Risk Factors of Depression Screened by Two-Sample Mendelian Randomization Analysis:A Systematic Review

Objective This study explored the potentially modifiable factors for depression and major depressive disorder(MDD)from the MR-Base database and further evaluated the associations between drug targets with MDD.Methods We analyzed two-sample of Mendelian randomization(2SMR)using genetic variant depression(n=113,154)and MDD(n=208,811)from Genome-Wide Association Studies(GWAS).Separate calculations were performed with modifiable risk factors from MR-Base for 1,001 genomes.The MR analysis was performed by screening drug targets with MDD in the DrugBank database to explore the therapeutic targets for MDD.Inverse variance weighted(IVW),fixed-effect inverse variance weighted(FE-IVW),MR-Egger,weighted median,and weighted mode were used for complementary calculation.Results The potential causal relationship between modifiable risk factors and depression contained 459 results for depression and 424 for MDD.Also,the associations between drug targets and MDD showed that SLC6A4,GRIN2A,GRIN2C,SCN10A,and IL1B expression are associated with an increased risk of depression.In contrast,ADRB1,CHRNA3,HTR3A,GSTP1,and GABRG2 genes are candidate protective factors against depression.Conclusion This study identified the risk factors causally associated with depression and MDD,and estimated 10 drug targets with significant impact on MDD,providing essential information for formulating strategies to prevent and treat depression.

Precise calibration of zero-crossing temperature and drift of an ultralow expansion cavity with a clock transition spectrum

We report a clock transition spectrum approach,which is used to calibrate the zero-crossing temperature and frequency drift of an ultralow expansion(ULE)cavity with a Hertz level resolution.With this approach,the linear and nonlinear drifts of the ULE cavity along a variety of controlled temperatures are clearly presented.When the controlled temperature of ULE cavity is tuned away from the zero-crossing temperature of the ULE cavity,the cavity shows larger and larger nonlinear drift.According to our theoretical analysis and experimental results,we investigate more details of the drift property of the ULE cavity around the zero-crossing temperature,which has seldom been explored before.We can definitely conclude that the zero-crossing temperature of our ULE cavity used in an ytterbium(Yb)lattice clock is around 31.7℃.

A Statistical Power Comparison of the Kolmogorov-Smirnov Two-Sample Test and the Wald Wolfowitz Test in Terms of Fixed Skewness and Fixed Kurtosis in Large Sample Sizes

In this study, the statistical powers of Kolmogorov-Smimov two-sample （KS-2） and Wald Wolfowitz （WW） tests, non-parametric tests used in testing data from two independent samples, have been compared in terms of fixed skewness and fixed kurtosis by means of Monte Carlo simulation. This comparison has been made when the ratio of variance is two as well as with equal and different sample sizes for large sample volumes. The sample used in the study is： （25, 25）, （25, 50）, （25, 75）, （25, 100）, （50, 25）, （50, 50）, （50, 75）, （50, 100）, （75, 25）, （75, 50）, （75, 75）, （75, 100）, （100, 25）, （100, 50）, （100, 75）, and （100, 100）. According to the results of the study, it has been observed that the statistical power of both tests decreases when the coefficient of kurtosis is held fixed and the coefficient of skewness is reduced while it increases when the coefficient of skewness is held fixed and the coefficient of kurtosis is reduced. When the ratio of skewness is reduced in the case of fixed kurtosis, the WW test is stronger in sample volumes （25, 25）, （25, 50）, （25, 75）, （25, 100）, （50, 75）, and （50, 100） while KS-2 test is stronger in other sample volumes. When the ratio of kurtosis is reduced in the case of fixed skewness, the statistical power of WW test is stronger in volume samples （25, 25）, （25, 75）, （25, 100）, and （75, 25） while KS-2 test is stronger in other sample volumes.

Tests for Two-Sample Location Problem Based on Subsample Quantiles

This paper presents a new class of test procedures for two-sample location problem based on subsample quantiles. The class includes Mann-Whitney test as a special case. The asymptotic normality of the class of tests proposed is established. The asymptotic relative performance of the proposed class of test with respect to the optimal member of Xie and Priebe (2000) is studied in terms of Pitman efficiency for various underlying distributions.

