Remote passive acoustic signal detection using multi-scale correlation networks and network spectrum distance in marine environment.

Detecting acoustic signals in the ocean is crucial for port and coastal security, but existing methods often require informative priors. This paper introduces a new approach that transforms acoustic signal detection into network characterization using a MCN construction method. The method constructs a network representation of the acoustic signal by measuring pairwise correlations at different time scales. It proposes a network spectrum distance method that combines information geometry and graph signal processing theory to characterize these complex networks. By comparing the spectra of two networks, the method quantifies their similarity or dissimilarity, enabling comparisons of multi-scale correlation networks constructed from different time series data and tracking changes in nonlinear dynamics over time. The effectiveness of these methods is substantiated through comprehensive simulations and real-world data collected from the South China Sea. The results illustrate that the proposed approach attains a significant detection probability of over 90% when the signal-to-noise ratio exceeds -18 dB, whereas existing methods require a signal-to-noise ratio of at least -15 dB to achieve a comparable detection probability. This innovative approach holds promising applications in bolstering port security, facilitating coastal operations, and optimizing offshore activities by enabling more efficient detection of weak acoustic signals.

Improved multivariate multiscale sample entropy and its application in multi-channel data.

Entropy, as a nonlinear feature in information science, has drawn much attention for time series analysis. Entropy features have been used to measure the complexity behavior of time series. However, traditional entropy methods mainly focus on one-dimensional time series originating from single-channel transducers and are incapable of handling the multidimensional time series from multi-channel transducers. Previously, the multivariate multiscale sample entropy (MMSE) algorithm was introduced for multi-channel data analysis. Although MMSE generalizes multiscale sample entropy and provides a new method for multidimensional data analysis, it lacks necessary theoretical support and has shortcomings, such as missing cross-channel correlation information and having biased estimation results. This paper proposes an improved multivariate multiscale sample entropy (IMMSE) algorithm to overcome these shortcomings. This paper highlights the existing shortcomings in MMSE under the generalized algorithm. The rationality of IMMSE is theoretically proven using probability theory. Simulations and real-world data analysis have shown that IMMSE is capable of effectively extracting cross-channel correlation information and demonstrating robustness in practical applications. Moreover, it provides theoretical support for generalizing single-channel entropy methods to multi-channel situations.

Apolipoprotein E2 inhibits mitochondrial apoptosis in pancreatic cancer cells through ERK1/2/CREB/BCL-2 signaling.

BACKGROUND: Apolipoprotein E2 (ApoE2) is a pleiotropic protein that influences several aspects of cancer metabolism and development. Evading apoptosis is a vital factor for facilitating cancer cell growth. However, the role and mechanism of ApoE2 in regulating cell apoptosis of pancreatic cancer remain unclear. METHODS: In this study, we firstly detected the mRNA and protein expressions of ApoE2 in PANC-1 and Capan-2 cells by real-time polymerase chain reaction and Western blotting. We then performed TUNEL and flow cytometric analyses to explore the role of recombinant human ApoE2, pCMV6-ApoE2 and siApoE2 in the apoptosis of PANC-1 and Capan-2 cells. Furthermore, we investigated the molecular mechanism through which ApoE2 affected apoptosis in PANC-1 cells using immunofluorescence, immunoprecipitation, Western blotting and co-immunoprecipitation analysis. RESULTS: ApoE2 phosphorylated ERK1/2 and inhibited pancreatic cancer cell apoptosis. In addition, our data showed that ApoE2/ERK1/2 altered the expression and mitochondrial localization of BCL-2 via activating CREB. ApoE2/ERK1/2/CREB also increased the total BCL-2/BAX ratio, inhibited the opening of the mitochondrial permeability transition pore and the depolarization of mitochondrial transmembrane potential, blocked the leakage of cytochrome-c and the formation of the apoptosome, and consequently, suppressed mitochondrial apoptosis. CONCLUSIONS: ApoE2 regulates the mitochondrial localization and expression of BCL-2 through the activation of the ERK1/2/CREB signaling cascade to evade the mitochondrial apoptosis of pancreatic cancer cells. ApoE2 may be a distinct prognostic marker and a potential therapeutic target for pancreatic cancer.

