Biolinguistic graph fusion model for circRNA-miRNA association prediction.

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations.

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

Optimizing ZnO-Quantum Dot Interface with Thiol as Ligand Modification for High-Performance Quantum Dot Light-Emitting Diodes.

As the electron transport layer in quantum dot light-emitting diodes (QLEDs), ZnO suffers from excessive electrons that lead to luminescence quenching of the quantum dots (QDs) and charge-imbalance in QLEDs. Therefore, the interplay between ZnO and QDs requires an in-depth understanding. In this study, DFT and COSMOSL simulations are employed to investigate the effect of sulfur atoms on ZnO. Based on the simulations, thiol ligands (specifically 2-hydroxy-1-ethanethiol) to modify the ZnO nanocrystals are adopted. This modification alleviates the excess electrons without causing any additional issues in the charge injection in QLEDs. This modification strategy proves to be effective in improving the performance of red-emitting QLEDs, achieving an external quantum efficiency of over 23% and a remarkably long lifetime T95 of >12 000 h at 1000 cd m-2. Importantly, the relationship between ZnO layers with different electronic properties and their effect on the adjacent QDs through a single QD measurement is investigated. These findings show that the ZnO surface defects and electronic properties can significantly impact the device performance, highlighting the importance of optimizing the ZnO-QD interface, and showcasing a promising ligand strategy for the development of highly efficient QLEDs.

fvs-Si48: a direct bandgap silicon allotrope.

A structurally stable silicon allotrope is predicted by means of first principles calculations. This new structure is composed of a six-membered ring, a five-membered ring and a three-membered ring with the space group PA3[combining macron] and fvs topology, which is named fvs-Si48. The calculations of geometrical, vibrational, and electronic and optical properties reveal that fvs-Si48 has good mechanical stability with a mass density of 1.86 g cm-3. More importantly, it is a semiconductor with a direct band gap of 2.15 eV. From the analysis of its optical properties, there is the possibility of its synthesis in theory. This fvs-Si48 could have a wide range of applications in photo catalysts, optoelectronics, hydrogen storage and aerospace engineering.

Memory effect of spider major ampullate silk in loading-unloading cycles and the structural connotations.

Spider silk is repeatedly stretched while performing biological functions. There is a close relationship between the shape change of the fibre materials and their mechanical properties. However, the effect of the deformation and interval time on the structure and tensile behaviour properties of spider silk after repeatedly stretching by given strain value has been rarely reported. Here we found that major ampullate silk (MAS) can revert its tensile behaviour independent of its previous loading history via intervals of approximately 8 s to 5 min with constant and increased elongation, respectively, after being subjected to yield and hardening regions. The true stress-true strain curve beyond a given value of true strain is independent from the previous loading history of the sample. Even after longer intervals (≥1 h), MAS can reproduce the last tensile behaviour via one stretched. Despite recognizing the development of irreversible deformations in the material when tested in air, the reversible change in tensile behaviour outside the spider silk's elastic region has rarely been observed before. MAS has at least one proper ground state that allows it to present good shape and mechanical behaviour memory in terms of longitudinal stretching, functioning as a new strategy to achieve certain tensile properties. The analysis of the true stress-true strain curves was performed from a series of loading‒unloading tests to evaluate the evolution of those mechanical parameters with the cycle number. The elastic modulus measured in the loading steps increases monotonously with increasing values of true strain reached in the cycles. In contrast, a marginal variation is found in the values of the yield stress measured in the different cycles. The memory and variation in the mechanical behaviour and performance of MAS can be accounted for through the irreversible and reversible deformation micromechanisms and its combination in which the viscoelasticity of the material plays a leading role. These findings may be helpful to guide the biomimetic design of novel fibre materials such as spider silk gut via artificially stretching spider silk glands.

Variation in the Elastic Modulus and Increased Energy Dissipation Induced by Cyclic Straining of Argiope bruennichi Major Ampullate Gland Silk.

The trends exhibited by the parameters that describe the mechanical behaviour of major ampullate gland silk fibers spun by Argiope bruennichi spiders is explored by performing a series of loading-unloading tests at increasing values of strain, and by the subsequent analysis of the true stress-true strain curves obtained from these cycles. The elastic modulus, yields stress, energy absorbed, and energy dissipated in each cycle are computed in order to evaluate the evolution of these mechanical parameters with this cyclic straining. The elastic modulus is observed to increase steadily under these loading conditions, while only a moderate variation is found in the yield stress. It is also observed that a significant proportion of the energy initially absorbed in each cycle is not only dissipated, but that the material may recover partially from the associated irreversible deformation. This variation in the mechanical performance of spider silk is accounted for through a combination of irreversible and reversible deformation micromechanisms in which the viscoelasticity of the material plays a leading role.

Learning Hand Kinematics for Parkinson's Disease Assessment Using a Multimodal Sensor Glove.

Hand dysfunctions in Parkinson's disease include rigidity, muscle weakness, and tremor, which can severely affect the patient's daily life. Herein, a multimodal sensor glove is developed for quantifying the severity of Parkinson's disease symptoms in patients' hands while assessing the hands' multifunctionality. Toward signal processing, various algorithms are used to quantify and analyze each signal: Exponentially Weighted Average algorithm and Kalman filter are used to filter out noise, normalization to process bending signals, K-Means Cluster Analysis to classify muscle strength grades, and Back Propagation Neural Network to identify and classify tremor signals with an accuracy of 95.83%. Given the compelling features, the flexibility, muscle strength, and stability assessed by the glove and the clinical observations are proved to be highly consistent with Kappa values of 0.833, 0.867, and 0.937, respectively. The intraclass correlation coefficients obtained by reliability evaluation experiments for the three assessments are greater than 0.9, indicating that the system is reliable. The glove can be applied to assist in formulating targeted rehabilitation treatments and improve hand recovery efficiency.

