ClearF++: Improved Supervised Feature Scoring Using Feature Clustering in Class-Wise Embedding and Reconstruction

Feature selection methods are essential for accurate disease classification and identifying informative biomarkers. While information-theoretic methods have been widely used, they often exhibit limitations such as high computational costs. Our previously proposed method, ClearF, addresses these issues by using reconstruction error from low-dimensional embeddings as a proxy for the entropy term in the mutual information. However, ClearF still has limitations, including a nontransparent bottleneck layer selection process, which can result in unstable feature selection. To address these limitations, we propose ClearF++, which simplifies the bottleneck layer selection and incorporates feature-wise clustering to enhance biomarker detection. We compare its performance with other commonly used methods such as MultiSURF and IFS, as well as ClearF, across multiple benchmark datasets. Our results demonstrate that ClearF++ consistently outperforms these methods in terms of prediction accuracy and stability, even with limited samples. We also observe that employing the Deep Embedded Clustering (DEC) algorithm for feature-wise clustering improves performance, indicating its suitability for handling complex data structures with limited samples. ClearF++ offers an improved biomarker prioritization approach with enhanced prediction performance and faster execution. Its stability and effectiveness with limited samples make it particularly valuable for biomedical data analysis.

SINR- and MI-Based Double-Robust Waveform Design

Owing to cognitive radar breaking the open-loop receiving-transmitting mode of traditional radar, adaptive waveform design for cognitive radar has become a central issue in radar system research. In this paper, the method of radar transmitted waveform design in the presence of clutter is studied. Since exact characterizations of the target and clutter spectra are uncommon in practice, a single-robust transmitted waveform design method is introduced to solve the problem of the imprecise target spectrum or the imprecise clutter spectrum. Furthermore, considering that radar cannot simultaneously obtain precise target and clutter spectra, a novel double-robust transmitted waveform design method is proposed. In this method, the signal-to-interference-plus-noise ratio and mutual information are used as the objective functions, and the optimization models for the double-robust waveform are established under the transmitted energy constraint. The Lagrange multiplier method was used to solve the optimal double-robust transmitted waveform. The simulation results show that the double-robust transmitted waveform can maximize SINR and MI in the worst case; the performance of SINR and MI will degrade if other transmitted waveforms are employed in the radar system.

Mechanism for the Unfolding of the TOP7 Protein in Steered Molecular Dynamics Simulations as Revealed by Mutual Information Analysis

We employed mutual information (MI) analysis to detect motions affecting the mechanical resistance of the human-engineered protein Top7. The results are based on the MI analysis of pair contact correlations measured in steered molecular dynamics (SMD) trajectories and their statistical dependence on global unfolding. This study is the first application of the MI analysis to SMD forced unfolding, and we furnish specific SMD recommendations for the utility of parameters and options in the TimeScapes package. The MI analysis provided a global overview of the effect of perturbation on the stability of the protein. We also employed a more conventional trajectory analysis for a detailed description of the mechanical resistance of Top7. Specifically, we investigated 1) the hydropathy of the interactions of structural segments, 2) the H₂O concentration near residues relevant for unfolding, and 3) the changing hydrogen bonding patterns and main chain dihedral angles. The results show that the application of MI in the study of protein mechanical resistance can be useful for the engineering of more resistant mutants when combined with conventional analysis. We propose a novel mutation design based on the hydropathy of residues that would stabilize the unfolding region by mimicking its more stable symmetry mate. The proposed design process does not involve the introduction of covalent crosslinks, so it has the potential to preserve the conformational space and unfolding pathway of the protein.

Effect of segmentation from different diffusive metric maps on diffusion tensor imaging analysis of the cervical spinal cord

Background

Segmentation is a crucial and necessary step in diffusion tensor imaging (DTI) analysis of the cervical spinal cord. In existing studies, different diffusive metric maps [B0, fractional anisotropy (FA) and axial diffusivity (AD) maps] have been involved in the segmentation of tissues of the cervical spinal cord. The selection of a diffusive metric map for segmentation may affect the accuracy of segmentation and then affect the validity and effectiveness of the extracted diffusive features. However, there are few discussions on this problem. Therefore, this study would like to examine the effect of segmentation based on different diffusive metric maps for DTI analysis of the cervical spinal cord.