Research on Area of Uncertainty of Underwater Moving Target Based on Stochastic Maneuvering Motion Model.

Considering the influence of measurement error on target state estimation, there is an uncertain dispersion region for target position estimate, that is, the area of uncertainty (AOU, area of uncertainty). In underwater target tracking, the state estimation is point estimation without AOU estimation and its accuracy is poor in the early stage because of large measurement errors. Fast tracking with higher accuracy and AOU estimation are of great significance to time-sensitive target tracking. To improve the state estimation accuracy in the early stage, and estimate the AOU, a method of AOU estimation of underwater moving target is presented based on a stochastic maneuvering motion (SMM, stochastic maneuvering motion) model. The stochastic maneuvering motion model is established based on the Langevin equation to reflect the movement characteristics of an underwater moving target. Then, the target state is estimated with a noise adaptive Kalman filter by constructing the measurement equation and state equation according to measurement error characteristic and stochastic maneuvering model. Based on the physical significance of the error covariance matrix from the Kalman filter, the parameters of AOU are deduced. Simulation results of underwater target tracking and AOU estimation are presented to demonstrate the relative performance of the proposed algorithm compared with the adaptive Kalman filter. It is clearly shown from the results that SMM tracking algorithm achieves higher accuracy of state estimation in the initial stage of tracking, and the predicted AOU is consistent with the actual distribution of underwater moving targets while yielding more concentrated distribution, which reveals that estimated AOU can be precisely represented by the confidence ellipses. The presented approach and obtained results may be useful in time-sensitive target threat analysis and weapon strike applications.

Biosynthetic Dendritic Cell-Exocytosed Aggregation-Induced Emission Nanoparticles for Synergistic Photodynamic Immunotherapy.

Dendritic cell (DC)-derived small extracellular vesicles (DEVs) are recognized as a highly promising alternative to DC vaccines; however, the clinical testing of DEV-based immunotherapy has shown limited therapeutic efficacy. Herein, we develop a straightforward strategy in which DCs serve as a cell reactor to exocytose high-efficient DEV-mimicking aggregation-induced emission (AIE) nanoparticles (DEV-AIE NPs) at a scaled-up yield for synergistic photodynamic immunotherapy. Exocytosed DEV-AIE NPs inherit not only the immune-modulation proteins from parental DCs, enabling T cell activation, but also the loaded AIE-photosensitizer MBPN-TCyP, inducing superior immunogenic cell death (ICD) by selectively accumulating in the mitochondria of tumor cells. Eventually, DEV-AIE synergistic photodynamic immunotherapy elicits dramatic immune responses and efficient eradication of primary tumors, distant tumors, and tumor metastases. In addition, cancer stem cells (CSCs) in 4T1 and CT26 solid tumors were significantly inhibited by the immune functional DEV-AIE NPs. Our work presents a facile method for the cellular generation of EV-biomimetic NPs and demonstrates that the integration of DEVs and AIE photosensitizers is a powerful direction for the production of clinical anticancer nanovaccines.

COL11A1-Driven Epithelial-Mesenchymal Transition and Stemness of Pancreatic Cancer Cells Induce Cell Migration and Invasion by Modulating the AKT/GSK-3ß/Snail Pathway.