Airline Point-of-Care System on Seat Belt for Hybrid Physiological Signal Monitoring.

With a focus on disease prevention and health promotion, a reactive and disease-centric healthcare system is revolutionized to a point-of-care model by the application of wearable devices. The convenience and low cost made it possible for long-term monitoring of health problems in long-distance traveling such as flights. While most of the existing health monitoring systems on aircrafts are limited for pilots, point-of-care systems provide choices for passengers to enjoy healthcare at the same level. Here in this paper, an airline point-of-care system containing hybrid electrocardiogram (ECG), breathing, and motion signals detection is proposed. At the same time, we propose the diagnosis of sleep apnea-hypopnea syndrome (SAHS) on flights as an application of this system to satisfy the inevitable demands for sleeping on long-haul flights. The hardware design includes ECG electrodes, flexible piezoelectric belts, and a control box, which enables the system to detect the original data of ECG, breathing, and motion signals. By processing these data with interval extraction-based feature selection method, the signals would be characterized and then provided for the long short-term memory recurrent neural network (LSTM-RNN) to classify the SAHS. Compared with other machine learning methods, our model shows high accuracy up to 84-85% with the lowest overfit problem, which proves its potential application in other related fields.

Ultrafast and Selective Nanofiltration Enabled by Graphene Oxide Membranes with Unzipped Carbon Nanotube Networks.

Carbon nanomaterials have proven their wide applicability in molecular separation and water purification techniques. Here, an unzipped carbon nanotubes (CNT) embedded graphene oxide (GO) membrane (uCNTm) is reported. The multiwalled CNTs were longitudinally cut into multilayer graphene oxide nanoribbons by a modified Hummer method. To investigate the varying effects of different bandwidths of unzipped CNTs on their properties, four uCNTms were prepared by a vacuum-assisted filtration process. Unzipped-CNTs with different bandwidths were made by unzipping multiwalled CNTs with outer diameters of 0-10, 10-20, 20-30, and 30-50 nm and named uCNTm-1, uCNTm-2, uCNTm-3, and uCNTm-4, respectively. The uCNTms exhibited good stability in different pH solutions, and the water permeability of the composite membranes showed an increasing trend with the increase of the inserted uCNTm's bandwidth up to 107 L·m-2·h-1·bar-1, which was more than 10 times greater than that of pure GO membranes. The composite membranes showed decent dye screening performance with the rejection rate of methylene blue and rhodamine B both greater than 99%.

An ultrathin rechargeable solid-state zinc ion fiber battery for electronic textiles.

Electronic textiles (e-textiles), having the capability of interacting with the human body and surroundings, are changing our everyday life in fundamental and meaningful ways. Yet, the expansion of the field of e-textiles is still limited by the lack of stable and biocompatible power sources with aesthetic designs. Here, we report a rechargeable solid-state Zn/MnO2 fiber battery with stable cyclic performance exceeding 500 hours while maintaining 98.0% capacity after more than 1000 charging/recharging cycles. The mechanism of the high electrical and mechanical performance due to the graphene oxide­embedded polyvinyl alcohol hydrogel electrolytes was rationalized by Monte Carlo simulation and finite element analysis. With a collection of key features including thin, light weight, economic, and biocompatible as well as high energy density, the Zn/MnO2 fiber battery could seamlessly be integrated into a multifunctional on-body e-textile, which provides a stable power unit for continuous and simultaneous heart rate, temperature, humidity, and altitude monitoring.

A Multi-Cell Hybrid Approach to Elevate the Energy Absorption of Micro-Lattice Materials.

Multi-cell hybrid micro-lattice materials, in which the stretching dominated octet cells were adopted as the strengthen phase while the bending dominated body centered cubic (BCC) lattice was chosen as the soft matrix, were proposed to achieve superior mechanical properties and energy absorption performance. Both stochastic and symmetric distribution of octet cells in the BCC lattice were considered. The cell assembly micromechanics finite element model (FEM) was built and validated by the experimental results. Accordingly, virtual tests were conducted to reveal the stress-strain relationship and deformation patterns of the hybrid lattice specimens. Meanwhile, the influence of reinforcement volume fraction and strut material on the energy absorption ability of the specimens was analyzed. It was concluded that the reinforced octet cells could be adopted to elevate the elastic modulus and collapse strength of the pure BCC micro-lattice material. The multi-cell design could lead to strain hardening in the plateau stress region which resulted in higher plateau stresses and energy absorption capacities. Besides, the symmetric distribution of reinforcements would cause significant stress fluctuations in the plateau region. The obtained results demonstrated that the multi-cell hybrid lattice architectures could be applied to tailor the mechanical behavior and plastic energy absorption performance of micro-lattice materials.

Dynamic Mechanical Behavior of Hierarchical Resin Honeycomb by 3D Printing.

In this paper, surface projection micron stereo-lithography technology (PµSL) by 3D printing was used to prepare two resin honeycomb materials with different levels, and the mechanical behavior of these materials was studied. The quasi-static compression experiment and the dynamic compression experiment were carried out on the samples using the in situ micro-compression testing machine and the Split Hopkinson bar (SHPB) experimental equipment. The stress-strain curves of these materials at different strain rates were obtained, and the energy absorption characteristic of materials with two different levels were analyzed. This article reveals that the collapse strength and energy absorption properties of the materials are related to the hierarchical level of honeycomb. Multi-level hierarchical honeycomb (MHH) has higher collapse strength and better energy absorption properties than single-level hierarchical honeycomb (SHH). It turned out that increasing the hierarchical level of honeycomb could improve the mechanical properties of the materials. In the future development of products, the mechanical properties of hierarchical material by 3D printing can be further optimized through changing the level of the fractal structure.