PACS: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model

Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omic data and clinical characteristics among different cancer subtypes. Therefore, accurate classification of cancer subtypes can help doctors choose the most appropriate treatment options, improve treatment outcomes, and provide more accurate patient survival predictions. In this study, we propose a supervised multi-head attention mechanism model (SMA) to classify cancer subtypes successfully. The attention mechanism and feature sharing module of the SMA model can successfully learn the global and local feature information of multi-omics data. Second, it enriches the parameters of the model by deeply fusing multi-head attention encoders from Siamese through the fusion module. Validated by extensive experiments, the SMA model achieves the highest accuracy, F1 macroscopic, F1 weighted, and accurate classification of cancer subtypes in simulated, single-cell, and cancer multiomics datasets compared to AE, CNN, and GNN-based models. Therefore, we contribute to future research on multiomics data using our attention-based approach.Comment: Submitted to BIBM202

Efficient Inverted ITO-Free Organic Solar Cells Based on Transparent Silver Electrode with Aqueous Solution-Processed ZnO Interlayer

Efficient inverted organic solar cells (OSCs) with the MoO3 (2 nm)/Ag (12 nm) transparent cathode and an aqueous solution ZnO electron extraction layer processed at low temperature are investigated in this work. The blend of low bandgap poly[[4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b′]dithiophene-2,6-diyl][3-fluoro-2-[(2-ethylhexyl)carbonyl]thieno[3,4-b]thiophenediyl]] (PTB7) and [6,6]-phenyl-C71-butyric acid methylester (PC71BM) is employed as the photoactive layer here. A power conversion efficiency (PCE) of 5.55% is achieved for such indium tin oxide- (ITO-) free OSCs under AM 1.5G simulated illumination, comparable to that of ITO-based reference OSCs (PCE of 6.11%). It is found that this ZnO interlayer not only slightly enhances the transparency of MoO3/Ag cathode but also obtains a lower root-mean-square (RMS) roughness on the MoO3/Ag surface. Meanwhile, ITO-free OSCs also show a good stability. The PCE of the devices still remains above 85% of the original values after 30 days, which is slightly superior to ITO-based reference OSCs where the 16% degradation in PCE is observed after 30 days. It may be instructive for further research of OSCs based on metal thin film electrodes

MCR-ALS-based muscle synergy extraction method combined with LSTM neural network for motion intention detection

IntroductionThe time-varying and individual variability of surface electromyographic signals (sEMG) can lead to poorer motor intention detection results from different subjects and longer temporal intervals between training and testing datasets. The consistency of using muscle synergy between the same tasks may be beneficial to improve the detection accuracy over long time ranges. However, the conventional muscle synergy extraction methods, such as non-negative matrix factorization (NMF) and principal component analysis (PCA) have some limitations in the field of motor intention detection, especially in the continuous estimation of upper limb joint angles.MethodsIn this study, we proposed a reliable multivariate curve-resolved-alternating least squares (MCR-ALS) muscle synergy extraction method combined with long-short term memory neural network (LSTM) to estimate continuous elbow joint motion by using the sEMG datasets from different subjects and different days. The pre-processed sEMG signals were then decomposed into muscle synergies by MCR-ALS, NMF and PCA methods, and the decomposed muscle activation matrices were used as sEMG features. The sEMG features and elbow joint angular signals were input to LSTM to establish a neural network model. Finally, the established neural network models were tested by using sEMG dataset from different subjects and different days, and the detection accuracy was measured by correlation coefficient.ResultsThe detection accuracy of elbow joint angle was more than 85% by using the proposed method. This result was significantly higher than the detection accuracies obtained by using NMF and PCA methods. The results showed that the proposed method can improve the accuracy of motor intention detection results from different subjects and different acquisition timepoints.DiscussionThis study successfully improves the robustness of sEMG signals in neural network applications using an innovative muscle synergy extraction method. It contributes to the application of human physiological signals in human-machine interaction

The Medical & Health Maintenance Tourism and Ecosystem of Industries around TCM Services

Background: Medical and health maintenance tourism (MHMT) is rising fast in our aging society with the largest population in the world. At the very core of its development, medical services are crucial for driving the development of the related industries that can develop into an ecosystem for sustainability. Methods: Through the market analysis, it is made very clear that the diversified demands can only be met by the medical services around TCM with its various advantages, which are proven in the example of the success of Mt. Qingcheng as Tianfu Qingcheng Resort for MHMT. Results: With TCM services as the driving force, the local hospitals, tertiary industry, transportation and logistics, and the R&D of new medicine grow into an ecosystem. Conclusions: The green ecosystem will enable the sustainable development of MHMT