BACKGROUND: Collagen type XI α1 (COL11A1) is associated with tumorigenesis and development in many human malignancies. Previous reports indicate that COL11A1 may be a significant diagnostic marker for pancreatic ductal adenocarcinoma (PDAC); however, its biological role in PDAC progression remains unclear. In this study, we investigated the influence of COL11A1 on the invasion and migration abilities of pancreatic cancer cells and explored its potential molecular mechanisms. METHODS: Cell migration and invasion were assessed using Transwell assays in pancreatic cancer cells transfected with siCOL11A1 and pCNV3-COL11A1 plasmids. The protein and mRNA expression levels of N-cadherin, E-cadherin, Vimentin, cluster of differentiation (CD)-24, CD44, serine-threonine kinase (AKT), glycogen synthase kinase (GSK)-3ß, phospho (p)-AKTSer473, p-GSK-3ßSer9, and Snail were analyzed using Western blotting and real-time polymerase chain reaction (PCR). The effect of COL11A1 on cell stemness was tested using flow cytometry and clone formation assays. RESULTS: These results demonstrated that COL11A1 significantly promoted the invasion and migration abilities of PDAC cells. Furthermore, COL11A1 facilitated the occurrence of epithelial-mesenchymal transition (EMT) and cell stemness by upregulating the expression levels of p-AKTSer473, p-GSK-3ßSer9, and Snail. CONCLUSIONS: This study suggests that the activation of the AKT/GSK-3ß/Snail signaling pathway induced by COL11A1 plays a major role in the progression of PDAC. Therefore, COL11A1 could serve as a potential target for PDAC treatment.

The sustained PGE2 release matrix improves neovascularization and skeletal muscle regeneration in a hindlimb ischemia model.

BACKGROUND: The promising therapeutic strategy for the treatment of peripheral artery disease (PAD) is to restore blood supply and promote regeneration of skeletal muscle regeneration. Increasing evidence revealed that prostaglandin E2 (PGE2), a lipid signaling molecule, has significant therapeutic potential for tissue repair and regeneration. Though PGE2 has been well reported in tissue regeneration, the application of PGE2 is hampered by its short half-life in vivo and the lack of a viable system for sustained release of PGE2. RESULTS: In this study, we designed and synthesized a new PGE2 release matrix by chemically bonding PGE2 to collagen. Our results revealed that the PGE2 matrix effectively extends the half-life of PGE2 in vitro and in vivo. Moreover, the PGE2 matrix markedly improved neovascularization by increasing angiogenesis, as confirmed by bioluminescence imaging (BLI). Furthermore, the PGE2 matrix exhibits superior therapeutic efficacy in the hindlimb ischemia model through the activation of MyoD1-mediated muscle stem cells, which is consistent with accelerated structural recovery of skeletal muscle, as evidenced by histological analysis. CONCLUSIONS: Our findings highlight the chemical bonding strategy of chemical bonding PGE2 to collagen for sustained release and may facilitate the development of PGE2-based therapies to significantly improve tissue regeneration.

The COL11A1/Akt/CREB signaling axis enables mitochondrial-mediated apoptotic evasion to promote chemoresistance in pancreatic cancer cells through modulating BAX/BCL-2 function.

Collagen XI, a member of the collagen family, is present in the extracellular matrix (ECM), and high collagen XI/αI (COL11A1) expression in tumor tissue is reportedly correlated with the clinicopathological parameters of pancreatic ductal adenocarcinoma (PDAC). However, the function of COL11A1 in the development of pancreatic cancer cells remains unclear. In the current study, we assessed mRNA expression of COL11A1 and its receptors and created a testing-model of both a COL11A1-overexpressing tumor microenvironment and/or altered-COL11A1 expression in pancreatic cancer cell lines. Next, we investigated the mechanism by which COL11A1 affects growth, gemcitabine (GEM) resistance and apoptosis in pancreatic cancer cells. We demonstrated that COL11A1 phosphorylated AktSer473, promoting proliferation of cancer cells and inhibiting their apoptosis. Additionally, our data showed that COL11A1/Akt/CREB altered the balance between BCL-2 and BAX and mediated their mitochondrial translocation in pancreatic cancer cells. The COL11A1/Akt axis induced disruption of mitochondrial transmembrane function, enabling mitochondria-mediated apoptotic evasion to promote chemoresistance. We also explored the regulatory effect of COL11A1/Akt on molecular signaling in the mitochondria-mediated apoptotic program. COL11A1/Akt disturbed the BCL-2/BAX balance, inhibiting cytochrome c (Cyt-C) release and binding of Apaf-1/procaspase-9/Cyt-C, which suppressed the apoptotic program and induced GEM resistance in pancreatic cancer cells. In conclusion, COL11A1 modulates apoptotic inhibition and chemoresistance in pancreatic cancer cells by activating the Akt/CREB/BCL-2/BAX signaling pathway. COL11A1 may represent a distinct prognostic indicator and may be an attractive therapeutic target for PDAC.