A robust feature extraction approach based on an auditory model for classification of speech and expressiveness

Based on an auditory model, the zero-crossings with maximal Teager energy operator (ZCMT) feature extraction approach was described, and then applied to speech and emotion recognition. Three kinds of experiments were carried out. The first kind consists of isolated word recognition experiments in neutral (non-emotional) speech. The results show that the ZCMT approach effectively improves the recognition accuracy by 3.47% in average compared with the Teager energy operator (TEO). Thus, ZCMT feature can be considered as a noise-robust feature for speech recognition. The second kind consists of mono-lingual emotion recognition experiments by using the Taiyuan University of Technology (TYUT) and the Berlin databases. As the average recognition rate of ZCMT approach is 82.19%, the results indicate that the ZCMT features can characterize speech emotions in an effective way. The third kind consists of cross-lingual experiments with three languages. As the accuracy of ZCMT approach only reduced by 1.45%, the results indicate that the ZCMT features can characterize emotions in a language independent way.

Detecting dynamical complexity changes in time series using the base-scale entropy

Timely detection of dynamical complexity changes in natural and man-made systems has deep scientific and practical meanings. We introduce a complexity measure for time series： the base-scale entropy. The definition directly applies to arbitrary real-word data. We illustrate our method on a practical speech signal and in a theoretical chaotic system. The results show that the simple and easily calculated measure of base-scale entropy can be effectively used to detect qualitative and quantitative dynamical changes.

Adaptive bands filter bank optimized by genetic algorithm for robust speech recognition system

Perceptual auditory filter banks such as Bark-scale filter bank are widely used as front-end processing in speech recognition systems.However,the problem of the design of optimized filter banks that provide higher accuracy in recognition tasks is still open.Owing to spectral analysis in feature extraction,an adaptive bands filter bank (ABFB) is presented.The design adopts flexible bandwidths and center frequencies for the frequency responses of the filters and utilizes genetic algorithm (GA) to optimize the design parameters.The optimization process is realized by combining the front-end filter bank with the back-end recognition network in the performance evaluation loop.The deployment of ABFB together with zero-crossing peak amplitude (ZCPA) feature as a front process for radial basis function (RBF) system shows significant improvement in robustness compared with the Bark-scale filter bank.In ABFB,several sub-bands are still more concentrated toward lower frequency but their exact locations are determined by the performance rather than the perceptual criteria.For the ease of optimization,only symmetrical bands are considered here,which still provide satisfactory results.

Estimation of Wind Wave Frequency Spectra by Use of the Arcsine Law

In the present study, the surface elevation of wind waves observed in laboratory and in the Bohai Sea are adopted for the estimation of the wind wave frequency spectrtm by use of the method of the arcsine law （MAL）. The traditional method uses the surface elevation to calculate the correlation and then estimate the frequency spectrum while the MAL, presented by Yu and l.an （1979）, uses the time sequence of zero-crossing points of surface elevation rather than directly the surface elevation to calculate the correlation. 66 sets of wind wave data obtained in laboratory and 420 sets of data observed in the Bohai Sea are adopted for the examination of the method introduced by Yu and Lan. Results show that the MAL can give reliable estimation of wind wave spectra. Correlation and form of spectra estimated by the MAL are similar to those estimated by the traditional method. The peak frequency and the spectral density in peak frequency by the MAL are close to those obtained by the traditional method.

Relative energy zero ratio-based approach for identifying pulse-like ground motions

Pulse-like ground motions are capable of inflicting significant damage to structures. Efficient classification of pulse-like ground motion is of great importance when performing the seismic assessment in near-fault regions. In this study, a new method for identifying the velocity pulses is proposed, based on different trends of two parameters: the short-time energy and the short-time zero crossing rate of a ground motion record. A new pulse indicator, the relative energy zero ratio(REZR), is defined to qualitatively identify pulse-like features. The threshold for pulse-like ground motions is derived and compared with two other identification methods through statistical analysis. The proposed procedure not only shows good accuracy and efficiency when identifying pulse-like ground motions but also exhibits good performance for classifying records with high-frequency noise and discontinuous pulses. The REZR method does not require a waveform formula to express and fit the potential velocity pulses;it is a purely signal-based classification method. Finally, the proposed procedure is used to evaluate the contribution of pulse-like motions to the total input energy of a seismic record, which dramatically increases the seismic damage potential.

Prosthetic Leg Locomotion-Mode Identification Based on High-Order Zero-Crossing Analysis Surface Electromyography

The research purpose was to improve the accuracy in identifying the prosthetic leg locomotion mode.Surface electromyography(sEMG)combined with high-order zero-crossing was used to identify the prosthetic leg locomotion modes.sEMG signals recorded from residual thigh muscles were chosen as inputs to pattern classifier for locomotion-mode identification.High-order zero-crossing were computed as the sEMG features regarding locomotion modes.Relevance vector machine(RVM)classifier was investigated.Bat algorithm(BA)was used to compute the RVM classifier kernel function parameters.The classification performance of the particle swarm optimization-relevance vector machine(PSO-RVM)and RVM classifiers was compared.The BA-RVM produced lower classification error in sEMG pattern recognition for the transtibial amputees over a variety of locomotion modes:upslope,downgrade,level-ground walking and stair ascent/descent.

