An Online Ensemble Coupled Data Assimilation Capability for the Community Earth System Model: System Design and Evaluation

The Community Earth System Model (CESM) developed at the National Center of Atmospheric Research (NCAR) has been used worldwide for climate studies. This study extends the efforts of CESM development to include an online (i.e., in-core) ensemble coupled data assimilation system (CESM-ECDA) to enhance CESM’s capability for climate predictability studies and prediction applications. The CESM-ECDA system consists of an online atmospheric data assimilation (ADA) component implemented to both the finite-volume and spectral-element dynamical cores, and an online oceanic data assimilation (ODA) component. In ADA, surface pressures (Ps) are assimilated, while in ODA, gridded sea surface temperature (SST) and ocean temperature and salinity profiles at real Argo locations are assimilated. The system has been evaluated within a perfect twin experiment framework, showing significantly reduced errors of the model atmosphere and ocean states through “observation”-constraints by ADA and ODA. The weakly CDA in which both the online ADA and ODA are conducted during the coupled model integration shows smaller errors of air-sea fluxes than the single ADA and ODA, facilitating the future utilization of cross-covariance between the atmosphere and ocean at the air-sea interface. A three-year CDA reanalysis experiment is also implemented by assimilating Ps, SST and ocean temperature and salinity profiles from the real world spanning the period 1978 to 1980 using 12 ensemble members. Results show that Ps RMSE is smaller than 20CR and SST RMSE is better than ERA-20C and close to CFSR. The success of the online CESM-ECDA system is the first step to implement a high-resolution long-term climate reanalysis once the algorithm efficiency is much improved.

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.

Cosmological parameters from the likelihood analysis of the galaxy power spectrum and bispectrum in real space

We present a joint likelihood analysis of the halo power spectrum and bispectrum in real space. We take advantage of a large set of numerical simulations and of an even larger set of halo mock catalogs to provide a robust estimate of the covariance properties. We derive constraints on bias and cosmological parameters assuming a theoretical model from perturbation theory at one-loop for the power spectrum and tree-level for the bispectrum. By means of the Deviance Information Criterion, we select a reference bias model dependent on seven parameters that can describe the data up to k max,P = 0.3 h Mpc-1 for the power spectrum and k max,B = 0.09 h Mpc-1 for the bispectrum at redshift z = 1. This model is able to accurately recover three selected cosmological parameters even for the rather extreme total simulation volume of 1000h -3 Gpc3. With the same tools, we study how relations among bias parameters can improve the fit while reducing the parameter space. In addition, we compare common approximations to the covariance matrix against the full covariance estimated from the mocks, and quantify the (non-negligible) effect of ignoring the cross-covariance between the two statistics. Finally, we explore different selection criteria for the triangular configurations to include in the analysis, showing that excluding nearly equilateral triangles rather than simply imposing a fixed maximum k max,B on all triangle sides can lead to a better exploitation of the information contained in the bispectrum.

Cross Subaperture Averaging Generalized Sidelobe Canceler Beamforming Applied to Medical Ultrasound Imaging

For adaptive ultrasound imaging, a reliable estimation of the covariance matrix has a decisive influence on the performance of beamformers. In this paper, we propose a new cross subaperture averaging generalized sidelobe canceler approach (GSC-CROSS) for medical ultrasound imaging, which uses the cross-covariance matrix instead of the traditional covariance matrix estimation. By using the more stable and accurate estimation of the covariance matrix, GSC-CROSS performs well in both lateral resolution and contrast. Experiments are conducted based on the simulated echo data of scattering points and a cyst target. Beamforming responses of scattering points show that GSC-CROSS can improve the lateral resolution by 76.9%, 68.8%, and 17.1% compared with delay-and-sum (DS), synthetic aperture (SA), and the traditional generalized sidelobe canceler (GSC), respectively. Also, imaging of the cyst target shows that compared with DS, SA, and GSC, the contrast increases by 101%, 32.6%, and 63.5%, respectively. Finally, the actual echo data collected from a medical ultrasonic imaging system is applied to reconstruct the image. Results show that the proposed method has a good performance on lateral resolution and contrast. Both the simulated and experimental data demonstrate the effectiveness of the proposed method.

Lagged Covariance and Cross-Covariance Operators of Processes in Cartesian Products of Abstract Hilbert Spaces

A major task in Functional Time Series Analysis is measuring the dependence within and between processes, for which lagged covariance and cross-covariance operators have proven to be a practical tool in well-established spaces. This article deduces estimators and asymptotic upper bounds of the estimation errors for lagged covariance and cross-covariance operators of processes in Cartesian products of abstract Hilbert spaces for fixed and increasing lag and Cartesian powers. We allow the processes to be non-centered, and to have values in different spaces when investigating the dependence between processes. Also, we discuss features of estimators for the principle components of our covariance operators.