Methods

Twenty-nine healthy subjects and thirty patients with cervical spondylotic myelopathy (CSM) were finally included in this study. All subjects accepted DTI scanning at cervical levels from C2 to C7/T1. For healthy subjects, all cervical levels were included for analysis; while, for each patient, only one compressed cervical level was included. After DTI scanning, DTI metrics including B0, FA, AD, radial diffusivity (RD) and mean diffusivity (MD) were calculated. The evaluation was performed to B0, FA and AD maps from two aspects. First, the accuracy of segmentation was evaluated via a comparison between segmentation based on each diffusive metric map and segmentation based on an average image, which was acquired by averaging B0, FA, AD, RD and MD maps. The segmentation was achieved by a semi-automatic segmentation process, and the similarity between two segmentation results was denoted by the intersection of the union (IOU). Second, the diversity of extracted diffusive features was equalized as their performance in the classification of image pixels of different regions of interest (ROIs) and then was evaluated by mutual information (MI) and area under the curve (AUC). One-way ANOVA and Bonferroni's post hoc tests were applied to compare the evaluation results.

Results

One-way ANOVA suggested that there were differences (P<0.001) in IOU, MI and AUC values among the three diffusive metric maps for both healthy subjects and patients. The post-hoc tests further indicated that FA performed the best (P<0.001), i.e., the most substantial accuracy of segmentation and the highest diversity in extracted diffusive features.

Conclusions

Different evaluation results had been observed for segmentation based on different diffusive metric maps, suggesting the necessity of selection of diffusive metric maps for segmentation in DTI analysis of the cervical spinal cord. Moreover, FA map is suggested for segmentation due to its best performance in the evaluation.

A Single Mutation Increases the Thermostability and Activity of Aspergillus terreus Amine Transaminase

Enhancing the thermostability of (R)-selective amine transaminases (AT-ATA) will expand its application in the asymmetric synthesis of chiral amines. In this study, mutual information and coevolution networks of ATAs were analyzed by the Mutual Information Server to Infer Coevolution (MISTIC). Subsequently, the amino acids most likely to influence the stability and function of the protein were investigated by alanine scanning and saturation mutagenesis. Four stabilized mutants (L118T, L118A, L118I, and L118V) were successfully obtained. The best mutant, L118T, exhibited an improved thermal stability with a 3.7-fold enhancement in its half-life (t_1/2) at 40 °C and a 5.3 °C increase in T₅₀¹⁰ compared to the values for the wild-type protein. By the differential scanning fluorimetry (DSF) analysis, the best mutant, L118T, showed a melting temperature (T_m) of 46.4 °C, which corresponded to a 5.0 °C increase relative to the wild-type AT-ATA (41.4 °C). Furthermore, the most stable mutant L118T displayed the highest catalytic efficiency among the four stabilized mutants.

Efficient Low-PAR Waveform Design Method for Extended Target Estimation Based on Information Theory in Cognitive Radar

This paper addresses the waveform design problem of cognitive radar for extended target estimation in the presence of signal-dependent clutter, subject to a peak-to-average power ratio (PAR) constraint. Owing to this kind of constraint and the convolution operation of the waveform in the time domain, the formulated optimization problem for maximizing the mutual information (MI) between the target and the received signal is a complex non-convex problem. To this end, an efficient waveform design method based on minimization-maximization (MM) technique is proposed. First, by using the MM approach, the original non-convex problem is converted to a convex problem concerning the matrix variable. Then a trick is used for replacing the matrix variable with the vector variable by utilizing the properties of the Toeplitz matrix. Based on this, the optimization problem can be solved efficiently combined with the nearest neighbor method. Finally, an acceleration scheme is used to improve the convergence speed of the proposed method. The simulation results illustrate that the proposed method is superior to the existing methods in terms of estimation performance when designing the constrained waveform.

SINR- and MI-Based Maximin Robust Waveform Design

Due to the uncertainties of radar target prior information in the actual scene, the waveform designed based on radar target prior information cannot meet the needs of detection and parameter estimation performance. In this paper, the optimal waveform design techniques under energy constraints for different tasks are considered. To improve the detection performance of radar systems, a novel waveform design method which can maximize the signal-to-interference-plus-noise ratio (SINR) for known and random extended targets is proposed. To improve the performance of parameter estimation, another waveform design method which can maximize the mutual information (MI) between the radar echo and the random-target spectrum response is also considered. Most of the previous waveform design researches assumed that the prior information of the target spectrum is completely known. However, in the actual scene, the real target spectrum cannot be accurately captured. To simulate this scenario, the real target spectrum was assumed to be within an uncertainty range where the upper and lower bounds are known. Then, the SINR- and MI-based maximin robust waveforms were designed, which could optimize the performance under the most unfavorable conditions. The simulation results show that the designed optimal waveforms based on these two criteria are different, which provides useful guidance for waveform energy allocation in different transmission tasks. However, under the constraint of limited energy, we also found that the performance improvement of SINR or MI in the worst case for single targets is less significant than that of multiple targets.

