The optimal connection model for blood vessels segmentation and the MEA-Net

Vascular diseases have long been regarded as a significant health concern. Accurately detecting the location, shape, and afflicted regions of blood vessels from a diverse range of medical images has proven to be a major challenge. Obtaining blood vessels that retain their correct topological structures is currently a crucial research issue. Numerous efforts have sought to reinforce neural networks' learning of vascular geometric features, including measures to ensure the correct topological structure of the segmentation result's vessel centerline. Typically, these methods extract topological features from the network's segmentation result and then apply regular constraints to reinforce the accuracy of critical components and the overall topological structure. However, as blood vessels are three-dimensional structures, it is essential to achieve complete local vessel segmentation, which necessitates enhancing the segmentation of vessel boundaries. Furthermore, current methods are limited to handling 2D blood vessel fragmentation cases. Our proposed boundary attention module directly extracts boundary voxels from the network's segmentation result. Additionally, we have established an optimal connection model based on minimal surfaces to determine the connection order between blood vessels. Our method achieves state-of-the-art performance in 3D multi-class vascular segmentation tasks, as evidenced by the high values of Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD) metrics. Furthermore, our approach improves the Betti error, LR error, and BR error indicators of vessel richness and structural integrity by more than 10% compared to other methods, and effectively addresses vessel fragmentation and yields blood vessels with a more precise topological structure.Comment: 19 page

Knowledge-refined Denoising Network for Robust Recommendation

Knowledge graph (KG), which contains rich side information, becomes an essential part to boost the recommendation performance and improve its explainability. However, existing knowledge-aware recommendation methods directly perform information propagation on KG and user-item bipartite graph, ignoring the impacts of \textit{task-irrelevant knowledge propagation} and \textit{vulnerability to interaction noise}, which limits their performance. To solve these issues, we propose a robust knowledge-aware recommendation framework, called \textit{Knowledge-refined Denoising Network} (KRDN), to prune the task-irrelevant knowledge associations and noisy implicit feedback simultaneously. KRDN consists of an adaptive knowledge refining strategy and a contrastive denoising mechanism, which are able to automatically distill high-quality KG triplets for aggregation and prune noisy implicit feedback respectively. Besides, we also design the self-adapted loss function and the gradient estimator for model optimization. The experimental results on three benchmark datasets demonstrate the effectiveness and robustness of KRDN over the state-of-the-art knowledge-aware methods like KGIN, MCCLK, and KGCL, and also outperform robust recommendation models like SGL and SimGCL

Systematic Hydrogen‐Bond Manipulations To Establish Polysaccharide Structure–Property Correlations

A dense hydrogen‐bond network is responsible for the mechanical and structural properties of polysaccharides. Random derivatization alters the properties of the bulk material by disrupting the hydrogen bonds, but obstructs detailed structure–function correlations. We have prepared well‐defined unnatural oligosaccharides including methylated, deoxygenated, deoxyfluorinated, as well as carboxymethylated cellulose and chitin analogues with full control over the degree and pattern of substitution. Molecular dynamics simulations and crystallographic analysis show how distinct hydrogen‐bond modifications drastically affect the solubility, aggregation behavior, and crystallinity of carbohydrate materials. This systematic approach to establishing detailed structure–property correlations will guide the synthesis of novel, tailor‐made carbohydrate materials

Systematic Hydrogen‐Bond Manipulations To Establish Polysaccharide Structure–Property Correlations

A dense hydrogen‐bond network is responsible for the mechanical and structural properties of polysaccharides. Random derivatization alters the properties of the bulk material by disrupting the hydrogen bonds, but obstructs detailed structure–function correlations. We have prepared well‐defined unnatural oligosaccharides including methylated, deoxygenated, deoxyfluorinated, as well as carboxymethylated cellulose and chitin analogues with full control over the degree and pattern of substitution. Molecular dynamics simulations and crystallographic analysis show how distinct hydrogen‐bond modifications drastically affect the solubility, aggregation behavior, and crystallinity of carbohydrate materials. This systematic approach to establishing detailed structure–property correlations will guide the synthesis of novel, tailor‐made carbohydrate materials

Exploring the Molecular Conformation Space by Soft Molecule–Surface Collision

Biomolecules function by adopting multiple conformations. Such dynamics are governed by the conformation landscape whose study requires characterization of the ground and excited conformation states. Here, the conformational landscape of a molecule is sampled by exciting an initial gas-phase molecular conformer into diverse conformation states, using soft molecule-surface collision (0.5-5.0 eV). The resulting ground and excited molecular conformations, adsorbed on the surface, are imaged at the single-molecule level. This technique permits the exploration of oligosaccharide conformations, until now, limited by the high flexibility of oligosaccharides and ensemble-averaged analytical methods. As a model for cellulose, cellohexaose chains are observed in two conformational extremes, the typical "extended" chain and the atypical "coiled" chain-the latter identified as the gas-phase conformer preserved on the surface. Observing conformations between these two extremes reveals the physical properties of cellohexaose, behaving as a rigid ribbon that becomes flexible when twisted. The conformation space of any molecule that can be electrosprayed can now be explored

