Table_1_A Novel Human Microbe-Disease Association Prediction Method Based on the Bidirectional Weighted Network.xls

The survival of human beings is inseparable from microbes. More and more studies have proved that microbes can affect human physiological processes in various aspects and are closely related to some human diseases. In this paper, based on known microbe-disease associations, a bidirectional weighted network was constructed by integrating the schemes of normalized Gaussian interactions and bidirectional recommendations firstly. And then, based on the newly constructed bidirectional network, a computational model called BWNMHMDA was developed to predict potential relationships between microbes and diseases. Finally, in order to evaluate the superiority of the new prediction model BWNMHMDA, the framework of LOOCV and 5-fold cross validation were implemented, and simulation results indicated that BWNMHMDA could achieve reliable AUCs of 0.9127 and 0.8967 ± 0.0027 in these two different frameworks respectively, which is outperformed some state-of-the-art methods. Moreover, case studies of asthma, colorectal carcinoma, and chronic obstructive pulmonary disease were implemented to further estimate the performance of BWNMHMDA. Experimental results showed that there are 10, 9, and 8 out of the top 10 predicted microbes having been confirmed by related literature in these three kinds of case studies separately, which also demonstrated that our new model BWNMHMDA could achieve satisfying prediction performance.</p

Additional file 1: of Prediction of microRNA-disease associations based on distance correlation set

The known lncRNA-disease associations for constructing the DS1. We list 583 known lncRNA-disease associations which were collected from LncRNAdisease dataset to construct the DS1. (XLS 58 kb

Rational Design of Yolk–Shell CuO/Silicalite-1@mSiO₂ Composites for a High-Performance Nonenzymatic Glucose Biosensor

In this study, an interface coassembly strategy is employed to rationally synthesize a yolk–shell CuO/silicalite-1@void@mSiO₂ composite consisting of silicalite-1 supported CuO nanoparticles confined in the hollow space of mesoporous silica, and the obtained composite materials were used as a novel nonenzymatic biosensor for highly sensitive and selective detecting glucose with excellent anti-interference ability. The synthesis of CuO/silicalite-1@mSiO₂ includes four steps: coating silicalite-1 particles with resorcinol-formaldehyde polymer (RF), immobilization of copper species, interface deposition of a mesoporous silica layer, and final calcination in air to decompose RF and form CuO nanoparticles. The unique hierarchical porous structure with mesopores and micropores is beneficial to selectively enrich glucose for fast oxidation into gluconic acid. Besides, the mesopores in the silica shell can effectively inhibit the large interfering substances or biomacromolecules diffusing into the void as well as the loss of CuO nanoparticles. The hollow chamber inside serves as a nanoreactor for glucose oxidation catalyzed by the active CuO nanoparticles, which are spatially accessible for glucose molecules. The nonenzymatic glucose biosensors based on CuO/silicalite-1@mSiO₂ materials show excellent electrocatalytic sensing performance with a wide linear range (5–500 μM), high sensitivity (5.5 μA·mM^–1·cm^–2), low detection limit (0.17 μM), and high selectivity against interfering species. Furthermore, the unique sensors even display a good capability in the determination of glucose in real blood serum samples

Additional file 4: of Prediction of microRNA-disease associations based on distance correlation set

The known lncRNA-miRNA associations for constructing the DS4. We list 1883 known lncRNA-miRNA associations which were collected from starBasev2.0 database to construct the DS4. (XLS 123 kb

Additional file 2: of Prediction of microRNA-disease associations based on distance correlation set

The known lncRNA-disease associations for constructing the DS2. We list 702 known lncRNA-disease associations which were collected from MNDR dataset to construct the DS2. (XLS 63 kb

Additional file 5: of Prediction of microRNA-disease associations based on distance correlation set

The known miRNA-disease associations for constructing the DS5. We list 3252 high-quality miRNA-disease associations which were collected from HMDD database to validate the performance of our method. (XLS 191 kb

Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schr\"odinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction tool with reduced complexity and good generalization capability. However, the complexity of long grid input points and the flexibility of neural network structure should be further optimized for broader applications. Here, we propose a convolutional feature separation modeling method to predict full-field ultrafast nonlinear dynamics with low complexity and high flexibility, where the linear effects are firstly modeled by NLSE-derived methods, then a convolutional deep learning method is implemented for nonlinearity modeling. With this method, the temporal relevance of nonlinear effects is substantially shortened, and the parameters and scale of neural networks can be greatly reduced. The running time achieves a 94% reduction versus NLSE and an 87% reduction versus RNN without accuracy deterioration. In addition, the input pulse conditions, including grid point numbers, durations, peak powers, and propagation distance, can be flexibly changed during the predicting process. The results represent a remarkable improvement in the ultrafast nonlinear dynamics prediction and this work also provides novel perspectives of the feature separation modeling method for quickly and flexibly studying the nonlinear characteristics in other fields

Additional file 5: of Prediction of microRNA-disease associations based on distance correlation set

The known miRNA-disease associations for constructing the DS5. We list 3252 high-quality miRNA-disease associations which were collected from HMDD database to validate the performance of our method. (XLS 191 kb

Additional file 3: of Prediction of microRNA-disease associations based on distance correlation set

The integrated lncRNA-disease associations for constructing the DS3. We list 1073 lncRNA-disease associations which were collected by integrating the datasets of DS1 and DS2. (XLS 83 kb