A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information

A visual localization approach for dynamic objects based on hybrid semantic-geometry information is presented. Due to the interference of moving objects in the real environment, the traditional simultaneous localization and mapping (SLAM) system can be corrupted. To address this problem, we propose a method for static/dynamic image segmentation that leverages semantic and geometric modules, including optical flow residual clustering, epipolar constraint checks, semantic segmentation, and outlier elimination. We integrated the proposed approach into the state-of-the-art ORB-SLAM2 and evaluated its performance on both public datasets and a quadcopter platform. Experimental results demonstrated that the root-mean-square error of the absolute trajectory error improved, on average, by 93.63% in highly dynamic benchmarks when compared with ORB-SLAM2. Thus, the proposed method can improve the performance of state-of-the-art SLAM systems in challenging scenarios

A Heuristic Diagnostic Method for a PV System: Triple-Layered Particle Swarm Optimization–Back-Propagation Neural Network

This paper proposes a heuristic triple layered particle swarm optimization–back-propagation (PSO-BP) neural network method for improving the convergence and prediction accuracy of the fault diagnosis system of the photovoltaic (PV) array. The parameters, open-circuit voltage (Voc), short-circuit current (Isc), maximum power (Pm) and voltage at maximum power point (Vm) are extracted from the output curve of the PV array as identification parameters for the fault diagnosis system. This study compares performances of two methods, the back-propagation neural network method, which is widely used, and the heuristic method with MATLAB. In the training phase, the back-propagation method takes about 425 steps to convergence, while the heuristic method needs only 312 steps. In the fault diagnosis phase, the prediction accuracy of the heuristic method is 93.33%, while the back-propagation method scores 86.67%. It is concluded that the heuristic method can not only improve the convergence of the simulation but also significantly improve the prediction accuracy of the fault diagnosis system

Extreme Rainfall Simulations with Changing Resolution of Orography Based on the Yin-He Global Spectrum Model: A Case Study of the Zhengzhou 20·7 Extreme Rainfall Event

In recent years, the study of numerical weather prediction (NWP) in complex orographic areas has attracted a great deal of attention. Complex orography plays an important role in the occurrence and development of extreme rainfall events. In this study, the Yin–He Global Spectrum Model (YHGSM) was used, and the wave number truncation method was employed to decompose the orographic data to different resolutions. The obtained orographic data with different resolutions were used to simulate the extreme rainfall in Zhengzhou, Henan Province, China, to discuss the degree of influence and mechanism of the different orographic resolutions on the extreme rainfall. The results show that the simulation results of the YHGSM with high-resolution orography are better than those of the low-resolution orography in terms of the rainfall intensity and range. When the rainfall intensity is higher, the results of the low-resolution orography simulated the rainfall range of big heavy rainfall better. The orography mainly affected the rainfall by affecting the velocity of the updraft, but it had a limited influence on the maximum height that the updraft could reach. A strong updraft is one of the key factors leading to extreme rainfall in Henan Province. When the orographic resolution changes, the sensitivity of the vertical velocity of the updraft to the orographic resolution is the greatest, the sensitivity of the upper-air divergence and low-level vorticity to the orographic resolution is lower than that of the vertical velocity. In conclusion, the high-resolution orography is helpful in improving the model’s prediction of extreme rainfall, and when predicting extreme rainfall in complex orographic areas, forecasters may need to artificially increase rainfall based on model results

Simultaneous Qualitative and Quantitative Analysis of Multiple Chemical Constituents in YiQiFuMai Injection by Ultra-Fast Liquid Chromatography Coupled with Ion Trap Time-of-Flight Mass Spectrometry

YiQiFuMai injection (YQFM) is a modern lyophilized powder preparation derived from the traditional Chinese medicine Sheng-mai san (SMS) used for treating cardiovascular diseases, such as chronic heart failure. However, its chemical composition has not been fully elucidated, particularly for the preparation derived from Ophiopogon japonicus. This study aimed to establish a systematic and reliable method to quickly and simultaneously analyze the chemical constituents in YQFM by ultra-fast liquid chromatography coupled with ion trap time-of-flight mass spectrometry (UFLC-IT-TOF/MS). Sixty-five compounds in YQFM were tentatively identified by comparison with reference substances or literature data. Furthermore, twenty-one compounds, including three ophiopogonins, fifteen ginsenosides and three lignans were quantified by UFLC-IT-TOF/MS. Notably, this is the first determination of steroidal saponins from O. japonicus in YQFM. The relative standard deviations (RSDs) of intra- and inter-day precision, reproducibility and stability were <4.9% and all analytes showed good linearity (R2 ≥ 0.9952) and acceptable recovery of 91.8%–104.2% (RSD ≤ 5.4%), indicating that the methods were reliable. These methods were successfully applied to quantitative analysis of ten batches of YQFM. The developed approach can provide useful and comprehensive information for quality control, further mechanistic studies in vivo and clinical application of YQFM