Diffusion Model Research Articles

Numerical simulation of deuterium retention in tungsten under ELM-like conditions

Retention of hydrogen isotopes (HI) in plasma-facing components (PFCs) is a crucial process influencing the operation of a fusion device. The dynamics of HI retention is mainly determined by irradiation conditions of PFCs. These conditions can change due to the onset of fast transient events, such as edge-localised modes (ELMs). The development of ELMs results in repetitive short-term plasma bursts on PFCs, affecting the exposure regime. In this work, the effect of ELM-like loads on the fuel retention is numerically analysed using a one-dimensional diffusion model, implemented in the FESTIM code. As a representative simulation case, the deuterium (D) retention in tungsten (W) is considered with the geometry and inter-ELM exposure conditions relevant for large fusion devices. Temporal dependencies of heat and particle fluxes during intra-ELM stages are accounted for by applying the free-streaming model for an ELM filament transport. The simulations were conducted for three inter-ELM exposure regimes with various properties of transient loads, D trapping sites, and D recombination rates. Compared to the ELM-free irradiation, the onset of transients is shown to mainly reduce the D retention rate because of significant material heating induced by the arrival of energetic ELMy particles. This relative difference can decrease if the material is characterised by strong trapping sites or limited desorption from a surface. The findings also demonstrate that additional heating during transients provides conditions for a faster D migration into the bulk. In addition, we discuss the possibility of using ELM-average loads to obtain quick assessments of the D content. The approach is shown to be applicable for the case of small ELMs and is used to derive the analytical expressions for the D distribution in W within the steady-state approximation.

Achieving the United Nations sustainable development goals – innovation diffusion and business model innovations

Purpose The study aims to analyse the race towards green development and United Nations sustainable development goals (SDGs) in the cases of Huawei and Shell. Both companies are the leaders in their respective industries. Huawei is an example case study representing the information and communications technology (ICT) industry whereas Shell is an example case study representing the oil and gas industry. The research analyses of the races in achieving UN SDGs were undertaken based on the innovation diffusion framework with the use of machine learning algorithms trained to extract data on sustainability activities and initiatives. Design/methodology/approach The research analyses the two case studies of Huawei and Shell. The research was undertaken through the steps of training machine learning algorithms, industry benchmarking and evaluating the performance of the race. The analyses regarding the activities and initiatives of Huawei and Shell in contributing towards SDGs are based on the data in the past 10 years (Years 2010–2019) using machine learning to extract data on sustainability activities and initiatives. In the case of Huawei, 313 sustainability reports were fed to the unsupervised machine learning algorithms revealing 15,101 sustainability actions and initiatives related to UN SDGs in the ICT industry. In the case of Shell, 2,015 sustainability reports were fed to the unsupervised machine learning algorithms revealing 47,365 sustainability actions and initiatives related to UN SDGs in the oil and gas industry. Findings The analyses of findings revealed that Huawei and Shell performed very well in progressing towards the UN SDGs. Huawei had strong performance in the ICT industry with regard to SDGs No. 3, 4, 7, 8, 11, 12 and 16 while Shell had strong performance in the oil and gas industry with regard to SDGs No. 3, 4, 6, 7, 8, 12 and 16. Both companies had placed a focus on achieving SDG 12 responsible consumption and production, SDG 7 affordable and clean energy and SDG 4 quality education. The synthesised business model innovations of Huawei and Shell had shown their environmental, social and governance strategies – Huawei’s 2030 vision for green development and Shell’s 2050 vision for net zero emissions. Practical implications The five pillars of people, planet, prosperity, peace and partnership according to the UN 2030 agenda for sustainable development have shown the way a company operates to promote sustainable eco-systems. The extent to which both Huawei and Shell link corporate strategies to the UN SDGs has reflected their implementation progress. Furthermore, the business model innovations of Huawei and Shell provides a useful framework which can be applied to encourage other companies/organisations in various industries to undertake ESG activities in practice. Originality/value The main contribution of this research is the application of machine learning algorithms and the innovation diffusion model in analysing the SDGs performance. The study applies the innovation diffusion framework to explore strategic actions and initiatives of Huawei and Shell in transitioning towards sustainability. The use of machine learning algorithms has identified their sustainability approach in achieving the UN SDGs.

