Development of Hydroacoustic Localization Algorithms for AUV Based on the Error-Corrected WMChan-Taylor Algorithm

Autonomous underwater vehicles (AUVs) are susceptible to non-line-of-sight (NLOS) errors and noise bias at receiving stations during the application of hydroacoustic localization systems, leading to a degradation in positioning accuracy. To address this problem, this paper optimizes the Chan-Taylor algorithm. Initially, we propose the Weighted Modified Chan-Taylor (WMChan-Talor) algorithm, which introduces dynamic weights into the Chan algorithm to correct noise variance at measurement stations, thereby improving the accuracy of AUV positioning. Computer simulations validate the effectiveness of the WMChan-Taylor algorithm in enhancing positioning accuracy. To further address the accuracy degradation caused by noise deviations across different receiving stations, we introduce an error-corrected WMChan-Taylor algorithm. This algorithm utilizes a standard residual function to eliminate significant delays caused by large errors at receiving stations and applies standard residual weighting to improve the combined positioning solution. The performance of the error-corrected WMChan-Taylor algorithm is demonstrated through both computer and semi-physical simulation experiments, confirming its capability to isolate noisier stations and thus enhance overall positioning accuracy

A Tightly Integrated Navigation Method of SINS, DVL, and PS Based on RIMM in the Complex Underwater Environment

Navigation and positioning of autonomous underwater vehicles (AUVs) in the complex and changeable marine environment are crucial and challenging. For the positioning of AUVs, the integrated navigation of the strap-down inertial navigation system (SINS), Doppler velocity log (DVL), and pressure sensor (PS) has a common application. Nevertheless, in the complex underwater environment, the DVL performance is affected by the current and complex terrain environments. The outliers in sensor observations also have a substantial adverse effect on the AUV positioning accuracy. To address these issues, in this paper, a novel tightly integrated navigation model of the SINS, DVL, and PS is established. In contrast to the traditional SINS, DVL, and PS tightly integrated navigation methods, the proposed method in this paper is based on the velocity variation of the DVL beam by applying the DVL bottom-track and water-track models. Furthermore, a new robust interacting multiple models (RIMM) information fusion algorithm is proposed. In this algorithm, DVL beam anomaly is detected, and the Markov transfer probability matrix is accordingly updated to enable quick model matching. By simulating the motion of the AUV in a complex underwater environment, we also compare the performance of the traditional loosely integrated navigation (TLIN) model, the tightly integrated navigation (TTIN) model, and the IMM algorithm. The simulation results show that because of the PS, the velocity and height in the up-change amplitude of the four algorithms are small. Compared with the TLIN algorithm in terms of maximum deviation of latitude and longitude, the RIMM algorithm also improves the accuracy by 39.1243 m and 26.4364 m, respectively. Furthermore, compared with the TTIN algorithm, the RIMM algorithm improves latitude and longitude accuracy by 1.8913 m and 11.8274 m, respectively. A comparison with IMM also shows that RIMM improves the accuracy of latitude and longitude by 1.1506 m and 7.2301 m, respectively. The results confirm that the proposed algorithm suppresses the observed noise and outliers of DVL and further achieves quick conversion between different DVL models while making full use of the effective information of the DVL beams. The proposed method also improves the navigation accuracy of AUVs in complex underwater environments

Landslide Monitoring along the Dadu River in Sichuan Based on Sentinel-1 Multi-Temporal InSAR

