Background
Though the matrix metalloproteinases (MMPs) are widely investigated in lung cancer (LC), however, almost no review systematically clarify their multi-faced roles in LC.
Methods
We ...investigated the expression of MMPs and their effects on survival of patients with LC, the resistance mechanisms of MMPs in anti-tumor therapy, the regulatory networks of MMPs involved, the function of MMPs inducing CSCLs, MMPs-related tumor immunity, and effects of MMP polymorphisms on risk of LC.
Results
High expression of MMPs was mainly related to poor survival, high clinical stages and cancer metastasis. Role of MMPs in LC are multi-faced. MMPs are involved in drug resistance, induced CSCLs, participated in tumor immunity. Besides, MMPs polymorphisms may increase risk of LC.
Conclusions
MMPs might be promising targets to restore the anti-tumor immune response and enhance the killing function of nature immune cells in LC.
Uranium is a key resource for the development of the nuclear industry, and extracting uranium from the natural seawater is one of the most promising ways to address the shortage of uranium resources. ...Herein, a semiconducting covalent organic framework (named NDA‐TN‐AO) with excellent photocatalytic and photoelectric activities was synthesized. The excellent photocatalytic effect endowed NDA‐TN‐AO with a high anti‐biofouling activity by generating biotoxic reactive oxygen species and promoting photoelectrons to reduce the adsorbed UVI to insoluble UIV, thereby increasing the uranium extraction capacity. Owing to the photoinduced effect, the adsorption capacity of NDA‐TN‐AO to uranium in seawater reaches 6.07 mg g−1, which is 1.33 times of that in dark. The NDA‐TN‐AO with enhanced adsorption capacity is a promising material for extracting uranium from the natural seawater.
Photoelectric and photocatalytic effects endow the covalent organic framework NDA‐TN‐AO with good anti‐biofouling activity. This occurs by generating biotoxic reactive oxygen species and promoting photoelectrons to reduce the adsorbed UVI to insoluble UIV, thereby improving the uranium adsorption capacity.
This paper provides an in-depth analysis and research on piano timbre teaching in the context of artificial intelligence interaction, a bold vision of piano teaching, proposes a feasible solution in ...terms of teaching modules in intelligent piano teaching for senior teachers, and proposes an implementation path for the integration of intelligent piano and piano teaching from the four main blocks of piano teaching. Based on the multiplicative harmonic model of monophonic signal, combined with the variability of timbre characteristics, an audio synthesis model with editable timbre is proposed, and the experimental results show that editing the timbre parameters in the model can realize timbre modification, and the synthesized timbre conforms to the piano timbre characteristics. Based on the timbre analysis and the timbre synthesis model, a piano timbre library generation system is designed. The detailed design of the software modules such as audio file reading and writing, audio information analysis, timbre parameter acquisition, timbre synthesis, and simulated performance is given. The system can generate piano timbre libraries of different qualities flexibly and meet the requirements of timbre realism. The teaching experiment designed for teaching practice from solo teaching, and the practice target is first-year undergraduate students in the university, and the practice period is six weeks, and finally, the feasibility of intelligent piano teaching application is analysed by combining the experimental results. Through the teaching objectives, teaching content, and teaching methods, teaching environment reflects intelligent piano teaching to make up for the limitations of traditional piano teaching. Analyse the development trend of intelligent piano teaching in the context of artificial intelligence interaction, and explore the value of intelligent piano teaching.
Anisotropic Nd2Fe14B/Fe67Co33 nanocomposite thin films are successfully fabricated. The multilayer composite films comprise Nd‐rich shell‐enveloped Nd2Fe14B grains and a Fe67Co33 phase. The strong ...(001) texture of the Nd2Fe14B grains and the presence of exchange‐coupled Fe67Co33 lead to a high remanence and the presence of the Nd‐rich shell gives rise to a high coercivity. The unique nanocomposite microstructure provides hints for developing rare‐earth‐lean high‐performance magnets.
Sensor selection plays an essential and fundamental role in prognostics and health management technology, and it is closely related to fault diagnosis, life prediction, and health assessment. The ...existing methods of sensor selection do not have an evaluation standard, which leads to different selection results. It is not helpful for the selection and layout of sensors. This paper proposes a comprehensive evaluation method of sensor selection for prognostics and health management (PHM) based on grey clustering. The described approach divides sensors into three grey classes, and defines and quantifies three grey indexes based on a dependency matrix. After a brief introduction to the whitening weight function, we propose a combination weight considering the objective data and subjective tendency to improve the effectiveness of the selection result. Finally, the clustering result of sensors is obtained by analyzing the clustering coefficient, which is calculated based on the grey clustering theory. The proposed approach is illustrated by an electronic control system, in which the effectiveness of different methods of sensor selection is compared. The result shows that the technique can give a convincing analysis result by evaluating the selection results of different methods, and is also very helpful for adjusting sensors to provide a more precise result. This approach can be utilized in sensor selection and evaluation for prognostics and health management.