Enhancing Underwater Object Detection and Classification Using Advanced Imaging Techniques: A Novel Approach with Diffusion Models

Underwater object detection and classification pose significant challenges due to environmental factors such as water turbidity and variable lighting conditions. This research proposes a novel approach that integrates advanced imaging techniques with diffusion models to address these challenges effectively, aligning with Sustainable Development Goal (SDG) 14: Life Below Water. The methodology leverages the Convolutional Block Attention Module (CBAM), Modified Swin Transformer Block (MSTB), and Diffusion model to enhance the quality of underwater images, thereby improving the accuracy of object detection and classification tasks. This study utilizes the TrashCan dataset, comprising diverse underwater scenes and objects, to validate the proposed method’s efficacy. This study proposes an advanced imaging technique YOLO (you only look once) network (AIT-YOLOv7) for detecting objects in underwater images. This network uses a modified U-Net, which focuses on informative features using a convolutional block channel and spatial attentions for color correction and a modified swin transformer block for resolution enhancement. A novel diffusion model proposed using modified U-Net with ResNet understands the intricate structures in images with underwater objects, which enhances detection capabilities under challenging visual conditions. Thus, AIT-YOLOv7 net precisely detects and classifies different classes of objects present in this dataset. These improvements are crucial for applications in marine ecology research, underwater archeology, and environmental monitoring, where precise identification of marine debris, biological organisms, and submerged artifacts is essential. The proposed framework advances underwater imaging technology and supports the sustainable management of marine resources and conservation efforts. The experimental results demonstrate that state-of-the-art object detection methods, namely SSD, YOLOv3, YOLOv4, and YOLOTrashCan, achieve mean accuracies (mAP@0.5) of 57.19%, 58.12%, 59.78%, and 65.01%, respectively, whereas the proposed AIT-YOLOv7 net reaches a mean accuracy (mAP@0.5) of 81.4% on the TrashCan dataset, showing a 16.39% improvement. Due to this improvement in the accuracy and efficiency of underwater object detection, this research contributes to broader marine science and technology efforts, promoting the better understanding and management of aquatic ecosystems and helping to prevent and reduce the marine pollution, as emphasized in SDG 14.

BADM: Boundary-Assisted Diffusion Model for Skin Lesion Segmentation

Skin lesion segmentation is one of the basic tasks in the field of medical image analysis. Deep learning approaches have emerged as crucial tools for segmenting skin lesion images, and their accuracy in processing abnormalities influences the ultimate diagnostic outcomes. Skin lesion images suffer from problems such as variability in shape and size and fuzzy boundaries between lesions and background, making it difficult to accurately segment lesions from images. To address this challenge, we propose Boundary-assisted Diffusion Model for Skin Lesion Segmentation that incorporates boundary information into the denoising learning process of the diffusion model by introducing an additional boundary-aware task. We design a Boundary-Aware Fusion Model to capture boundary information from deep networks and incorporate boundary information into the learning process of the diffusion model. A Scale-Coordinated Interaction Model is proposed, which effectively transmits and enhances the information of different scale features, and improves the performance of the model when dealing with lesions of different sizes. We evaluate our approach on four different datasets and compare it with other methods. Encouragingly, our method achieves significant improvements in performance without losing novelty.

Research on new magnetic epoxy resin composite slurry materials and localization grouting diffusion mechanism

A significant number of steep cracks are frequently encountered in underground engineering, posing a threat to operation. The high-pressure grouting method is a commonly utilized repair technique. Nevertheless, conventional grout is prone to displacement due to its weight, making it challenging to ensure adequate filling of the cracks. Therefore, this study aims to develop a grouting material with targeted displacement and anchoring properties. Firstly, an optimal magnetic slurry composition was determined through an orthogonal test. Subsequently, XRD and SEM were used to analyze the impact of the magnetic field on the composition distribution, internal pore structure, and transient viscosity of the slurry. Afterwards, a model for localized grout diffusion under magnetic was established. The results show that the application of a magnetic field caused the slurry to compact due to magnetic forces, reducing its porosity. Moreover, the dynamic viscosity of the slurry increased exponentially with rising magnetic induction intensity. Notably, a 40.5% increase in the diffusion area was observed when the magnetic field intensity rose from 2500 to 4500 GS. The error between the measured and theoretical values of the magnetic slurry diffusion model was only 8.91%, indicating the model's accuracy in describing the slurry diffusion process under magnetic field influence.

