Establishment of an optimized CTC detection model consisting of EpCAM,MUC1 and WT1 in epithelial ovarian cancer and its correlation with clinical characteristics

Objective:Emerging studies have demonstrated the promising clinical value of circulating tumor cells(CTCs)for diagnosis,disease assessment,treatment monitoring and prognosis in epithelial ovarian cancer.However,the clinical application of CTC remains restricted due to diverse detection techniques with variable sensitivity and specificity and a lack of common standards.Methods:We enrolled 160 patients with epithelial ovarian cancer as the experimental group,and 90 patients including 50 patients with benign ovarian tumor and 40 healthy females as the control group.We enriched CTCs with immunomagnetic beads targeting two epithelial cell surface antigens(EpCAM and MUC1),and used multiple reverse transcription-polymerase chain reaction(RT-PCR)detecting three markers(EpCAM,MUC1 and WT1)for quantification.And then we used a binary logistic regression analysis and focused on EpCAM,MUC1 and WT1 to establish an optimized CTC detection model.Results:The sensitivity and specificity of the optimized model is 79.4%and 92.2%,respectively.The specificity of the CTC detection model is significantly higher than CA125(92.2%vs.82.2%,P=0.044),and the detection rate of CTCs was higher than the positive rate of CA125(74.5%vs.58.2%,P=0.069)in early-stage patients(stage I and II).The detection rate of CTCs was significantly higher in patients with ascitic volume≥500 mL,suboptimal cytoreductive surgery and elevated serum CA125 level after 2 courses of chemotherapy(P<0.05).The detection rate of CTC;and CTC;was significantly higher in chemo-resistant patients(26.3%vs.11.9%;26.4%vs.13.4%,P<0.05).The median progression-free survival time for CTC;patients trended to be longer than CTC;patients,and overall survival was shorter in CTC;patients(P=0.043).Conclusions:Our study presents an optimized detection model for CTCs,which consists of the expression levels of three markers(EpCAM,MUC1 and WT1).In comparison with CA125,our model has high specificity and demonstrates better diagnostic values,especially for early-stage ovarian cancer.Detection of CTC;and CTC;had predictive value for chemotherapy resistance,and the detection of CTC;suggested poor prognosis.

Detection optimization for resonance region radar with dense multi-carrier waveform

Unlike the existing resonance region radar systems （RRRS ） that transmit the orthogonal frequency division multiplexing （OFDM）multi-carrier waveform,the dense multi-carrier （DMC）radar waveform which has a narrower frequency interval than the traditional OFDM waveform is proposed.Therefore,in the same frequency bandwidth,the DMC waveform contains more sub-carriers and provides more frequency diversity.Additionally,to further improve detection performance,a novel optimal weight accumulation target detection （OWATD）method is proposed,where the echo electromagnetic waves at different frequencies are accumulated with the optimal weight coefficients.Then,with the signal-to-noise ratio （SNR）of echo waveform approaching infinity,the asymptotic detection performance is analyzed, and the condition that the OWATD method with the DMC outperforms the matched filter with the OFDM is presented.Simulation results show that the DMC outperforms the OFDM in the target detection performance,and the OWATD method can further improve the detection performance of the traditional methods with both the OFDM and DMC radar waveform.

Fitness Landscape Analysis for Optimum Multiuser Detection Problem

Optimum multiuser detection （OMD） for CDMA systems is an NP-complete combinatorial optimization problem. Fitness landscape has been proven to be very useful for understanding the behavior of combinatorial optimization algorithms and can help in predicting their performance. This paper analyzes the statistic properties of the fitness landscape of the OMD problem by performing autocorrelation analysis, fitness distance correlation test and epistasis measure. The analysis results explain why some random search algorithms are effective methods for OMD problem and give hints how to design more efficient randomized search heuristic algorithms for OMD.

Optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing in cognitive radio network

In order to improve the throughput of cognitive radio(CR), optimization of sensing time and cooperative user allocation for OR-rule cooperative spectrum sensing was investigated in a CR network that includes multiple users and one fusion center. The frame structure of cooperative spectrum sensing was divided into multiple transmission time slots and one sensing time slot consisting of local energy detection and cooperative overhead. An optimization problem was formulated to maximize the throughput of CR network, subject to the constraints of both false alarm probability and detection probability. A joint optimization algorithm of sensing time and number of users was proposed to solve this optimization problem with low time complexity. An allocation algorithm of cooperative users was proposed to preferentially allocate the users to the channels with high utilization probability. The simulation results show that the significant improvement on the throughput can be achieved through the proposed joint optimization and allocation algorithms.