The intelligent recognition of epileptic electro-encephalogram (EEG) signals is a valuable tool for the epileptic seizure detection. Recent deep learning models fail to fully consider both spectral ...and temporal domain representations simultaneously, which may lead to omitting the nonstationary or nonlinear property in epileptic EEGs and further produce a suboptimal recognition performance consequently. In this paper, an end-to-end EEG seizure detection framework is proposed by using a novel channel-embedding spectral-temporal squeeze-and-excitation network (CE-stSENet) with a maximum mean discrepancy-based information maximizing loss. Specifically, the CE-stSENet firstly integrates both multi-level spectral and multi-scale temporal analysis simultaneously. Hierarchical multi-domain representations are then captured in a unified manner with a variant of squeeze-and-excitation block. The classification net is finally implemented for epileptic EEG recognition based on features extracted in previous subnetworks. Particularly, to address the fact that the scarcity of seizure events results in finite data distribution and the severe overfitting problem in seizure detection, the CE-stSENet is coordinated with a maximum mean discrepancy-based information maximizing loss for mitigating the overfitting problem. Competitive experimental results on three EEG datasets against the state-of-the-art methods demonstrate the effectiveness of the proposed framework in recognizing epileptic EEGs, indicating its powerful capability in the automatic seizure detection.
This article deeply explores the behavior and effect of online learning in ideological and political education in colleges and universities, firstly, it clarifies the mechanism of the occurrence of ...online ideological and political learning behavior, and constructs the corresponding indicators of learning behavior. Using CART tree and XGBoost model, the article ranks the feature importance of learning behaviors. It combines with Bayesian network to construct a comprehensive analysis model to explore the causal relationship between learning behaviors and learning effects. By analyzing the data of M online learning platform in 2021, the study found that resource learning features have the most significant impact on learning performance, especially the indicators of video viewing time, number of homework submissions and number of online discussions. The study results show that when learning resources are rich and professional, learning performance is significantly improved, providing an effective way to optimize the teaching quality of online Civics education.
Under the background of the artificial intelligence era, the education management style of colleges and universities is moving towards a more intelligent direction. This paper combines the F-S ...learning style model and the environmental characteristics of online learning to construct an index system of online learning behavioral characteristics for Civic and Political Education in colleges and universities. On this basis, the traditional K-means clustering algorithm is improved based on three-branch decision-making and is used to cluster and divide the learning behaviors of students in the Civic and Political Education courses. At the same time, the degree of interest is introduced to optimize the Apriori algorithm, and the association rules of students’ learning behavior and performance are mined. Then, taking the student learning behavior data of the online course of X Civic and Political Education in colleges and universities as a research sample, TK-means, and Apriori algorithm are used to explore the management path and operation mechanism of Civic and Political Education in colleges and universities. The study shows that among the students in the four clusters, the students with higher task point completion, chapter completion number, and check-in number are the most, accounting for 32%, and the students with a higher course video completion progress, task completion number, and document completion are the least, accounting for 19%. The probability of receiving an ‘Excellent’ grade was 93.9% when students were classified as ‘Visual-Active-High-Commitment’. When students were of the ‘Verbal - Active - passive - High Engagement’ type, there was a 91.3% probability of getting a ‘Medium’ grade. The effectiveness of civics education can be improved by enriching curriculum resources and improving assessment methods.
Motor imagery electroencephalography (EEG) decoding is an essential part of brain-computer interfaces (BCIs) which help motor-disabled patients to communicate with the outside world by external ...devices. Recently, deep learning algorithms using decomposed spectrums of EEG as inputs may omit important spatial dependencies and different temporal scale information, thus generated the poor decoding performance. In this paper, we propose an end-to-end EEG decoding framework, which employs raw multi-channel EEG as inputs, to boost decoding accuracy by the channel-projection mixed-scale convolutional neural network (CP-MixedNet) aided by amplitude-perturbation data augmentation. Specifically, the first block in CP-MixedNet is designed to learn primary spatial and temporal representations from EEG signals. The mixed-scale convolutional block is then used to capture mixed-scale temporal information, which effectively reduces the number of training parameters when expanding reception fields of the network. Finally, based on the features extracted in previous blocks, the classification block is constructed to classify EEG tasks. The experiments are implemented on two public EEG datasets (BCI competition IV 2a and High gamma dataset) to validate the effectiveness of the proposed approach compared to the state-of-the-art methods. The competitive results demonstrate that our proposed method is a promising solution to improve the decoding performance of motor imagery BCIs.
β-type titanium (Ti) alloys have attracted a lot of attention as novel biomedical materials in the past decades due to their low elastic moduli and good biocompatibility. This article provides a ...broad and extensive review of β-type Ti alloys in terms of alloy design, preparation methods, mechanical properties, corrosion behavior, and biocompatibility. After briefly introducing the development of Ti and Ti alloys for biomedical applications, this article reviews the design of β-type Ti alloys from the perspective of the molybdenum equivalency (Moeq) method and DV-Xα molecular orbital method. Based on these methods, a considerable number of β-type Ti alloys are developed. Although β-type Ti alloys have lower elastic moduli compared with other types of Ti alloys, they still possess higher elastic moduli than human bones. Therefore, porous β-type Ti alloys with declined elastic modulus have been developed by some preparation methods, such as powder metallurgy, additive manufacture and so on. As reviewed, β-type Ti alloys have comparable or even better mechanical properties, corrosion behavior, and biocompatibility compared with other types of Ti alloys. Hence, β-type Ti alloys are the more suitable materials used as implant materials. However, there are still some problems with β-type Ti alloys, such as biological inertness. As such, summarizing the findings from the current literature, suggestions forβ-type Ti alloys with bioactive coatings are proposed for the future development.
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