Abundance of kinless hubs within soil microbial networks are associated with high functional potential in agricultural ecosystems

Microbial taxa within complex ecological networks can be classified by their universal roles based on their level of connectivity with other taxa. Highly connected taxa within an ecological network (kinless hubs) are theoretically expected to support higher levels of ecosystem functions than less connected taxa (peripherals). Empirical evidence of the role of kinless hubs in regulating the functional potential of soil microbial communities, however, is largely unexplored and poorly understood in agricultural ecosystems. Here, we built a correlation network of fungal and bacterial taxa using a large-scale survey consisting of 243 soil samples across functionally and economically important agricultural ecosystems (wheat and maize); and found that the relative abundance of taxa classified as kinless hubs within the ecological network are positively and significantly correlated with the abundance of functional genes including genes for C fixation, C degradation, C methanol, N cycling, P cycling and S cycling. Structural equation modeling of multiple soil properties further indicated that kinless hubs, but not provincial, connector or peripheral taxa, had direct significant and positive relationships with the abundance of multiple functional genes. Our findings provide novel evidence that the relative abundance of soil taxa classified as kinless hubs within microbial networks are associated with high functional potential, with implications for understanding and managing (through manipulating microbial key species) agricultural ecosystems at a large spatial scale

Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perception

Collaboration by leveraging the shared semantic information plays a crucial role in overcoming the perception capability limitations of isolated agents. However, existing collaborative perception methods tend to focus solely on the spatial features of semantic information, while neglecting the importance of the temporal dimension. Consequently, the potential benefits of collaboration remain underutilized. In this article, we propose Select2Col, a novel collaborative perception framework that takes into account the {s}patial-t{e}mpora{l} importanc{e} of semanti{c} informa{t}ion. Within the Select2Col, we develop a collaborator selection method that utilizes a lightweight graph neural network (GNN) to estimate the importance of semantic information (IoSI) in enhancing perception performance, thereby identifying contributive collaborators while excluding those that bring negative impact. Moreover, we present a semantic information fusion algorithm called HPHA (historical prior hybrid attention), which integrates multi-scale attention and short-term attention modules to capture the IoSI in feature representation from the spatial and temporal dimensions respectively, and assigns IoSI-consistent weights for efficient fusion of information from selected collaborators. Extensive experiments on two open datasets demonstrate that our proposed Select2Col significantly improves the perception performance compared to state-of-the-art approaches. The code associated with this research is publicly available at https://github.com/huangqzj/Select2Col/

Synergistic effects of hybrid conductive nanofillers on the performance of 3D printed highly elastic strain sensors

In this work, thermoplastic polyurethane based conductive polymer composites containing carbon nanotubes (CNTs) and synthesized silver nanoparticles (AgNPs) were used to fabricate highly elastic strain sensors via fused deposition modeling. The printability of the materials was improved with the introduction of the nanofillers, and the size and content of the AgNPs significantly influenced the sensing performance of the 3D printed sensors. When the CNTs:AgNPs weight ratio was 5:1, the sensors exhibited outstanding performance with high sensitivity (GF = 43260 at 250% strain), high linearity (R 2 = 0.97 within 50% strain), fast response (~57 ms), and excellent repeatability (1000 cycles) due to synergistic effects. A modeling study based on the Simmons' tunneling theory was also undertaken to analyze the sensing mechanism. The sensor was applied to monitor diverse joint movements and facial motion, showing its potential for application in intelligent robots, prosthetics, and wear-able devices where customizability are usually demanded

Metal-free photoanodes for C–H functionalization

Organic semiconductors, such as carbon nitride, when employed as powders, show attractive photocatalytic properties, but their photoelectrochemical performance suffers from low charge transport capability, charge carrier recombination, and self-oxidation. High film-substrate affinity and well-designed heterojunction structures may address these issues, achieved through advanced film generation techniques. Here, we introduce a spin coating pretreatment of a conductive substrate with a multipurpose polymer and a supramolecular precursor, followed by chemical vapor deposition for the synthesis of dual-layer carbon nitride photoelectrodes. These photoelectrodes are composed of a porous microtubular top layer and an interlayer between the porous film and the conductive substrate. The polymer improves the polymerization degree of carbon nitride and introduces C-C bonds to increase its electrical conductivity. These carbon nitride photoelectrodes exhibit state-of-the-art photoelectrochemical performance and achieve high yield in C-H functionalization. This carbon nitride photoelectrode synthesis strategy may be readily adapted to other reported processes to optimize their performance