A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering

User-transformer relations are significant to electric power marketing,power supply safety,and line loss calculations.To get accurate user-transformer relations,this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization(QPSO)and Fuzzy C-Means Clustering.The main idea is:as energymeters at different transformer areas exhibit different zero-crossing shift features,we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations.The proposed method contributes in three main ways.First,based on the fuzzy C-means clustering algorithm(FCM),the quantum particle swarm optimization(PSO)is introduced to optimize the FCM clustering center and kernel parameters.The optimized FCM algorithm can improve clustering accuracy and efficiency.Since easily falls into a local optimum,an improved PSO optimization algorithm(IQPSO)is proposed.Secondly,considering that traditional FCM cannot solve the linear inseparability problem,this article uses a FCM(KFCM)that introduces kernel functions.Combinedwith the IQPSOoptimization algorithm used in the previous step,the IQPSO-KFCM algorithm is proposed.Simulation experiments verify the superiority of the proposed method.Finally,the proposed method is applied to transformer detection.The proposed method determines the class members of transformers and meters in the actual transformer area,and obtains results consistent with actual user-transformer relations.This fully shows that the proposed method has practical application value.

EDGE LOCALIZATION BY MULTIPLE OF GAUSSIANs THEORY

Zero-crossing of a derivative of Gaussian filter is a well-known edge location criterion. Examples are the Laplacian, the second derivative in the gradient direction (SDGD) and the sum of the Laplacian and SDGD (PLUS). Derivative operators can easily be implemented by convoluting the primitive image with a derivative of a Gaussian. Gaussian filter displaces the equipotential of half height inwards for convex edge and outwards for concave edges. A Difference-of-Gaussian (DoG) filter is similar to the Laplacian-of-Gaussian but with opposite sign and causes a convex edge shift inwards. This paper introduces the Multiple-of-Gaussian niters to reduce curvature-based location error. Using a linear combination of N Gaussians(N】2) with proper weights, the edge shifts can be reduced to 1/(2N-3) of the ones produced by a similar Laplacian-of-Gaussian filter.

A Comparative Study of the Statistical Distributions of Wave Heights

Distribution of wave heights and surface elevations of wind-driven waves are studied. Records of surface elevations obtained from both field observations and laboratory measurements are analyzed. Wave heights can be approximated by normal, two-parameter Weibull, and/or Rayleigh distribution. However, while the first two models may have almost equal probabilities to fit measured data quite satisfactorily, the Rayleigh distribution does not appear to be a good model for the majority of the cases studied. Surface elevations from field data are well described by the Gaussian model, but as with increasing wind speeds, water surface in a wind-wave flume deviates from normality, and the Edgeworth/s form of the type A Gram-Charlier series is then applied.

A comparison of n-γ discrimination by the rise-time and zero-crossing methods

The n-γ discrimination performance of two experimental arrangements based on the rise-time method and the zero-crossing method was compared for a 50.8 mm-diametered and 50.8 mm-high BC501A liquid scintillator coupled to a 50.8 mm-diametered 9807B photomultiplier in this work. The low energy limitation of the detected neutron with different detector high voltages and the figure of merit of the n-γ discrimination in four neutron energy regions (1–2 MeV, 0.75–1 MeV, 0.5–0.75 MeV and below 0.5 MeV) were studied by using the Am-Be neutron source. Under a time statistical model of the photoelectron emission process in scintillation counters, the intrinsic capability of the n-γ discrimination performance under the optimal condition was evaluated. The experimental results of the zero-crossing method demonstrate a better n-γ. discrimination performance than those of the rise-time method, which is consistent with the calculated results.

Bayesian Prediction of Future Generalized Order Statistics from a Class of Finite Mixture Distributions

This article is concerned with the problem of prediction for the future generalized order statistics from a mixture of two general components based on doubly?type II censored sample. We consider the one sample prediction and two sample prediction techniques. Bayesian prediction intervals for the median of future sample of generalized order statistics having odd and even sizes are obtained. Our results are specialized to ordinary order statistics and ordinary upper record values. A mixture of two Gompertz components model is given as an application. Numerical computations are given to illustrate the procedures.

Performance Comparison of Three Algorithms Applied to UM2000 Signal Demodulation

UM2000 signal is a type of multi-audio frequency-modulated signal which is widely used for railway blocking. Principles of three typical demodulating algorithms are presented in details in this paper. Bit error rates of the three methods at different SNRs are achieved by Monte Carlo simulation experiments. Among the three algorithms, the quadrature demodulation has the best performance at the real working environment. However, the three methods have the same problem of phase hopping when noise is too strong.

