes on the cracking behavior of concrete with a circular hole and a pre-existing crack is clarified

Extend Wave Function Collapse to Large-Scale Content Generation

Wave Function Collapse (WFC) is a widely used tile-based algorithm in procedural content generation, including textures, objects, and scenes. However, the current WFC algorithm and related research lack the ability to generate commercialized large-scale or infinite content due to constraint conflict and time complexity costs. This paper proposes a Nested WFC (N-WFC) algorithm framework to reduce time complexity. To avoid conflict and backtracking problems, we offer a complete and sub-complete tileset preparation strategy, which requires only a small number of tiles to generate aperiodic and deterministic infinite content. We also introduce the weight-brush system that combines N-WFC and sub-complete tileset, proving its suitability for game design. Our contribution addresses WFC's challenge in massive content generation and provides a theoretical basis for implementing concrete games.Comment: This paper is accepted by IEEE Conference on Games 2023 (nomination of the Best Paper Award

Influences of Divalent Ions in Natural Seawater/River Water on Nanofluidic Osmotic Energy Generation

Besides the dominant NaCl, natural seawater/river water contains trace multivalent ions, which can provide effective screening to surface charges. Here, in both negatively and positively charged nanopores, influences from divalent ions as counterions and coions have been investigated on the performance of osmotic energy conversion (OEC) under natural salt gradients. As counterions, trace Ca2+ ions can suppress the electric power and conversion efficiency significantly. The reduced OEC performance is due to the bivalence and low diffusion coefficient of Ca2 ions, instead of the uphill transport of divalent ions discovered in the previous work. Effectively screened charged surfaces by Ca2+ ions induce enhanced diffusion of Cl ions which simultaneously decreases the net ion penetration and ionic selectivity of the nanopore. While as coions, Ca2+ ions have weak effects on the OEC performance. The promotion from charged exterior surfaces on OEC processes for ultra-short nanopores is also studied, which effective region is ~200 nm in width beyond pore boundaries independent of the presence of Ca2+ ions. Our results shed light on the physical details of the nanofluidic OEC process under natural seawater/river water conditions, which can provide a useful guide for high-performance osmotic energy harvesting.Comment: 24 pages, 5 figure

Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient

Diffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond. They achieve state-of-the-art generation results in various generative tasks. A great diversity of conditioning inputs, such as text or bounding boxes, are accessible to control the generation. In this work, we propose a conditioning mechanism utilizing Gaussian mixture models (GMMs) as feature conditioning to guide the denoising process. Based on set theory, we provide a comprehensive theoretical analysis that shows that conditional latent distribution based on features and classes is significantly different, so that conditional latent distribution on features produces fewer defect generations than conditioning on classes. Two diffusion models conditioned on the Gaussian mixture model are trained separately for comparison. Experiments support our findings. A novel gradient function called the negative Gaussian mixture gradient (NGMG) is proposed and applied in diffusion model training with an additional classifier. Training stability has improved. We also theoretically prove that NGMG shares the same benefit as the Earth Mover distance (Wasserstein) as a more sensible cost function when learning distributions supported by low-dimensional manifolds

Three-dimensional topology optimization of auxetic metamaterial using isogeometric analysis and model order reduction

In this work, we present an efficiently computational approach for designing material micro-structures by means of topology optimization. The central idea relies on using the isogeometric analysis integrated with the parameterized level set function for numerical homogenization, sensitivity calculation and optimization of the effective elastic properties. Design variables, which are level set values associated with control points, are updated from the optimizer and represent the geometry of the unit cell. We further improve the computational efficiency in each iteration by employing reduced order modeling when solving linear systems of the equilibrium equations. We construct a reduced basis by reusing computed solutions from previous optimization steps, and a much smaller linear system of equations is solved on the reduced basis. Two- and three-dimensional numerical results show the effectiveness of the topology optimization algorithm coupled with the reduced basis approach in designing metamaterials

NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination

Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lighting models such as environment maps to represent non-distant illumination, or using a network to fit indirect light modeling without a solid basis, can lead to an undesirable decomposition between lighting and material. To address this, we propose a fully differentiable framework named neural ambient illumination (NeAI) that uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Together with integral lobe encoding for roughness-adaptive specular lobe and leveraging the pre-convoluted background for accurate decomposition, the proposed method represents a significant step towards integrating physically based rendering into the NeRF representation. The experiments demonstrate the superior performance of novel-view rendering compared to previous works, and the capability to re-render objects under arbitrary NeRF-style environments opens up exciting possibilities for bridging the gap between virtual and real-world scenes. The project and supplementary materials are available at https://yiyuzhuang.github.io/NeAI/.Comment: Project page: <a class="link-external link-https" href="https://yiyuzhuang.github.io/NeAI/" rel="external noopener nofollow">https://yiyuzhuang.github.io/NeAI/</a