Exploring Solitary Waves and Nonlinear Dynamics in the Fractional Chaffee–Infante Equation: A Study Beyond Conventional Diffusion Models

Exploring Solitary Waves and Nonlinear Dynamics in the Fractional Chaffee–Infante Equation: A Study Beyond Conventional Diffusion Models

Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence

Heavy and light particles are commonly found in many natural phenomena and industrial processes, such as suspensions of bubbles, dust, and droplets in incompressible turbulent flows. Based on a recent machine learning approach using a diffusion model that successfully generated single tracer trajectories in three-dimensional turbulence and passed most statistical benchmarks across time scales, we extend this model to include heavy and light particles. Given the particle type – tracer, light, or heavy – the model can generate synthetic, realistic trajectories with correct fat-tail distributions for acceleration, anomalous power laws, and scale dependent local slope properties. This work paves the way for future exploration of the use of diffusion models to produce high-quality synthetic datasets for different flow configurations, potentially allowing interpolation between different setups and adaptation to new conditions.

Creating High-quality 3D Content by Bridging the Gap Between Text-to-2D and Text-to-3D Generation

In recent times, automatic text-to-3D content creation has made significant progress, driven by the development of pretrained 2D diffusion models. Existing text-to-3D methods typically optimize the 3D representation to ensure that the rendered image aligns well with the given text, as evaluated by the pretrained 2D diffusion model. Nevertheless, a substantial domain gap exists between 2D images and 3D assets, primarily attributed to variations in camera-related attributes and the exclusive presence of foreground objects. Consequently, employing 2D diffusion models directly for optimizing 3D representations may lead to suboptimal outcomes. To address this issue, we present X-Dreamer, a novel approach for high-quality text-to-3D content creation that effectively bridges the gap between text-to-2D and text-to-3D synthesis. The key components of X-Dreamer are two innovative designs: Camera-Guided Low-Rank Adaptation (CG-LoRA) and Attention-Mask Alignment (AMA) Loss. CG-LoRA dynamically incorporates camera information into the pretrained diffusion models by employing camera-dependent generation for trainable parameters. This integration makes the 2D diffusion model camera-sensitive. AMA loss guides the attention map of the pretrained diffusion model using the binary mask of the 3D object, prioritizing the creation of the foreground object. This module ensures that the model focuses on generating accurate and detailed foreground objects. Extensive evaluations demonstrate the effectiveness of our proposed method compared to existing text-to-3D approaches. Our project webpage: https://anonymous-11111.github.io/ . Our code is available at https://github.com/xmu-xiaoma666/X-Dreamer .

Multiphase modeling of pressure-dependent hydrogen diffusivity in fractal porous structures of acrylonitrile butadiene rubber-carbon black composites with different fillers

The hydrogen diffusivities of acrylonitrile butadiene rubber (NBR) composites with different types and contents of carbon black (CB) fillers were investigated in the exposure pressure range of 0.5–10 MPa using a volumetric analysis system. These measured diffusivities exhibited distinct pressure-dependent behavior. The diffusivities of unfilled NBR and NBR composites containing medium-thermal CB filler decreased from 39.7 × 10−11 m2/s to 9.4 × 10−11 m2/s as the pressure increased. On the other hand, the pressure-dependent diffusivities of NBR composites containing high-abrasion furnace, fast-extrusion furnace and semireinforcing furnace CB fillers revealed left-biased unimodal shaped curves, with peak values ranging from 19.7 × 10−11 m2/s to 5.6 × 10−11 m2/s. To model this observed behavior, the diffusion resistance theory with a heterogeneous NBR–CB composite and a fractal porous structure for H2 was introduced. The theoretical parallel diffusion resistance model was found to coexist independently as the surface, Knudsen, and bulk diffusion phases. This theoretical multiphase modeling was applied to the measured diffusivities, determining diffusion resistance parameters for each phase. The obtained individual parametric characteristics for each diffusion were interpreted by considering the CB filler content and volume fraction of the filler. As a result, the diffusivities calculated by multiphase diffusion modeling were in quite agreement with the measured diffusivities for all investigated specimens, where the determined squared correlation coefficient (R2) by fitting process was in the range from 0.69 to 0.97.

Stochastic Resilience in Operations Management Using Ito Diffusion and Gamma Catastrophe Processes

This study introduces a novel Ito diffusion model for operations management, addressing the challenge of maintaining resilience in supply chains and production networks against unpredictable disruptions. The model incorporates a general catastrophe process with a low occurrence rate, using stochastic methods to represent disruption magnitudes as gamma distribution variables. It provides an analytical framework detailing the process's mean, variance, and sample path. Applying this model across various operational scenarios demonstrates its practical significance. By examining the impacts of disruptions on operational efficiency, the model offers insights into disruption dynamics, crucial for resilience planning and risk mitigation. The findings enhance logistics networks' resilience and efficiency, aiding decision-makers in navigating disruptions. This research presents a practical tool for decision-making in operations management and sets the stage for future research with complex variables and emerging technologies to enhance predictive strength in a dynamic environment.