Statistical Procedures for Estimating and Detecting Climate Changes

This paper provides a concise description of the philosophy, mathematics, and algorithms for estimating, detecting, and attributing climate changes. The estimation follows the spectral method by using empirical orthogonal functions, also called the method of reduced space optimal averaging. The detection follows the linear regression method, which can be found in most textbooks about multivariate statistical techniques. The detection algorithms are described by using the space-time approach to avoid the non-stationarity problem. The paper includes （1） the optimal averaging method for minimizing the uncertainties of the global change estimate, （2） the weighted least square detection of both single and multiple signals, （3） numerical examples, and （4） the limitations of the linear optimal averaging and detection methods.

BER Performance Analysis of Asynchronous NOMA with Arbitrary Phase Offset

An asynchronous transmission scenario for non-orthogonal multiple access(NOMA)user signals with arbitrary phase offset is investigated in this paper.To improve the system performance in the user power-balanced conditions,we adopt a synthetic detection method at the receiver,i.e.,the jointly optimal maximal likelihood detection aided triangular successive interference cancellation(JO ML-TSIC)method.Analytical bit error rate(BER)solutions are obtained for a two-user case with the optimal,intentional onehalf symbol period time delay implemented between the user signals.Furthermore,closed-form BER solutions for the case using the triangular successive interference cancellation(TSIC)detection method are also derived for comparisons.Numerical results show that the JO ML-TSIC receiver for the asynchronous system outperforms the TSIC receiver as well as the synchronous successive interference cancellation(SIC)receiver in all the conditions concerned.The results also show that the superiority of the JO ML-TSIC receiver is strengthened when the signals experience flat Rayleigh fading channels compared to the TSIC and the synchronous SIC receivers.

Polarization-based optimal detection scheme for digital self-interference cancellation in full-duplex system

In order to detect and cancel the self-interference(SI)signal from desired binary phase-shift keying(BPSK)signal,the polarization-based optimal detection(POD)scheme for cancellation of digital SI in a full-duplex(FD)system is proposed.The POD scheme exploits the polarization domain to isolate the desired signal from the SI signal and then cancel the SI to obtain the interference-free desired signal at the receiver.In FD communication,after antenna and analog cancellation,the receiver still contains residual SI due to non-linearities of hardware imperfections.In POD scheme,a likelihood ratio expression is obtained,which isolates and detects SI bits from the desired bits.After isolation of these signal points,the POD scheme cancels the residual SI.As compared to the conventional schemes,the proposed POD scheme gives significantly low bit error rate(BER),a clear constellation diagram to obtain the boundary between desired and SI signal points,and increases the receiver's SI cancellation performance in low signal to interference ratio(SIR)environment.

An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and group method of data handling surrogate model

In this study,the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been investigated.In the first stage,in order to locate the damage accurately,the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion matrix.In the second stage,to estimate the damage extent,the sensitivity of most used modal properties due to damage,such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy(MNMSE)of suspected damaged elements.Moreover,a modal property change vector is evaluated using the group method of data handling(GMDH)network as a surrogate model during damage extent estimation by optimization algorithm;in this part of methodology,the performance of the three popular optimization algorithms including particle swarm optimization(PSO),bat algorithm(BA),and colliding bodies optimization(CBO)is examined and in this regard,root mean square deviation(RMSD)based on the modal property change vector has been proposed as an objective function.Furthermore,the effect of noise in the measurement of structural responses by the sensors has also been studied.Finally,in order to achieve the most generalized neural network as a surrogate model,GMDH performance is compared with a properly trained cascade feed-forward neural network(CFNN)with log-sigmoid hidden layer transfer function.The results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and MNMSE.Moreover,the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model.

Optimization design of the angle detecting system used in the fast steering mirror

In this paper, in order to design a fast steering mirror(FSM) with large deflection angle and high linearity, a deflection angle detecting system(DADS) using quadrant detector(QD) is developed. And the mathematical model describing DADS is established by analyzing the principle of position detecting and error characteristics of QD. Based on this mathematical model, the variation tendencies of deflection angle and linearity of FSM are simulated. Then, by changing the parameters of the DADS, the optimization of deflection angle and linearity of FSM is demonstrated. Finally, a QD-based FSM is designed based on this method, which achieves ±2° deflection angle and 0.72% and 0.68% linearity along x and y axis, respectively. Moreover, this method will be beneficial to the design of large deflection angle and high linearity FSM.