Deep learning neural network incorporating surface enhancement Raman scattering technique(SERS)is becoming as a powerful tool for the precise classifications and diagnosis of bacterial infections.However,the large amount of sample requirement and time-consuming sample collection severely hinder its applications.We herein propose a spectral concatenation strategy for residual neural network using nonspecific and specific SERS spectra for the training data augmentation,which is accessible to acquiring larger training dataset with same number of SERS spectra or same size of training dataset with fewer SERS spectra,compared with pure non-specific SERS spectra.With this strategy,the training loss exhibit rapid convergence,and an average accuracy up to 100%in bacteria classifications was achieved with50 SERS spectra for each kind of bacterium;even reduced to 20 SERS spectra per kind of bacterium,classification accuracy is still>95%,demonstrating marked advantage over the results without spectra concatenation.This method can markedly improve the classification accuracy under fewer samples and reduce the data collection workload,and can evidently enhance the performance when used in different machine learning models with high generalization ability.Therefore,this strategy is beneficial for rapid and accurate bacteria classifications with residual neural network.
目的:近年来研究发现中性粒细胞与淋巴细胞比值可以预测急性缺血性卒中(AIS)患者的短期和长期预后。但其变化量(ΔNLR)与患者溶栓后24 h的出血转化(HT)及早期神经功能改善(ENI)的关系仍缺乏研究。本次研究旨在探讨ΔNLR与溶栓后24 h的HT和ENI的关系,及ΔNLR与不同TOAST分型的患者溶栓后早期结局的关系。方法:纳入接受静脉溶栓治疗的AIS患者234例。对患者进行TOAST分型。根据HT和ENI的有无将患者进行分组。结果:发生HT的患者溶栓后NLR升高更明显,出现ENI的患者溶栓后NLR降低更明显。动脉粥样硬化型与心源性栓塞型患者的ΔNLR无明显差异,但小动脉闭塞型患者与大动脉粥样硬化型轻型患者相比,ΔNLR有统计学差异。结论:ΔNLR是AIS患者溶栓后HT和ENI的独立影响因素,且小动脉闭塞型脑卒中患者的ΔNLR大于轻型大动脉粥样硬化型脑卒中患者的ΔNLR。Objective: Recent studies have shown that the neutrophil-to-lymphocyte ratio can predict the short- and long-term prognosis of patients with acute ischemic stroke (AIS). However, the association between the variation of neutrophil-to- lymphocyte ratio(ΔNLR) and the bleeding transformation (HT) as well as early neurological improvement (ENI) at 24 h after thrombolysis is still lacking. The purpose of the study was to investigate the correlation between ΔNLR and HT as well as ENI 24 h after thrombolytic therapy, and the relationship between ΔNLR and early outcomes after thrombolytic therapy in patients with different TOAST classifications. Methods: 234 patients with AIS who received intravenous thrombolysis were included. The patients were classified by TOAST classifications. Patients were grouped according to the presence or absence of HT and ENI. Results: The increase of NLR after thrombolysis was more obvious in patients with HT while the decrease of NLR was more obvious in patients with ENI. There was no significant difference in ΔNLR between patients with at
This article,referencing to the historical documents,focuses on the various kinds of Bonshen(bon gshen in Tibetan)found in the historical process,analyzes the roles played by the Bonshen in supporting government,assisting in military affairs,and conducting folk rituals.It is found that there are three categories of Bonshen,namely,Kushen(sku gshen in Tibetan),Makshen(dmag gshen in Tibetan),and Bonshen.The position and functions of these three categories are discussed.
The direction-of-arrival(DoA) estimation is one of the hot research areas in signal processing. To overcome the DoA estimation challenge without the prior information about signal sources number and multipath number in millimeter wave system,the multi-task deep residual shrinkage network(MTDRSN) and transfer learning-based convolutional neural network(TCNN), namely MDTCNet, are proposed. The sampling covariance matrix based on the received signal is used as the input to the proposed network. A DRSN-based multi-task classifications model is first introduced to estimate signal sources number and multipath number simultaneously. Then, the DoAs with multi-signal and multipath are estimated by the regression model. The proposed CNN is applied for DoAs estimation with the predicted number of signal sources and paths. Furthermore, the modelbased transfer learning is also introduced into the regression model. The TCNN inherits the partial network parameters of the already formed optimization model obtained by the CNN. A series of experimental results show that the MDTCNet-based DoAs estimation method can accurately predict the signal sources number and multipath number under a range of signal-to-noise ratios. Remarkably, the proposed method achieves the lower root mean square error compared with some existing deep learning-based and traditional methods.
