The role of spleen radiomics model for predicting prognosis in esophageal squamous cell carcinoma patients receiving definitive radiotherapy.

BACKGROUND: The spleen plays an important role in systemic antitumor immune response, but whether spleen imaging features have predictive effect for prognosis and immune status was unknown. The aim of this study was to investigate computed tomography (CT)-based spleen radiomics to predict the prognosis of patients with esophageal squamous cell carcinoma (ESCC) underwent definitive radiotherapy (dRT) and to try to find its association with systemic immunity. METHODS: This retrospective study included 201 ESCC patients who received dRT. Patients were randomly divided into training (n = 142) and validation (n = 59) groups. The pre- and delta-radiomic features were extracted from enhanced CT images. LASSO-Cox regression was used to select the radiomics signatures most associated with progression-free survival (PFS) and overall survival (OS). Independent prognostic factors were identified by univariate and multivariate Cox analyses. The ROC curve and C-index were used to evaluate the predictive performance. Finally, the correlation between spleen radiomics and immune-related hematological parameters was analyzed by spearman correlation analysis. RESULTS: Independent prognostic factors involved TNM stage, treatment regimen, tumor location, pre- or delta-Rad-score. The AUC of the delta-radiomics combined model was better than other models in the training and validation groups in predicting PFS (0.829 and 0.875, respectively) and OS (0.857 and 0.835, respectively). Furthermore, some spleen delta-radiomic features are significantly correlated with delta-ALC (absolute lymphocyte count) and delta-NLR (neutrophil-to-lymphocyte ratio). CONCLUSIONS: Spleen radiomics is expected to be a useful noninvasive tool for predicting the prognosis and evaluating systemic immune status for ESCC patients underwent dRT.

High-resolution frequency-difference beamforming for a short linear array.

Conventional beamforming (CBF) is a commonly employed approach for detecting and estimating the direction-of-arrival (DOA) of acoustic signals in underwater environments. However, CBF becomes ambiguous due to spatial aliasing when the received signal's half wavelength is smaller than the array spacing. Frequency-difference beamforming (FDB) allows for processing data in the lower frequency Δf without encountering spatial aliasing by utilizing the product of array data at frequency f with its complex conjugate at frequency f+Δf. However, lower frequency results in a wider mainlobe, which can lead to poorer DOA performance for short arrays. In this paper, a fourth-order cumulants FDB method and a conjugate augmented FDB method are proposed to extend an M-element uniform linear array to 2M-1 and 4M-3 elements. The proposed methods generate narrower beams and lower sidelobe levels than the original FDB for short arrays with large spacing. And by setting the signal subspace dimension reasonably, the proposed methods can improve the weak target detection ability under strong interference compared with FDB. Last, we verify the excellent performance of the proposed methods through simulations and experimental data.

Unambiguous broadband direction of arrival estimation based on improved extended frequency-difference method.

In this paper, we consider the problem of bearing ambiguity in the direction of arrival (DOA) estimation due to spatial aliasing when the minimum wavelength of the processing broadband signal is less than the element spacing of a uniform linear array (ULA). First, an extended frequency-difference (FD) method is presented. Unlike the existing FD methods, the extended FD signal is constructed by conjugate multiplying a diagonal matrix consisting of steering vectors at high frequency and pre-processing direction with the array sampled signal matrix at low frequency. Then, this paper establishes a decision criterion for distinguishing the aliasing component that varies linearly with frequency in the extended FD space. Finally, an unambiguous broadband DOA estimation method is achieved by suppressing spatial aliasing in the extended FD space. The simulation results show the effectiveness of the proposed method in low signal-to-noise ratio, low signal-to-interference ratio, and multi-interference conditions. The unambiguous processing ability of the proposed method is further verified in the South China Sea using ship signals in the frequency band of 200 to 700 Hz and a 10-element ULA with a 6.25 m spacing deployed on the seabed.

Ambient noise under stably covered ice.

Sea ice and freshwater ice can be different in terms of physical and acoustic characteristics, such as density, salinity, etc. In this paper, under-ice ambient noise in the Mudan river (Jilin Province, Northeast of China) is analyzed using the data recorded by autonomous hydrophones to test if the river ice environment is an effective analog for studying under-ice noise of multi-year ice sheets in the Arctic. The noise spectrum level below 250 Hz and above 1 kHz decreases linearly with the increase in the logarithmic frequency in a quiet environment. The ice cracks are detected and extracted, and Pearson correlation analysis between meteorological information and cracks is carried out. Frequency correlation matrixes are calculated to obtain the correlation between pairs of frequencies and evaluate the frequency correlation of ice crack noise of two hydrophones under different depths, different distances, and different times. Finally, the paper compares the experimental results with Arctic under-ice noise in the literature. Similarities with Arctic under-ice noise are observed in the experiment, including noise spectrum, cracks' peak frequency, and correlations between temperature and crack intensity. This paper believes that the study of under-ice noise in freshwater rivers can be used to simulate multi-year ice regions in the Arctic in terms of thermal-induced cracks and meteorological correlation. And future research is needed to prove this judgment further.

Frequency-domain equalization with interference rejection combining for single carrier multiple-input multiple-output underwater acoustic communications.

This paper describes a three-step frequency-domain equalization scheme for multiple-input multiple-output (MIMO) underwater acoustic communication. First, an iterative least-squares channel estimation method is developed to enhance the accuracy of channel estimation in MIMO communication. The interference rejection combining method is then adopted to suppress co-channel interference based on the estimated MIMO channels. This technique exploits the correlation between the interference received through different channels. Finally, a decision feedback equalizer embedded with a digital phase-lock loop is cascaded before the final determination of equalized symbols to compensate the phase rotation. Experimental results show that the bit error rates of the proposed scheme can be several orders of magnitude lower than those of conventional frequency-domain equalization schemes.

A Bioinspired Twin Inverted Multiscale Matched Filtering Method for Detecting an Underwater Moving Target in a Reverberant Environment.

To this day, biological sonar systems still have great performance advantages over artificial sonar systems, especially for detection in environments with clutter, strong reverberation, and a low signal to noise ratio (SNR). Therefore, mammal sonar systems, for instance, bats and toothed whales, have many characteristics worth learning from. This paper proposes a bioinspired twin inverted multiscale matched filtering method to detect underwater moving targets. This method can be mainly divided into three parts. Firstly, a hyperbolic frequency modulation (HFM) continuous wave (CW) multiharmonic detection signal was adopted after analyzing signals from bats and dolphins. This signal combines the advantages of CW and HFM signals and has excellent time measurement and speed measurement performance when detecting a moving target. Secondly, the twin inverted waveform was introduced to suppress strong linear reverberation and highlight moving targets. The pulse interval was determined by assessing the reverberation reduction time. Thirdly, when processing echoes, a multiscale matched filtering method was proposed to make use of multiharmonic information and improve detection performance. Finally, a channel pool experiment was carried out to test the performance of the proposed method. The experimental result demonstrates that the proposed method has better performance when detecting a moving target in a reverberant environment compared to the conventional matched filtering method. Related results can be applied to small underwater platforms or sensor network platforms for target detection and coastal defense applications.

Multitarget Tracking Algorithm Using Multiple GMPHD Filter Data Fusion for Sonar Networks.

Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm's performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones.