Source depth estimation with feature matching using convolutional neural networks in shallow water.

A feature matching method based on the convolutional neural network (named FM-CNN), inspired from matched-field processing (MFP), is proposed to estimate source depth in shallow water. The FM-CNN, trained on the acoustic field replicas of a single source generated by an acoustic propagation model in a range-independent environment, is used to estimate single and multiple source depths in range-independent and mildly range-dependent environments. The performance of the FM-CNN is compared to the conventional MFP method. Sensitivity analysis for the two methods is performed to study the impact of different environmental mismatches (i.e., bottom parameters, water column sound speed profile, and topography) on depth estimation performance in the East China Sea environment. Simulation results demonstrate that the FM-CNN is more robust to the environmental mismatch in both single and multiple source depth estimation than the conventional MFP. The proposed FM-CNN is validated by real data collected from four tracks in the East China Sea experiment. Experimental results demonstrate that the FM-CNN is capable of reliably estimating single and multiple source depths in complex environments, while MFP has a large failure probability due to the presence of strong sidelobes and wide mainlobes.

Sound attenuation at low to mid frequencies in low velocity seabottoms.

Attenuation is the most difficult seafloor acoustic property to get, particularly at low to mid frequencies. For low velocity bottoms (LVB), it becomes even more challenging, due to its small attenuation and lower velocity (relative to the velocity of the adjacent water). The latter one causes a fatal "seafloor velocity-attenuation couplings" in geo-acoustic inversions. Thus, attenuation inversions for the LVB require an accurate seafloor velocity profile, especially the velocity in the LVB layer. The propagation of explosive sound in the Yellow Sea with a strong thermocline and a top LVB layer exhibits many prominent characteristics: modal dispersion (the ground wave, water wave, Airy phase), two groups of water waves at high frequencies, and the siphon effect which causes abnormally large sound transmission loss at selected frequencies, etc. These observations are used to precisely measure the critical frequency, the Airy frequency, Airy wave velocity, 1st mode group velocity, and to derive the velocities in the LVB layer and in the basement. Using inverted seafloor parameters, the source level-normalized transmission loss and the first mode decay rate in ranges up to 27.66 km, the sound attenuations in the LVB are derived for a frequency range of 13-5000 Hz.

Theoretical and numerical study on transient acoustic wave propagation across ice layers in the Arctic Ocean.

The study of transient acoustic wave propagation across the Arctic Ocean ice layer provides theoretical guidance for the design of trans-ice acoustic communication systems. In this study, the Arctic Ocean was modeled as an ice-water composite structure, where the ice and water are regarded as an elastic solid and liquid, respectively. An analytical transient solution for acoustic wave propagation in this structure was derived using the eigenfunction expansion method. Further, the numerical procedures were presented and used to analyze the acoustic wave propagation characteristics across the ice layer. The results show that waveforms corresponding to the radial displacements are more severely distorted than the axial displacements. The amplitudes of the radial and axial displacements decreased rapidly with increasing propagation distance. The ice thickness had a greater impact on the radial displacement than axial displacement; the thicker the ice, the greater the distortion for both radial and axial displacements.

Deploying Cationic Cellulose Nanofiber Confinement to Enable High Iodine Loadings Towards High Energy and High-Temperature Zn-I2 Battery.

High iodine loading and high-temperature adaptability of the iodine cathode are prerequisites to achieving high energy density at full battery level and promoting the practical application for the zinc-iodine (Zn-I2 ) battery. However, it would aggravate the polyiodide shuttle effect when employing high iodine loading and working temperature. Here, a sustainable cationic cellulose nanofiber (cCNF) was employed to confine the active iodine species through strong physiochemical adsorption to enlarge the iodine loading and stabilize it even at high temperatures. The cCNF could accommodate dual-functionality by enlarging the iodine loading and suppressing the polyiodide shuttle effect, owing to the unique framework structure with abundant surface positive charges. As a result, the iodine cathode based on the cCNF could deliver high iodine mass loading of 14.1 mg cm-2 with a specific capacity of 182.7 mAh g-1 , high areal capacity of 2.6 mAh cm-2 , and stable cycling over 3000 cycles at 2 A g-1 , thus enabling a high energy density of 34.8 Wh kg-1 and the maximum power density of 521.2 W kg-1 at a full Zn-I2 battery level. In addition, even at a high temperature of 60 °C, the Zn-I2 battery could still deliver a stable cycling.