Explicit-implicit priori knowledge-based diffusion model for generative medical image segmentation

The diffusion probabilistic model (DPM) has achieved unparalleled results in current image generation tasks, and some recent research works employed it in several computer vision tasks, such as image super-resolution, object detection, etc. Thanks to DPM's superior ability to generate fine-grained details, these research efforts have yielded significant successes. In this paper, we propose a new DPM-based generative medical image segmentation method, named EIDiffuSeg. Specifically, we first construct an explicit-implicit aggregation priori knowledge with directional supervision ability by mining the semantic distribution pattern in the frequency and spatial domains. Then, the explicit-implicit aggregation priori knowledge is integrated into the different encoding stages of the denoising backbone network using a novel unsupervised priori knowledge induction strategy, which can guide the model to generate a segmentation mask of the region of interest directionally from a random inference process. We evaluate our method on three medical image segmentation benchmark datasets with different modalities and achieve the best segmentation results compared to state-of-the-art methods. Especially, compared to several current diffusion-based image segmentation methods, we achieved a 9% Dice improvement in the polyp segmentation benchmark. Our code will be available at https://github.com/Notmezhan/EIDiffuSeg.

Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation.

The convergence of biotechnology and artificial intelligence has the potential to transform drug development, especially in the field of therapeutic peptide design. Peptides are short chains of amino acids with diverse therapeutic applications that offer several advantages over small molecular drugs, such as targeted therapy and minimal side effects. However, limited oral bioavailability and enzymatic degradation have limited their effectiveness. With advances in deep learning techniques, innovative approaches to peptide design have become possible. In this work, we demonstrate HYDRA, a hybrid deep learning approach that leverages the distribution modeling capabilities of a diffusion model and combines it with a binding affinity maximization algorithm that can be used for de novo design of peptide binders for various target receptors. As an application, we have used our approach to design therapeutic peptides targeting proteins expressed by Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) genes. The ability of HYDRA to generate peptides conditioned on the target receptor's binding sites makes it a promising approach for developing effective therapies for malaria and other diseases.

Corn Husk‐Derived Carbon Fused with Iron Oxide as Adsorbent for Cationic Dyes

AbstractThe development of nanomaterials for dye degradation has garnered significant interest due to their efficiency, environmental benefits, and cost‐effectiveness. In this study, a nanocomposite adsorbent composed of α‐Fe2O3 coupled with carbon derived from eco‐friendly corn husk has been developed. This material effectively captured cationic dyes, Methylene Blue (MB) and Malachite Green (MG), from aqueous solutions, including industrial dye effluent from local industry. The synthesized nanocomposite demonstrated rapid removal of MB and MG from the solution without the need for additional oxidizing or reducing agents. The adsorption conditions by varying parameters such as adsorbent dose, contact time, solution pH, initial dye concentration, and temperature have been optimized. Adsorption isothermal studies indicated that the Langmuir isotherm model best explained the adsorption process. Kinetic studies revealed that the adsorption process follows a pseudo‐first‐order model for MB, while the intraparticle diffusion model is more appropriate for MG. Moreover, the nanocomposite exhibited excellent reusability and regenerability for dye adsorption. Our study showcases the effectiveness of the synthesized nanocomposite adsorbent, comprising α‐Fe2O3 integrated with carbon derived from eco‐friendly corn husk using a simple and sustainable methodology, in efficiently removing cationic dyes from textile wastewater. This approach offers a promising solution for environmental remediation.

Conceptual Models for Exploring Sea-Surface Temperature Variability Vis-à Long-Range Weather Forecasting

