HGSMDA: miRNA-Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss.

Biological research has demonstrated the significance of identifying miRNA-disease associations in the context of disease prevention, diagnosis, and treatment. However, the utilization of experimental approaches involving biological subjects to infer these associations is both costly and inefficient. Consequently, there is a pressing need to devise novel approaches that offer enhanced accuracy and effectiveness. Presently, the predominant methods employed for predicting disease associations rely on Graph Convolutional Network (GCN) techniques. However, the Graph Convolutional Network algorithm, which is locally aggregated, solely incorporates information from the immediate neighboring nodes of a given node at each layer. Consequently, GCN cannot simultaneously aggregate information from multiple nodes. This constraint significantly impacts the predictive efficacy of the model. To tackle this problem, we propose a novel approach, based on HyperGCN and Sørensen-Dice loss (HGSMDA), for predicting associations between miRNAs and diseases. In the initial phase, we developed multiple networks to represent the similarity between miRNAs and diseases and employed GCNs to extract information from diverse perspectives. Subsequently, we draw into HyperGCN to construct a miRNA-disease heteromorphic hypergraph using hypernodes and train GCN on the graph to aggregate information. Finally, we utilized the Sørensen-Dice loss function to evaluate the degree of similarity between the predicted outcomes and the ground truth values, thereby enabling the prediction of associations between miRNAs and diseases. In order to assess the soundness of our methodology, an extensive series of experiments was conducted employing the Human MicroRNA Disease Database (HMDD v3.2) as the dataset. The experimental outcomes unequivocally indicate that HGSMDA exhibits remarkable efficacy when compared to alternative methodologies. Furthermore, the predictive capacity of HGSMDA was corroborated through a case study focused on colon cancer. These findings strongly imply that HGSMDA represents a dependable and valid framework, thereby offering a novel avenue for investigating the intricate association between miRNAs and diseases.

Large-area transfer of two-dimensional materials free of cracks, contamination and wrinkles via controllable conformal contact.

The availability of graphene and other two-dimensional (2D) materials on a wide range of substrates forms the basis for large-area applications, such as graphene integration with silicon-based technologies, which requires graphene on silicon with outperforming carrier mobilities. However, 2D materials were only produced on limited archetypal substrates by chemical vapor deposition approaches. Reliable after-growth transfer techniques, that do not produce cracks, contamination, and wrinkles, are critical for layering 2D materials onto arbitrary substrates. Here we show that, by incorporating oxhydryl groups-containing volatile molecules, the supporting films can be deformed under heat to achieve a controllable conformal contact, enabling the large-area transfer of 2D films without cracks, contamination, and wrinkles. The resulting conformity with enhanced adhesion facilitates the direct delamination of supporting films from graphene, providing ultraclean surfaces and carrier mobilities up to 1,420,000 cm2 V-1 s-1 at 4 K.

Slip-Line-Guided Growth of Graphene.

Manipulating the crystal orientation of emerging 2D materials via chemical vapor deposition (CVD) is a key premise for obtaining single-crystalline films and designing specific grain-boundary (GB) structures. Herein, the controllable crystal orientation of graphene during the CVD process is demonstrated on a single-crystal metal surface with preexisting atomic-scale stair steps resulting from dislocation slip lines. The slip-line-guided growth principle is established to explain and predict the crystal orientation distribution of graphene on a variety of metal facets, especially for the multidirectional growth cases on Cu(hk0) and Cu(hkl) substrates. Not only large-area single-crystal graphene, but also bicrystal graphene with controllable GB misorientations, are successfully synthesized by rationally employing tailored metal substrate facets. As a demonstration, bicrystal graphenes with misorientations of ≈21° and ≈11° are constructed on Cu(410) and Cu(430) foils, respectively. This guideline builds a bridge linking the crystal orientation of graphene and the substrate facet, thereby opening a new avenue for constructing bicrystals with the desired GB structures or manipulating 2D superlattice twist angles in a bottom-up manner.

Large Single-Crystal Cu Foils with High-Index Facets by Strain-Engineered Anomalous Grain Growth.

The rich and complex arrangements of metal atoms in high-index metal facets afford appealing physical and chemical properties, which attracts extensive research interest in material science for the applications in catalysis and surface chemistry. However, it is still a challenge to prepare large-area high-index single crystals in a controllable and cost-efficient manner. Herein, entire commercially available decimeter-sized polycrystalline Cu foils are successfully transformed into single crystals with a series of high-index facets, relying on a strain-engineered anomalous grain growth technique. The introduction of a moderate thermal-contact stress upon the Cu foil during the annealing leads to the formation of high-index grains dominated by the thermal strain of the Cu foils, rather than the (111) surface driven by the surface energy. Besides, the designed static gradient of the temperature enables the as-formed high-index grain seed to expand throughout the entire Cu foil. The as-received high-index Cu foils can serve as the templates for producing high-index single-crystal Cu-based alloys. This work provides an appealing material basis for the epitaxial growth of 2D materials, and the applications that require the unique surface structures of high-index metal foils and their alloys.

Fast Growth of Strain-Free AlN on Graphene-Buffered Sapphire.

We study the roles of graphene acting as a buffer layer for growth of an AlN film on a sapphire substrate. Graphene can reduce the density of AlN nuclei but increase the growth rate for an individual nucleus at the initial growth stage. This can lead to the reduction of threading dislocations evolved at the coalescence boundaries. The graphene interlayer also weakens the interaction between AlN and sapphire and accommodates their large mismatch in the lattice and thermal expansion coefficients; thus, the compressive strain in AlN and the tensile strain in sapphire are largely relaxed. The effective relaxation of strain further leads to a low density of defects in the AlN films. These findings reveal the roles of graphene in III-nitride growth and offer valuable insights into the efficient applications of graphene in the light-emitting diode industry.