Passive synthetic aperture for direction-of-arrival estimation using sparse Bayesian learning.

Passive synthetic aperture (PSA) extension for a moving array has the ability to enhance the accuracy of direction-of-arrival (DOA) estimation by constructing a larger virtual aperture. The array element overlap in array continuous measurements is required for the traditional extended towed array measurement (ETAM) methods. Otherwise, the phase factor estimation is biased, and the aperture extension fails when multiple sources exist. To solve this problem, passive aperture extension with sparse Bayesian learning (SBL) is proposed. In this method, SBL is used to simultaneously estimate the phase correction factors of different targets, followed by phase compensation applied to the extended aperture manifold vectors for DOA estimation. Simulation and experimental data results demonstrate that this proposed method successfully extends the aperture and provides higher azimuth resolution and accuracy compared to conventional beamforming (CBF) and SBL without extension. Compared with the traditional ETAM methods, the proposed method still performs well even when the array elements are not overlapped during the motion.

Difference frequency coherent matched autoproduct processing for source localization in deep ocean.

Matched autoproduct processing (MAP) refers to a matched field processing (MFP) style array signal processing technique for passive source localization, which interrogates frequency-difference autoproduct instead of genuine acoustic pressure. Due to frequency downshifting, MAP is less sensitive to environmental mismatch, but it suffers from low spatial resolution and a low peak-to-sidelobe ratio of ambiguity surface. These source localization metrics are herein improved with coherent approaches. Specifically, the coherent normalized MFP is extended to coherent matched autoproduct processing (CMAP), a difference frequency coherent algorithm that exploits correlations among the autoproducts at various difference frequencies and eliminates the phase factor of the source spectrum for passive source localization. Phase-only coherent matched autoproduct processing is a CMAP derivation technique that only uses phase information. Through simulations in a Munk sound-speed profile environment, sensitivity analysis in the South China Sea environment, and high signal-to-noise ratio experimental measurements, these two algorithms are validated as compared to the conventional MFP and incoherent MAP. Simulation investigations demonstrate that difference frequency coherent algorithms can suppress sidelobes while simultaneously enhancing the localization resolution and robustness. The experimental results generally support the findings of the simulations.

Deep-learning geoacoustic inversion using multi-range vertical array data in shallow water.

A multi-range vertical array data processing (MRP) method based on a convolutional neural network (CNN) is proposed to estimate geoacoustic parameters in shallow water. The network input is the normalized sample covariance matrices of the broadband multi-range data received by a vertical line array. Since the geoacoustic parameters (e.g., bottom sound speed, density, and attenuation) have different scales, the multi-task learning is used to estimate these parameters simultaneously. To reduce the influence of the uncertainty of the source position, the training and validation data are composed of the simulation data of different source depths. Simulation results demonstrate that compared with the conventional matched-field inversion (MFI), the CNN with MRP alleviates the coupling between the geoacoustic parameters and is more robust to different source depths in the shallow water environment. Based on the inversion results, better localization performance is achieved when the range-dependent environment is assumed to be a range-independent model. Real data from the East China Sea experiment are used to validate the MRP method. The results show that, compared with the MFI and the CNN with single-range vertical array data processing, the use of geoacoustic parameters from MRP achieves better localization performance.

Multiple source localization using learning-based sparse estimation in deep ocean.