Cognitive Radar Waveform Optimization Based on Mutual Information and Kalman filtering

A new strategy to optimizing the waveforms of cognitive radar under transmitted power constraint is presented. Our scheme is to enhance the performance of target estimation by minimizing the MSE (mean-square error) of the estimates of target scattering coefficients (TSC) based on Kalman filtering and then minimizing mutual information (MI) between the radar target echoes at successive time instants. The two steps are the optimal design of transmission waveform and the selection of a reasonable waveform from the ensemble for emission, respectively. The waveform design technique addresses the problems of target detection and parameter estimation in intelligent transportation system (ITS), where there is a need of extracting the features of target information obtained from different sensors. As the number of iterations increases, simulation results show better TSC estimation from the radar scene provided by the proposed approach as compared with the traditional waveform optimization algorithm. In addition, the proposed algorithm results in improved target detection probability.

Mutual Information-Based Brain Network Analysis in Post-stroke Patients With Different Levels of Depression

Post-stroke depression (PSD) is the most common stroke-related emotional disorder, and it severely affects the recovery process. However, more than half cases are not correctly diagnosed. This study was designed to develop a new method to assess PSD using EEG signal to analyze the specificity of PSD patients' brain network. We have 107 subjects attended in this study (72 stabilized stroke survivors and 35 non-depressed healthy subjects). A Hamilton Depression Rating Scale (HDRS) score was determined for all subjects before EEG data collection. According to HDRS score, the 72 patients were divided into 3 groups: post-stroke non-depression (PSND), post-stroke mild depression (PSMD) and post-stroke depression (PSD). Mutual information (MI)-based graph theory was used to analyze brain network connectivity. Statistical analysis of brain network characteristics was made with a threshold of 10-30% of the strongest MIs. The results showed significant weakened interhemispheric connections and lower clustering coefficient in post-stroke depressed patients compared to those in healthy controls. Stroke patients showed a decreasing trend in the connection between the parietal-occipital and the frontal area as the severity of the depression increased. PSD subjects showed abnormal brain network connectivity and network features based on EEG, suggesting that MI-based brain network may have the potential to assess the severity of depression post stroke.

Adaptive Waveform Design for MIMO Radar-Communication Transceiver

The system architecture for an adaptive multiple input multiple output (MIMO) radar-communication transceiver is proposed. A waveform design approach for communication data embedding into MIMO radar pulse using M-ary position phase shift keying (MPPSK) waveforms is introduced. A waveform optimization algorithm for the adaptive system is presented. The algorithm aims to improve the target detection performance by maximizing the relative entropy (RE) between the distributions under existence and absence of the target, and minimizing the mutual information (MI) between the current received signals and the estimated signals in the next time instant. The proposed system adapts its MPPSK modulated inter-pulse duration to suit the time-varying environment. With subsequent iterations of the algorithm, simulation results show an improvement in target impulse response (TIR) estimation and target detection probability. Meanwhile, the system is able to transmit data of several Mbps with low symbol error rates.

Low Probability of Intercept-Based Radar Waveform Design for Spectral Coexistence of Distributed Multiple-Radar and Wireless Communication Systems in Clutter

In this paper, the problem of low probability of intercept (LPI)-based radar waveform design for distributed multiple-radar system (DMRS) is studied, which consists of multiple radars coexisting with a wireless communication system in the same frequency band. The primary objective of the multiple-radar system is to minimize the total transmitted energy by optimizing the transmission waveform of each radar with the communication signals acting as interference to the radar system, while meeting a desired target detection/characterization performance. Firstly, signal-to-clutter-plus-noise ratio (SCNR) and mutual information (MI) are used as the practical metrics to evaluate target detection and characterization performance, respectively. Then, the SCNR- and MI-based optimal radar waveform optimization methods are formulated. The resulting waveform optimization problems are solved through the well-known bisection search technique. Simulation results demonstrate utilizing various examples and scenarios that the proposed radar waveform design schemes can evidently improve the LPI performance of DMRS without interfering with friendly communications.

A Novel Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking in an LPI Radar Network

Radar networks are proven to have numerous advantages over traditional monostatic and bistatic radar. With recent developments, radar networks have become an attractive platform due to their low probability of intercept (LPI) performance for target tracking. In this paper, a joint sensor selection and power allocation algorithm for multiple-target tracking in a radar network based on LPI is proposed. It is found that this algorithm can minimize the total transmitted power of a radar network on the basis of a predetermined mutual information (MI) threshold between the target impulse response and the reflected signal. The MI is required by the radar network system to estimate target parameters, and it can be calculated predictively with the estimation of target state. The optimization problem of sensor selection and power allocation, which contains two variables, is non-convex and it can be solved by separating power allocation problem from sensor selection problem. To be specific, the optimization problem of power allocation can be solved by using the bisection method for each sensor selection scheme. Also, the optimization problem of sensor selection can be solved by a lower complexity algorithm based on the allocated powers. According to the simulation results, it can be found that the proposed algorithm can effectively reduce the total transmitted power of a radar network, which can be conducive to improving LPI performance.