Review for Dynamic Prediction in Clinical Survival Analysis

The accurate prediction of patient prognosis is a critical challenge in clinical practice. With the availability of various patient information, physicians can optimize medical care by closely monitoring disease progression and therapy responses. To enable better individualized treatment, dynamic prediction models are required to continuously update survival probability predictions as new information becomes available. This article aims to offer a comprehensive survey of current methods in dynamic survival analysis, encompassing both classical statistical approaches and deep learning techniques. Additionally, it will also discuss the limitations of existing methods and the prospects for future advancements in this field.Comment: 8 pages. arXiv admin note: text overlap with arXiv:1303.2797 by other author

A Model for Analysis of Time-Varying Mesh Stiffness of Helical Gears with Misalignment Errors

A mathematical model is proposed to calculate the time-varying mesh stiffness (TVMS) of helical gears under the condition of gear misalignment by combining the slice method. The proposed method aims to reveal the influences of different misalignment errors (centre distance error, action plane error and off action plane error) on the TVMS of helical gears. The results show that the misalignment error on the plane of action has an enormous influence on mesh characteristics and that it not only changes the contact line and load distribution but also results in a reduced TVMS. Meanwhile, the centre distance error causes the amplitude fluctuation of TVMS and transmission error (TE). The misalignment error on the off plane of action has almost no effect on TVMS and TE. The results can be used for vibration prediction and misalignment fault diagnosis

Counterfactual time series analysis for the air pollution during the outbreak of COVID-19 in Wuhan

Environmental issues are becoming one of the main topics of concern for society, and the quality of air is closely linked to people's lives. Previous studies have examined the effects of abrupt interventions on changes in air pollution. For example, researchers used an interrupted time series design to quantify the impact of the 1990 Dublin coal ban; and a regression discontinuity to determine the arbitrary spatial impact of the Huaihe River policy in China. An important feature of each of these studies is that they investigated abrupt and localized changes over relatively short time spans (the Dublin coal ban) and spatial scales (the Huaihe policy). Due to the abrupt nature of these interventions, defining a hypothetical experiment in these studies is straightforward. In response to the novel coronavirus outbreak, China implemented 'the largest quarantine in human history' in Wuhan on January 23, 2020. Similar measures were implemented in other Chinese cities. Since then, the movement of people and associated production and consumption activities have been significantly reduced. This provides us with an unprecedented opportunity to estimate the changes in air pollution brought about by this sudden "silent" move. We speculate that the initiative will lead to a significant reduction in regional air pollution. Thus, we performed counterfactual time series analysis on Wuhan air quality data from 2017-2022 based on three models, SARIMA, LSTM and XGBOOST, and compared the excellence of different models. Finally, we conclude that 'silent' measures will significantly reduce air pollution. Using this conclusion to further investigate the extent of air pollution reduction will help the country to better designate environmental policies.Comment: 9 pages, 8 figure

Modal Analysis and an Experimental Study Into a Marine Gearbox Featuring Confluence Transmission

An approach to calculating vibration modal characteristics of a marine gear system featuring confluence transmission based on the theoretical and the experimental modal analysis is given in view of the fact that it is difficult to accurately determine the modal data of the system because of its complex vibration mechanism. Firstly, a dynamic finite element model of a coupled gear-rotor-bearing-housing system is developed by combining the gearbox transmission model with the gearbox housing model using the modal parameter identification data. Then, the modal frequency and the mode of vibration can be obtained. In fact, the proposed model can provide a faster approach to analysing the mode of the gear system vibration. Finally, experimental testing of the mode of vibration is performed on the experimental prototype to verify the rationality of the theoretical analysis. A comparison of the two sets of results shows that the experimental results are in good agreement with the computational results, with a maximum error of 6.3%

Over-expression of eukaryotic translation initiation factor 4 gamma 1 correlates with tumor progression and poor prognosis in nasopharyngeal carcinoma

<p>Abstract</p> <p>Background</p> <p>The aim of the present study was to analyze the expression of eukaryotic translation initiation factor 4 gamma 1 (<it>EIF4G1</it>) in nasopharyngeal carcinoma (NPC) and its correlation with clinicopathologic features, including patients' survival time.</p> <p>Methods</p> <p>Using real-time PCR, we detected the expression of <it>EIF4G1 </it>in normal nasopharyngeal tissues, immortalized nasopharyngeal epithelial cell lines NP69, NPC tissues and cell lines. <it>EIF4G1 </it>protein expression in NPC tissues was examined using immunohistochemistry. Survival analysis was performed using Kaplan-Meier method. The effect of <it>EIF4G1 </it>on cell invasion and tumorigenesis were investigated.</p> <p>Results</p> <p>The expression levels of <it>EIF4G1 </it>mRNA were significantly greater in NPC tissues and cell lines than those in the normal nasopharyngeal tissues and NP69 cells (<it>P </it>< 0.001). Immunohistochemical analysis revealed that the expression of <it>EIF4G1 </it>protein was higher in NPC tissues than that in the nasopharyngeal tissues (<it>P </it>< 0.001). In addition, the levels of <it>EIF4G1 </it>protein in tumors were positively correlated with tumor T classification (<it>P </it>= 0.039), lymph node involvement (N classification, <it>P </it>= 0.008), and the clinical stages (<it>P </it>= 0.003) of NPC patients. Patients with higher <it>EIF4G</it>1 expression had shorter overall survival time (<it>P </it>= 0.019). Multivariate analysis showed that <it>EIF4G1 </it>expression was an independent prognostic indicator for the overall survival of NPC patients. Using shRNA to knock down the expression of <it>EIF4G1 </it>not only markedly inhibited cell cycle progression, proliferation, migration, invasion, and colony formation, but also dramatically suppressed <it>in vivo </it>xenograft tumor growth.</p> <p>Conclusion</p> <p>Our data suggest that <it>EIF4G1 </it>can serve as a biomarker for the prognosis of NPC patients.</p

Wavelength dependence of electron localization in the laser-driven dissociation of H2+_2^+

We theoretically investigate the laser wavelength dependence of asymmetric dissociation of H$_2^+$. It is found that the electron localization in molecular dissociation is significantly manipulated by varying the wavelength of the driving field. Through creating a strong nuclear vibration in the laser-molecular interaction, our simulations demonstrate that the few-cycle mid-infrared pulse can effectively localize the electron at one of the dissociating nuclei with weak ionization. Moreover, we show that the observed phase-shift of the dissociation asymmetry is attributed to the different population transfers by the remaining fields after the internuclear distances reach the one-photon coupling point.Comment: 11 pages, 7 figure