Apolipoprotein E2 Promotes the Migration and Invasion of Pancreatic Cancer Cells via Activation of the ERK1/2 Signaling Pathway.

BACKGROUND: Apolipoprotein E2 (ApoE2) is reported to be essential for cell metastasis and proliferation and has been considered a potential diagnostic marker in many cancers. However, the function of ApoE2 in the metastasis of pancreatic cancer, as well as the underlying mechanism, remain unclear. PURPOSE: In this study, we explored the effect of ApoE2 on the migration and invasion abilities of pancreatic cancer cells and explored the underlying molecular mechanism. METHODS AND RESULTS: Wound healing and Matrigel Transwell assays were used to investigate the role of ApoE2 in cell migration and invasion. Western blotting analysis showed that ApoE2 was overexpressed in pancreatic cancer tissues. Additionally, the overexpression of ApoE2 promoted the process of epithelial-mesenchymal transition (EMT) and enhanced the expression of MMP-2/9 in pancreatic cancer cells. Mechanistically, we found that inhibition of ERK1/2 signaling with PD98059 impaired the ApoE2-mediated promotion of cell migration, invasion and EMT. CONCLUSION: This study demonstrated that ApoE2/ERK1/2 signaling promoted the migration and invasion of pancreatic cancer cells. ApoE2 might be a potential therapeutic target for the treatment of pancreatic cancer metastasis.

Segmentation Method for Ship-Radiated Noise Using the Generalized Likelihood Ratio Test on an Ordinal Pattern Distribution.

Due to the diversity of ship-radiated noise (SRN), audio segmentation is an essential procedure in the ship statuses/categories identification. However, the existing segmentation methods are not suitable for the SRN because of the lack of prior knowledge. In this paper, by a generalized likelihood ratio (GLR) test on the ordinal pattern distribution (OPD), we proposed a segmentation criterion and introduce it into single change-point detection (SCPD) and multiple change-points detection (MCPD) for SRN. The proposed method is free from the acoustic feature extraction and the corresponding probability distribution estimation. In addition, according to the sequential structure of ordinal patterns, the OPD is efficiently estimated on a series of analysis windows. By comparison with the Bayesian Information Criterion (BIC) based segmentation method, we evaluate the performance of the proposed method on both synthetic signals and real-world SRN. The segmentation results on synthetic signals show that the proposed method estimates the number and location of the change-points more accurately. The classification results on real-world SRN show that our method obtains more distinguishable segments, which verifies its effectiveness in SRN segmentation.

An Efficient Topology Discovery Protocol with Node ID Assignment Based on Layered Model for Underwater Acoustic Networks.

Underwater acoustic networks are widely used in survey missions and environmental monitoring. When an underwater acoustic network (UAN) is deployed in a marine region or two UANs merge, each node hardly knows the entire network and may not have a unique node ID. Therefore, a network topology discovery protocol that can complete node discovery, link discovery, and node ID assignment are necessary and important. Considering the limited node energy and long propagation delay in UANs, it is challenging to obtain the network topology with reduced overheads and a short delay in this initial network state. In this paper, an efficient topology discovery protocol (ETDP) is proposed to achieve adaptive node ID assignment and topology discovery simultaneously. To avoiding packet collision in this initial network state, ETDP controls the transmission of topology discovery (TD) packets, based on a local timer, and divides the network into different layers to make nodes transmit TD packets orderly. Exploiting the received TD packets, each node could obtain the network topology and assign its node ID independently. Simulation results show that ETDP completes network topology discovery for all nodes in the network with significantly reduced energy consumption and short delay; meanwhile, it assigns the shortest unique IDs to all nodes with reduced overheads.

Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels.