Inference on Constant-Partially Accelerated Life Tests for Mixture of Pareto Distributions under Progressive Type-II Censoring

The main purpose of this paper is to obtain the inference of parameters of heterogeneous population represented by finite mixture of two Pareto (MTP) distributions of the second kind. The constant-partially accelerated life tests are applied based on progressively type-II censored samples. The maximum likelihood estimates (MLEs) for the considered parameters are obtained by solving the likelihood equations of the model parameters numerically. The Bayes estimators are obtained by using Markov chain Monte Carlo algorithm under the balanced squared error loss function. Based on Monte Carlo simulation, Bayes estimators are compared with their corresponding maximum likelihood estimators. The two-sample prediction technique is considered to derive Bayesian prediction bounds for future order statistics based on progressively type-II censored informative samples obtained from constant-partially accelerated life testing models. The informative and future samples are assumed to be obtained from the same population. The coverage probabilities and the average interval lengths of the confidence intervals are computed via a Monte Carlo simulation to investigate the procedure of the prediction intervals. Analysis of a simulated data set has also been presented for illustrative purposes. Finally, comparisons are made between Bayesian and maximum likelihood estimators via a Monte Carlo simulation study.

Bayes Prediction of Future Observables from Exponentiated Populations with Fixed and Random Sample Size

Bayesian predictive probability density function is obtained when the underlying pop-ulation distribution is exponentiated and subjective prior is used. The corresponding predictive survival function is then obtained and used in constructing 100(1 – ?)% predictive interval, using one- and two- sample schemes when the size of the future sample is fixed and random. In the random case, the size of the future sample is assumed to follow the truncated Poisson distribution with parameter λ. Special attention is paid to the exponentiated Burr type XII population, from which the data are drawn. Two illustrative examples are given, one of which uses simulated data and the other uses data that represent the breaking strength of 64 single carbon fibers of length 10, found in Lawless [40].

SADDLEPOINT APPROXIMATIONS TO THETWO-SAMPLE PERMUTATION TESTS

A saddlepoint approximation for a two-sample permutation test was obtained by Robinson[7].Although the approximation is very accurate, the formula is very complicated and difficult toapply. In this papert we shall revisit the same problem from a different angle. We shall first turnthe problem into a conditional probability and then apply a Lugannani-Rice type formula to it,which was developed by Skovagard[8] for the mean of i.i.d. samples and by Jing and Robinson[5]for smooth function of vector means. Both the Lugannani-Rice type formula and Robinson'sformula achieve the same relative error of order O(n-3/2), but the former is very compact andmuch easier to use in practice. Some numerical results will be presented to compare the twoformulas.

Causal Relationship Between Gut Microbiota and Liver Cirrhosis:16S rRNA Sequencing and Mendelian Randomization Analyses

Background and Aims:Accumulating evidence highlights the association between the gut microbiota and liver cirrhosis.However,the role of the gut microbiota in liver cirrhosis remains unclear.Methods:We first assessed the differences in the composition of the bacterial community between CCl4-induced liver cirrhosis and control mice using 16S rRNA sequencing.We then performed a two-sample Mendelian randomization(MR)analysis to reveal the underlying causal relationship between the gut microbiota and liver cirrhosis.Causal relationships were analyzed using primary inverse variance weighting(IVW)and other supplemental MR methods.Furthermore,fecal samples from liver cirrhosis patients and healthy controls were collected to validate the results of the MR analysis.Results:Analysis of 16S rRNA sequencing indicated significant differences in gut microbiota composition between the cirrhosis and control groups.IVW analyses suggested that Alphaproteobacteria,Bacillales,NB1n,Rhodospirillales,Dorea,Lachnospiraceae,and Rhodospirillaceae were positively correlated with the risk of liver cirrhosis,whereas Butyricicoccus,Hungatella,Marvinbryantia,and Lactobacillaceae displayed the opposite effects.However,the weighted median and MR-PRESSO estimates further showed that only Butyricicoccus and Marvinbryantia presented stable negative associations with liver cirrhosis.No significant heterogeneity or horizontal pleiotropy was observed in the sensitivity analysis.Furthermore,the result of 16S rRNA sequencing also showed that healthy controls had a higher relative abundance of Butyricicoccus and Marvinbryantia than liver cirrhosis patients.Conclusions:Our study provides new causal evidence for the link between gut microbiota and liver cirrhosis,which may contribute to the discovery of novel strategies to prevent liver cirrhosis.