This paper analyzes the ability of three conceptual stochastic models (one-box, two-box, and diffusion models) to reproduce essential features of sea surface temperature variability on intra-annual time scales. The variability of sea surface temperature, which is particularly influenced by feedback mechanisms in ocean surface–atmosphere coupling processes, is characterized by power spectral density, commonly used to analyze the response of dynamical systems to random forcing. The models are aimed at studying local effects of ocean–atmosphere interactions. Comparing observed and theoretical power spectra shows that in dynamically inactive ocean regions (e.g., north-eastern part of the Pacific Ocean), sea surface temperature variability can be described by linear stochastic models such as one-box and two-box models. In regions of the world ocean (e.g., north-western Pacific Ocean, subtropics of the North Atlantic, the Southern Ocean), in which the observed sea surface temperature spectra on the intra-annual time scales do not obey the ν−2 law (where ν is a regular frequency), the formation mechanisms of sea surface anomalies are mainly determined by ocean circulation rather than by local ocean–atmosphere interactions. The diffusion model can be used for simulating sea surface temperature anomalies in such areas of the global ocean. The models examined are not able to reproduce the variability of sea surface temperature over the entire frequency range for two primary reasons; first, because the object of study, the ocean surface mixed layer, changes during the year, and second, due to the difference in the physics of processes involved at different time scales.

Time‐Resolved Trigger Processes Leading to the Plinian Eruptions at Sakurajima Volcano, Japan

AbstractMafic magma recharge of crustal reservoirs and subsequent magma mixing has been considered a direct trigger of volcanic eruptions. However, although recharge frequently occurs in many active volcanoes, it rarely leads to an eruption immediately, making its role as a trigger ambiguous. Sakurajima volcano, Japan, has vigorously erupted three times since the 15th century following a common process; mixed magmas after recharge were once stored in a shallow, thick conduit before each eruption (conduit pre‐charge). We reconstructed the magma migration with a high time resolution by diffusion modeling on orthopyroxene and magnetite. Orthopyroxene phenocrysts recorded prolonged diffusive re‐equilibration timescales of years or more after recharge‐and‐mixing. Magnetite, which has the fastest elemental diffusivity among the phenocrysts examined, predominantly lacks zoning. This demonstrates that the mineral phase was re‐equilibrated with surrounding magma and homogenized via elemental diffusion after the final magmatic perturbation, implying the final repose of the shallow pre‐charged magma body for more than several tens of days. After this shallow stagnation period, the Plinian magmas began to ascend and reached the surface within 55 hr. Mass balance calculations show that crystallization‐driven vesiculation upon pre‐charge can produce overpressure sufficient to cause an eruption. The Sakurajima cases demonstrate the hierarchical timescales of trigger processes leading to the explosive eruptions.

A Cu/Ni/Cu/Sn1.8Ag microbump structure for void-free interface under long time high-temperature storage

Microbumps play a crucial role in interlayer interconnections for advanced packaging. Among the prevalent microbump structures are Cu/Sn and Cu/Ni/Sn configurations. However, as the bump size continues to shrink, excessive Kirkendall voids tend to form in Cu/Sn microbumps, leading to potential reliability issues. While Cu/Ni/Sn structures can suppress Kirkendall void formation, Ni3Sn4 intermetallic compound (IMC) exhibits inferior physical properties compared to Cu3Sn and Cu6Sn5. In this study, a novel Cu/Ni/Cu/Sn1.8Ag (CNCT) Sn-rich microbump structure with controlled Cu thickness is proposed to suppress Kirkendall void formation while promoting Cu6Sn5 and Cu3Sn as interfacial IMCs. A Ni barrier layer is inserted to partition the Cu pillar and impede Cu atom replenishment from the bottom side. The remaining Cu pillar at the top is designed to be 3μm thick, which is intended to be fully consumed by 8μm Sn1.8Ag solder while simultaneously serving as redundant protection for the bottom Cu pillar against unexpected Sn spillage. The effects of high temperature storage (HTS) at 150°C on IMC evolution in CNCT microbump bonding were investigated for 1500 hours. No significant voids were observed at the bonding interface during HTS. Interfacial positions recorded from 0 to 500 hours show that the Cu3Sn IMC grows unidirectionally toward the Cu side but not toward the Sn side, indicating a greater diffusion flux of Sn atoms compared to Cu atoms. The diffusion coefficients of Cu in Cu3Sn (DCu,ε), Cu6Sn5 (DCu,η), and Sn in Cu3Sn (DSn,ε), Cu6Sn5 (DSn,η) at 150°C were calculated as 1.36×10−18 m2/s, 5.01×10−19 m2/s, 9.53×10−19 m2/s and 1.10×10−18 m2/s, respectively. The dramatic decrease of DCu,η suggests that the diffusion capacity of Cu atoms into Sn is significantly suppressed. The notable transformation of Cu3Sn to Cu6Sn5 occurred after 500 hours. By 1500 hours, Cu3Sn was greatly consumed, and the bonding interface tended to form a stable Cu/Ni/Cu6Sn5/Ni/Cu structure. Diffusion models were established and discussed to elucidate the mechanisms governing both stages.

Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery.

Voltage-gated ion channels (VGICs) are pivotal in regulating electrical activity in excitable cells and are critical pharmaceutical targets for treating many diseases including cardiac arrhythmia and neuropathic pain. Despite their significance, challenges such as achieving target selectivity persist in VGIC drug development. Recent progress in deep learning, particularly diffusion models, has enabled the computational design of protein binders for any clinically relevant protein based solely on its structure. These developments coincide with a surge in experimental structural data for VGICs, providing a rich foundation for computational design efforts. This review explores the recent advancements in computational protein design using deep learning and diffusion methods, focusing on their application in designing protein binders to modulate VGIC activity. We discuss the potential use of these methods to computationally design protein binders targeting different regions of VGICs, including the pore domain, voltage-sensing domains, and interface with auxiliary subunits. We provide a comprehensive overview of the different design scenarios, discuss key structural considerations, and address the practical challenges in developing VGIC-targeting protein binders. By exploring these innovative computational methods, we aim to provide a framework for developing novel strategies that could significantly advance VGIC pharmacology and lead to the discovery of effective and safe therapeutics.

Rapid identification of the coefficient of moisture expansion of polymer materials by the employment of plates with asymmetric concentration fields

The paper pursues the development of a novel methodology for the rapid identification of the diffuso-mechanical properties of polymer materials based on the employment of plates subject to asymmetric moisture concentration fields. The study is carried out on epoxy plate samples equipped with a thin aluminium foil on a surface exposed to the environment to promote asymmetric moisture absorption. The asymmetric moisture fields promote deformations of the plate. Mass gain and plate curvatures are measured as a function of time during conditioning. By using a weakly coupled diffuso-mechanical model: 1D Fick’s diffusion model and 2D plane stress hygroelastic model the diffuso-mechanical properties of the material can be then identified. Due to the chosen size of the experimental samples the present study allows the identification of the coefficient of moisture expansion of the epoxy material. For the material under study, the following values can be identified for saturation mass gain, water diffusivity and coefficient of moisture expansion respectively: 1.67%, 0.025 mm2.h−1, 0.1628.

Antibody-SGM, a Score-Based Generative Model for Antibody Heavy-Chain Design.

Traditional computational methods for antibody design involved random mutagenesis followed by energy function assessment for candidate selection. Recently, diffusion models have garnered considerable attention as cutting-edge generative models, lauded for their remarkable performance. However, these methods often focus solely on the backbone or sequence, resulting in the incomplete depiction of the overall structure and necessitating additional techniques to predict the missing component. This study presents Antibody-SGM, an innovative joint structure-sequence diffusion model that addresses the limitations of existing protein backbone generation models. Unlike previous models, Antibody-SGM successfully integrates sequence-specific attributes and functional properties into the generation process. Our methodology generates full-atom native-like antibody heavy chains by refining the generation to create valid pairs of sequences and structures, starting with random sequences and structural properties. The versatility of our method is demonstrated through various applications, including the design of full-atom antibodies, antigen-specific CDR design, antibody heavy chains optimization, validation with Alphafold3, and the identification of crucial antibody sequences and structural features. Antibody-SGM also optimizes protein function through active inpainting learning, allowing simultaneous sequence and structure optimization. These improvements demonstrate the promise of our strategy for protein engineering and significantly increase the power of protein design models.

Diffusion barriers imposed by tissue topology shape Hedgehog morphogen gradients

Animals use a small number of morphogens to pattern tissues, but it is unclear how evolution modulates morphogen signaling range to match tissues of varying sizes. Here, we used single-molecule imaging in reconstituted morphogen gradients and in tissue explants to determine that Hedgehog diffused extracellularly as a monomer, and rapidly transitioned between membrane-confined and -unconfined states. Unexpectedly, the vertebrate-specific protein SCUBE1 expanded Hedgehog gradients by accelerating the transition rates between states without affecting the relative abundance of molecules in each state. This observation could not be explained under existing models of morphogen diffusion. Instead, we developed a topology-limited diffusion model in which cell-cell gaps create diffusion barriers, which morphogens can only overcome by passing through a membrane-unconfined state. Under this model, SCUBE1 promoted Hedgehog secretion and diffusion by allowing it to transiently overcome diffusion barriers. This multiscale understanding of morphogen gradient formation unified prior models and identified knobs that nature can use to tune morphogen gradient sizes across tissues and organisms.