Reorganizations of latency structure within white matter from wakefulness to sleep

Previous studies based on resting-state fMRI (rsfMRI) data have revealed the existence of highly reproducible latency structure, reflecting the propagation of BOLD fMRI signals, in white matter (WM). Here, based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data collected from 35 healthy subjects who were instructed to sleep, we explored the alterations of propagations in WM across wakefulness and nonrapid eye movement (NREM) sleep stages. Lagged cross-covariance was computed among voxel-wise time series, followed by parabolic interpolation to determine the actual latency value in-between. In WM, regions including cerebellar peduncle, internal capsule, posterior thalamic radiation, genu of corpus callosum, and corona radiata, were found to change their temporal roles drastically, as revealed by applying linear mixed-effect model on voxel-wise latency projections across wakefulness and NREM sleep stages. Using these regions as seeds, further seed-based latency analysis revealed that variations of latency projections across different stages were underlain by inconsistent temporal shifts between each seed and the remaining part of WM. Finally, latency analysis on resting-state networks (RSNs), obtained by applying k-means clustering technique on group-level functional connectivity matrix, identified a path of signal propagations similar to previous findings in EEG during wakefulness, which propagated mainly from the brainstem upward to internal capsule and further to corona radiata. This path showed inter-RSN temporal reorganizations depending on the paired stages between which the brain transitioned, e.g., it changed, between internal capsule and corona radiata, from mainly unidirectional to clearly reciprocal when the brain transitioned from wakefulness to N3 stage. These findings suggested the functional role of BOLD signals in white matter as a slow process, dynamically modulated across wakefulness and NREM sleep stages, and involving in maintaining different levels of consciousness and cognitive processes.

A New Pseudolinear Filter for Bearings-Only Tracking without Requirement of Bias Compensation

In bearings-only tracking systems, the pseudolinear Kalman filter (PLKF) has advantages in stability and computational complexity, but suffers from correlation problems. Existing solutions require bias compensation to reduce the correlation between the pseudomeasurement matrix and pseudolinear noise, but incomplete compensation may cause a loss of estimation accuracy. In this paper, a new pseudolinear filter is proposed under the minimum mean square error (MMSE) framework without requirement of bias compensation. The pseudolinear state-space model of bearings-only tracking is first developed. The correlation between the pseudomeasurement matrix and pseudolinear noise is thoroughly analyzed. By splitting the bearing noise term from the pseudomeasurement matrix and performing some algebraic manipulations, their cross-covariance can be calculated and incorporated into the filtering process to account for their effects on estimation. The target state estimation and its associated covariance can then be updated according to the MMSE update equation. The new pseudolinear filter has a stable performance and low computational complexity and handles the correlation problem implicitly under a unified MMSE framework, thus avoiding the severe bias problem of the PLKF. The posterior Cramer–Rao Lower Bound (PCRLB) for target state estimation is presented. Simulations are conducted to demonstrate the effectiveness of the proposed method.

An Improved Extended Kalman Filter for Radar Tracking of Satellite Trajectories

Nonlinear state estimation problem is an important and complex topic, especially for real-time applications with a highly nonlinear environment. This scenario concerns most aerospace applications, including satellite trajectories, whose high standards demand methods with matching performances. A very well-known framework to deal with state estimation is the Kalman Filters algorithms, whose success in engineering applications is mostly due to the Extended Kalman Filter (EKF). Despite its popularity, the EKF presents several limitations, such as exhibiting poor convergence, erratic behaviors or even inadequate linearization when applied to highly nonlinear systems. To address those limitations, this paper suggests an improved Extended Kalman Filter (iEKF), where a new Jacobian matrix expansion point is recommended and a Frobenius norm of the cross-covariance matrix is suggested as a correction factor for the a priori estimates. The core idea is to maintain the EKF structure and simplicity but improve its accuracy. In this paper, two case studies are presented to endorse the proposed iEKF. In both case studies, the classic EKF and iEKF are implemented, and the obtained results are compared to show the performance improvement of the state estimation by the iEKF.

Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles

Antioxidant proteins (AOPs) play important roles in the management and prevention of several human diseases due to their ability to neutralize excess free radicals. However, the identification of AOPs by using wet-lab experimental techniques is often time-consuming and expensive. In this study, we proposed an accurate computational model, called AOP-HMM, to predict AOPs by extracting discriminatory evolutionary features from hidden Markov model (HMM) profiles. First, auto cross-covariance (ACC) variables were applied to transform the HMM profiles into fixed-length feature vectors. Then, we performed the analysis of variance (ANOVA) method to reduce the dimensionality of the raw feature space. Finally, a support vector machine (SVM) classifier was adopted to conduct the prediction of AOPs. To comprehensively evaluate the performance of the proposed AOP-HMM model, the 10-fold cross-validation (CV), the jackknife CV, and the independent test were carried out on two widely used benchmark datasets. The experimental results demonstrated that AOP-HMM outperformed most of the existing methods and could be used to quickly annotate AOPs and guide the experimental process.