The Dadu River travels in the mountainous areas of southwestern China, one of regions with the most hazards that has long suffered from frequent geohazards. The early identification of landslides in this region is urgently needed, especially after the recent Luding earthquake (MS 6.8). While conventional ground-based monitoring techniques are limited by the complex terrain conditions in these alpine valley regions, space interferometric synthetic aperture radar (InSAR) provides an incomparable advantage in obtaining surface deformation with high precision and over a wide area, which is very useful for long-term and slow geohazard monitoring. In this study, more than 500 Sentinel-1 SAR images with four frames acquired during 2017~2022 were collected to detect the hidden landslide regions from the Jinchuan to Ebian Section along the Dadu River, based on joint-scatterer InSAR (JS-InSAR) and small baseline subset (SBAS) techniques. The results showed that our method could be successfully applied for landslide monitoring in complex mountainous regions. Furthermore, 143 potential landslide regions spreading over an 800 km area along the Dadu River were extracted by integrating the deformation measurements and optical images. Our study can provide a reference for large-scale geological hazard surveys in mountainous areas, and the InSAR technique will be encouraged for the local government in future long-term monitoring applications in the Dadu River Basin

Adrenergic activity in response to CRS promotes in vivo CRC growth.

<p>(A) HT29-inoculated mice were treated with PBS, 0.02 mg/kg E or 2 mg/kg E. The mean tumor weight of each treatment group was measured (* P = 0.034, ^# P = 0.043; n = 5–6; for each group, the mean ± SD of one of two experiments is shown). (B) Individual tumor weights of the three different treatment groups are also shown (n = 11; symbols represent individual mice).</p

Effect of CRS on CRC growth in vivo.

<p>Mice were injected subcutaneously (s.c.) with CRC HT29 cells (2×106) in the dorsal flank 7 d after starting the stress treatment. Daily CRS treatment was continued for an additional 21 d. Mean tumor weight (A), individual tumor weight (B) and representative tumor images (C) from the CRS group and the no-stress control group are shown (* P = 0.028; n = 16–17; symbols represent individual mice; for each treatment group, the mean ± SD of three independent experiments is shown). The same experimental protocol was applied to mice inoculated with SW116 cells. Mean tumor weight (D), individual tumor weight (E) and representative tumor images (F) are shown (* P = 0.021; n = 20; symbols represent individual mice; for each treatment group, the mean ± SD of three independent experiments is shown).</p

Effect of Chronic Restraint Stress on Human Colorectal Carcinoma Growth in Mice

<div><p>Stress alters immunological and neuroendocrinological functions. An increasing number of studies indicate that chronic stress can accelerate tumor growth, but its role in colorectal carcinoma (CRC) progression is not well understood. The aim of this study is to investigate the effects of chronic restraint stress (CRS) on CRC cell growth in nude mice and the possible underlying mechanisms. In this study, we showed that CRS increased the levels of plasma catecholamines including epinephrine (E) and norepinephrine (NE), and stimulated the growth of CRC cell-derived tumors in vivo. Treatment with the adrenoceptor (AR) antagonists phentolamine (PHE, α-AR antagonist) and propranolol (PRO, β-AR antagonist) significantly inhibited the CRS-enhanced CRC cell growth in nude mice. In addition, the stress hormones E and NE remarkably enhanced CRC cell proliferation and viability in culture, as well as tumor growth in vivo. These effects were antagonized by the AR antagonists PHE and PRO, indicating that the stress hormone-induced CRC cell proliferation is AR dependent. We also observed that the β-AR antagonists atenolol (ATE, β1- AR antagonist) and ICI 118,551 (ICI, β2- AR antagonist) inhibited tumor cell proliferation and decreased the stress hormone-induced phosphorylation of extracellular signal-regulated kinases-1/2 (ERK1/2) in vitro and in vivo. The ERK1/2 inhibitor U0126 also blocked the function of the stress hormone, suggesting the involvement of ERK1/2 in the tumor-promoting effect of CRS. We conclude that CRS promotes CRC xenograft tumor growth in nude mice by stimulating CRC cell proliferation through the AR signaling-dependent activation of ERK1/2.</p> </div

E or NE-induced CRC cell proliferation is both α- and β-AR dependent.

<p>HT29-inoculated mice were treated with PBS or a combination of the α-AR antagonist PHE (2 mg/kg) and β-AR antagonist PRO (2 mg/kg) under CRS or no stress. The mean tumor weight (A) of each treatment group was measured (* P = 0.038, ^# P = 0.015; n = 11–12; for each group, the mean ± SD of two independent experiments is shown) and individual tumor weights (B) of the four different treatment groups are also shown (n = 11–12; symbols represent individual mice). HT29 (C) and SW116 cells (D) were pretreated with or without the α-AR antagonist PHE (50 µM) or β-AR antagonist PRO (50 µM) for 45 min before incubation with NE (10 µM) or E (10 µM). After 24 h, cell proliferation was measured by the BrdU incorporation assay, as described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061435#s2" target="_blank">Materials and Methods</a> section. The data are expressed as the mean ± SD of triplicate or quadruplicate samples per treatment group from at least three independent experiments with similar results. (A) NE, (** P = 0.007, ^# P = 0.022); E, (** P = 0.004, ^# P = 0.043); (B) NE, (* P = 0.034, ^## P = 0.009); E, (* P = 0.013, ^# P = 0.032).</p

E-induced hyperphosphorylation of ERK1/2 can be abolished by β-AR antagonists.

<p>(A) HT29 cells were treated with E (10 µM) in the absence or presence of ATE (50 µM), ICI (50 µM) or U0126 (20 µM) for 45 min as indicated, and the cell lysates were homogenized for the immuno-detection of phos-ERK1/2 by Western blot. (B) HT29 tumor samples from the PBS (used as control), E (0.02 mg/kg), E plus ATE (5 mg/kg), and E plus ICI (5 mg/kg) treatments were immunohistochemically stained for phos-ERK1/2. The representative tumor sections are illustrated. (C) The quantified values represent the average immunostaining intensities of phos-ERK1/2 from at least five random fields under 400× magnification (scale bar, 50 µm). Under microscopy, a brown color indicates positive immunostaining (** P = 0.002, ^# P = 0.029, ^$$ P = 0.005; mean ± SD are shown).</p

Effects of E or NE on CRC cells Proliferation.

<p>CRC HT29, SW116 and LS174T cell lines were treated with different concentrations of E or NE, as indicated for 24 h, respectively, cell proliferation was measured by BrdU incorporation assay, as described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061435#s2" target="_blank">Materials and Methods</a> section. Data are expressed as mean ± SD of one representative of at least three experiments. HT29, (0.1 µM NE, ** P = 0.0011; *** P<0.001, significantly different from the control group); SW116, (0.1 µM NE, * P = 0.034; 1 µM NE, * P = 0.034; 10 µM NE, * P = 0.017; 0.1 µM E, ** P = 0.0037; 1 µM E, * P = 0.030; 10 µM E, * P = 0.023, significantly different from the control group); LS174T, (10 µM NE, * P = 0.025; 0.1 µM E, * P = 0.023; 1 µM E, ** P = 0.005; 10 µM E, *** P<0.001, significantly different from the control group).</p

β-AR-mediated CRC cell proliferation and growth in mice.

<p>(A) HT29 cells were pretreated with the β1-AR antagonist ATE (50 µM) or β2-AR antagonist ICI (50 µM) for 45 min before incubation with E (10 µM). After 24 h, cell proliferation was measured by the BrdU incorporation assay, as described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061435#s2" target="_blank">Materials and Methods</a> section. The data are expressed as the mean ± SD of triplicate or quadruplicate samples per treatment group from at least three independent experiments with similar results. (** P = 0.001, ^# P = 0.045, ^$ P = 0.013, ^§§P = 0.006) (B) HT29-inoculated mice were treated with PBS (used as control), E (0.02 mg/kg), E plus ATE (5 mg/kg), or E plus ICI (5 mg/kg). The mean tumor weight of each group was measured at the end of the experiment (* P = 0.02, ^# P = 0.032; n = 8; means ± SDs are shown). (C) Individual tumor weights of each group are also shown (n = 8; symbols represent individual mice).</p