Orthogonal Chirp Division Multiplexing (OCDM) is a modulation scheme which outperforms the conventional Orthogonal Frequency Division Multiplexing (OFDM) under frequency selective channels by using chirp subcarriers. However, low complexity equalization algorithms for OCDM based systems under doubly selective channels have not been investigated yet. Moreover, in OCDM, the usage of different phase matrices in modulation will lead to extra storage overhead. In this paper, we investigate an OCDM based modulation scheme termed uniform phase-Orthogonal Chirp Division Multiplexing (UP-OCDM) for high-speed communication over doubly selective channels. With uniform phase matrices equipped, UP-OCDM can reduce the storage requirement of modulation. We also prove that like OCDM, the transform matrix of UP-OCDM is circulant. Based on the circulant transform matrix, we show that the channel matrices in UP-OCDM system over doubly selective channels have special structures that (1) the equivalent frequency-domain channel matrix can be approximated as a band matrix, and (2) the transform domain channel matrix in the framework of the basis expansion model (BEM) is a sum of the product of diagonal and circulant matrices. Based on these special channel structures, two low-complexity equalization algorithms are proposed for UP-OCDM in this paper. The equalization algorithms are based on block LDL H factorization and iterative matrix inversion, respectively. Numerical simulations are finally proposed to show the performance of UP-OCDM and the validity of the proposed low complexity equalization algorithms. It is shown that when the channel is doubly selective, UP-OCDM and OCDM have similar BER performance, and both of them outperform OFDM. Moreover, the proposed low complexity equalizers for UP-OCDM both show better BER performance than their OFDM counterparts.

Adaptive Intrawell Matched Stochastic Resonance with a Potential Constraint Aided Line Enhancer for Passive Sonars.

Remote passive sonar detection and classification are challenging problems that require the user to extract signatures under low signal-to-noise (SNR) ratio conditions. Adaptive line enhancers (ALEs) have been widely utilized in passive sonars for enhancing narrowband discrete components, but the performance is limited. In this paper, we propose an adaptive intrawell matched stochastic resonance (AIMSR) method, aiming to break through the limitation of the conventional ALE by nonlinear filtering effects. To make it practically applicable, we addressed two problems: (1) the parameterized implementation of stochastic resonance (SR) under the low sampling rate condition and (2) the feasibility of realization in an embedded system with low computational complexity. For the first problem, the framework of intrawell matched stochastic resonance with potential constraint is implemented with three distinct merits: (a) it can ease the insufficient time-scale matching constraint so as to weaken the uncertain affect on potential parameter tuning; (b) the inaccurate noise intensity estimation can be eased; (c) it can release the limitation on system response which allows a higher input frequency in breaking through the large sampling rate limitation. For the second problem, we assumed a particular case to ease the potential parameter a o p t = 1 . As a result, the computation complexity is greatly reduced, and the extremely large parameter limitation is relaxed simultaneously. Simulation analyses are conducted with a discrete line signature and harmonic related line signature that reflect the superior filtering performance with limited sampling rate conditions; without loss of generality of detection, we considered two circumstances corresponding to H 1 (periodic signal with noise) and H 0 (pure noise) hypotheses, respectively, which indicates the detection performance fairly well. Application verification was experimentally conducted in a reservoir with an autonomous underwater vehicle (AUV) to validate the feasibility and efficiency of the proposed method. The results indicate that the proposed method surpasses the conventional ALE method in lower frequency contexts, where there is about 10 dB improvement for the fundamental frequency in the sense of power spectrum density (PSD).

LAMC2 modulates the acidity of microenvironments to promote invasion and migration of pancreatic cancer cells via regulating AKT-dependent NHE1 activity.

LAMC2, as a unique chain in the Laminin 5 molecule, has been found to be associated with malignant metastases in some cancers. However, the roles and mechanisms by which LAMC2 affects the migration and invasion of pancreatic cancer cells remain unclear. First, we found that laminin 5/LAMC2 and its receptors were highly expressed in pancreatic cancer tissues and cells. Then, we investigated the effects of LAMC2 on pancreatic cancer cell migration/invasion and extracellular (pHe). We also demonstrated that LAMC2 phosphorylated Akt-Ser473 to promote the expression, activity and cell membrane accumulation of NHE1 within pancreatic cancer cells. So we speculated that LAMC2 modulated the pHe to promote migration and invasion of pancreatic cancer cells. Additionally, our data also showed that LAMC2/NHE1 resulted in altered cell morphology and aberrant expression of mesenchymal markers. The function of actin-binding proteins (ABPs) were affected by LAMC2/NHE1 signaling. LAMC2/NHE1 signaling generated extracellular acidification to induce dynamic actin-dependent pseudopodial formation and EMT programs that promote tumor cell invasion in pancreatic cancer cells. Therefore, we found that LAMC2 was responsible for generating the extracellular acidic conditions that mediated invasion of pancreatic cancer cells by activating Akt/NHE1 signaling. LAMC2 is a characteristic prognostic and therapeutic agent of PDCA.

Apolipoprotein E2 modulates cell cycle function to promote proliferation in pancreatic cancer cells via regulation of the c-Myc-p21Waf1 signalling pathway.

Apolipoprotein E2 (ApoE2) is reportedly critical for cell proliferation and survival, and has been identified as a potential tumour-associated marker in many kinds of cancer. However, studies of the function and mechanisms of ApoE2 in pancreatic cancer proliferation and development are rare. In this study, we performed an analysis to determine the modulatory effects of ApoE2-LRP8 (lipoprotein receptor-related protein 8) pathway on cell cycle and cell proliferation, and explored its mechanisms in pancreatic cancer. High expression levels of ApoE2-LRP8/c-Myc were detected in tumour tissues and cell lines by immunohistochemistry and Western blotting. It was also shown that ApoE2-LRP8 induced phosphorylation of ERK1/2 to activate c-Myc and contribute to cell-cycle-related protein expression. ApoE2 conditions induced c-Myc binding to target gene sequences in the p21Waf1 promoter, resulting in decreased transcription. ERK/c-Myc contributes to the promotion of the expression levels of cyclin D1, cdc2, and cyclin B1, and reduces p21Waf1 activity, thereby promoting cell cycle distribution. We demonstrated the function of ApoE2-LRP8 in the activation of the ERK-c-Myc-p21Waf1 signalling cascade and the modulation of G1/S and G2/M transition, indicating ApoE2-LRP8's important role in the cancer cell proliferation. ApoE2 could serve as a diagnostic marker and chemotherapeutic target in pancreatic cancer.

Dynamic Reference Selection-Based Self-Localization Algorithm for Drifted Underwater Acoustic Networks.

Self-localization has become one of the major areas of research in drifted underwater acoustic networks (DUANs) since many applications are based on the knowledge of nodes' positions. However, self-localization for DUANs faces two main challenges: the insufficient anchors and the varying network topology. Both affect the localization performance seriously. In this paper, we focus on these two challenges and propose a dynamic reference selection-based self-localization algorithm for DUANs (DRSL) to improve the localization performance. First, an optimal reference selection scheme is presented to solve the insufficient anchors' problem. The selected optimal reference node can not only assist the insufficient anchors in accomplishing the localization procedure, but also obviously increase the localization accuracy. Based on the proposed optimal reference selection scheme, a dynamic reference selection-based self-localization algorithm is proposed to solve the topology changing problem. The proposed algorithm can improve the localization performance for DUANs significantly by selecting the reference node dynamically according to the predicted network topology, which is more suitable for DUANs with mobile sensor nodes. Simulation results show that the proposed DRSL algorithm can increase the localization accuracy greatly with insufficient anchor nodes and varying network topology. In addition, DRSL algorithm also has a lower communication cost than other anchor-free approaches, which distinctly demonstrates the advantages of the proposed DRSL algorithm.

Hierarchy Graph Based Barrier Coverage Strategy with a Minimum Number of Sensors for Underwater Sensor Networks.

Underwater sensor networks ( UWSNs ) based barrier coverage is increasingly important for intrusion detection due to the scarcity of underwater sensor resource. To improve UWSNs' detection performance and prolong their lifetime, an efficient barrier coverage strategy is very important. In this paper, a novel concept: hierarchy graph is proposed. Hierarchy graph can make the network's topology more clarity. In accordance with the hierarchy graph, 1-barrier coverage algorithm and k-barrier coverage algorithm are presented to construct the barrier with less sensors for higher energy efficiency. Both analytical and simulation studies demonstrate that the proposed algorithms can provide high detection probability and long lifetime for UWSNs.

A Comparative Study of Multiscale Sample Entropy and Hierarchical Entropy and Its Application in Feature Extraction for Ship-Radiated Noise.

The presence of marine ambient noise makes it difficult to extract effective features from ship-radiated noise. Traditional feature extraction methods based on the Fourier transform or wavelets are limited in such a complex ocean environment. Recently, entropy-based methods have been proven to have many advantages compared with traditional methods. In this paper, we propose a novel feature extraction method for ship-radiated noise based on hierarchical entropy (HE). Compared with the traditional entropy, namely multiscale sample entropy (MSE), which only considers information carried in the lower frequency components, HE takes into account both lower and higher frequency components of signals. We illustrate the different properties of HE and MSE by testing them on simulation signals. The results show that HE has better performance than MSE, especially when the difference in signals is mainly focused on higher frequency components. Furthermore, experiments on real-world data of five types of ship-radiated noise are conducted. A probabilistic neural network is employed to evaluate the performance of the obtained features. Results show that HE has a higher classification accuracy for the five types of ship-radiated noise compared with MSE. This indicates that the HE-based feature extraction method could be used to identify ships in the field of underwater acoustic signal processing.

Tenascin-C Modulates Cell Cycle Progression to Enhance Tumour Cell Proliferation through AKT/FOXO1 Signalling in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a disease with an extremely poor prognosis that is characterized by a rich extracellular matrix (ECM). Tenascin-C (TNC) is a component of the ECM and plays a role in tumour progression. In this study, we reported that TNC is overexpressed in PDAC tissues and is correlated with tumour stage and cyclin D1 expression. Cyclin D1 is key regulator of the cell cycle G1/S transition. Further experiments revealed that TNC promotes G1/S transition through AKT signalling. TNC/AKT increases the expression of cyclin D1 by enhancing the transcriptional activity of ß-catenin, whereas the translocation of FOXO1 from the nucleus results in the downregulation of p27Kip1. Cyclin D1 and p27Kip1 regulate the activity of cyclin D1-CDK4 complexes and retinoblastoma (Rb), and then they stimulate the progression of G1/S transition and tumour cell proliferation. In conclusion, TNC exerts its activating effect on the proliferation of pancreatic cancer cells in vitro and in vivo through its functional target AKT/FOXO1/ß-catenin. The molecular mechanisms that drive PDAC progression will be useful for the development of molecular markers and the evaluation of patient prognosis.

Application of Differential Entropy in Characterizing the Deformation Inhomogeneity and Life Prediction of Low-Cycle Fatigue of Metals.

The relation between deformation inhomogeneity and low-cycle-fatigue failure of T2 pure copper and the nickel-based superalloy GH4169 under symmetric tension-compression cyclic strain loading is investigated by using a polycrystal representative volume element (RVE) as the material model. The anisotropic behavior of grains and the strain fields are calculated by crystal plasticity, taking the Bauschinger effect into account to track the process of strain cycles of metals, and the Shannon's differential entropies of both distributions of the strain in the loading direction and the first principal strain are employed at the tension peak of the cycles as measuring parameters of strain inhomogeneity. Both parameters are found to increase in value with increments in the number of cycles and they have critical values for predicting the material's fatigue failure. Compared to the fatigue test data, it is verified that both parameters measured by Shannon's differential entropies can be used as fatigue indicating parameters (FIPs) to predict the low cycle fatigue life of metal.