This paper proposes the use of gated feedback gated recurrent unit network (GFGRU), a learning-based sparse estimation algorithm, for multiple source localization in the direct arrival zone of the deep ocean. The GFGRU, trained on sound field replicas of a single source generated by an acoustic propagation model, is used to estimate the ranges and depths of multiple sources without knowing the number of sources. The performance of GFGRU is compared to the Bartlett processor, feedforward neural network (FNN), and sparse Bayesian Learning (SBL) algorithm. Simulations indicate that GFGRU behaves similarly to SBL and offers modest localization performance improvement over the Bartlett and FNN in the presence of array tilt mismatch. The results of real data from the South China Sea also verify the robustness of the proposed GFGRU using a 105 m-aperture vertical array in the deep ocean.

Mode separation with one hydrophone in shallow water: A sparse Bayesian learning approach based on phase speed.

An approach of broadband mode separation in shallow water is proposed using phase speed extracted from one hydrophone and solved with sparse Bayesian learning (SBL). The approximate modal dispersion relation, connecting the horizontal wavenumbers (phase velocities) for multiple frequencies, is used to build the dictionary matrix for SBL. Given a multi-frequency pressure vector on one hydrophone, SBL estimates a set of sparse coefficients for a large number of atoms in the dictionary. With the estimated coefficients and corresponding atoms, the separated normal modes are retrieved. The presented method can be used for impulsive or known-form signals in a shallow-water environment while no bottom information is required. The simulation results demonstrate that the proposed approach is adapted to the environment where both the reflected and refracted modes coexist, whereas the performance of the time warping transformation degrades significantly in this scenario.

A multi-task learning convolutional neural network for source localization in deep ocean.

A multi-task learning (MTL) method with adaptively weighted losses applied to a convolutional neural network (CNN) is proposed to estimate the range and depth of an acoustic source in deep ocean. The network input is the normalized sample covariance matrices of the broadband data received by a vertical line array. To handle the environmental uncertainty, both the training and validation data are generated by an acoustic propagation model based on multiple possible sets of environmental parameters. The sensitivity analysis is investigated to examine the effect of mismatched environmental parameters on the localization performance in the South China Sea environment. Among the environmental parameters, the array tilt is found to be the most important factor on the localization. Simulation results demonstrate that, compared with the conventional matched field processing (MFP), the CNN with MTL performs better and is more robust to array tilt in the deep-ocean environment. Tests on real data from the South China Sea also validate the method. In the specific ranges where the MFP fails, the method reliably estimates the ranges and depths of the underwater acoustic source.

Measurement and modeling of sound propagation over continental slope in the South China Sea.

Measurements along two ship tracks were obtained in an experiment to investigate the properties of acoustic propagation over the continental slope in the South China Sea. The measured data show a notable difference in transmission loss about 35 dB as sound crosses different geodesic paths. Numerical simulations indicate that the range and azimuth-dependent geological properties control the level of the transmission loss and lead to this large transmission loss fluctuation. In addition, the model also suggests some small-scale features of horizontal refraction effect caused by irregular topography, but they are not observed in the measured data.

Block sparse Bayesian learning for broadband mode extraction in shallow water from a vertical array.

The horizontal wavenumbers and modal depth functions are estimated by block sparse Bayesian learning (BSBL) for broadband signals received by a vertical line array in shallow-water waveguides. The dictionary matrix consists of multi-frequency modal depth functions derived from shooting methods given a large set of hypothetical horizontal wavenumbers. The dispersion relation for multi-frequency horizontal wavenumbers is also taken into account to generate the dictionary. In this dictionary, only a few of the entries are used to describe the pressure field. These entries represent the modal depth functions and associated wavenumbers. With the constraint of block sparsity, the BSBL approach is shown to retrieve the horizontal wavenumbers and corresponding modal depth functions with high precision, while a priori knowledge of sea bottom, moving source, and source locations is not needed. The performance is demonstrated by simulations and experimental data.

Improving Cancer Immunotherapy Outcomes Using Biomaterials.

Immunotherapy has made great strides in improving clinical outcomes in cancer treatment. However, few patients exhibit adequate response rates for key outcome measures and desired long-term responses, and they often suffer systemic side effects due to the dynamic nature of the immune system. This has motivated a search for alternative strategies to improve unsatisfactory immunotherapeutic outcomes. In recent years, biomaterial-assisted immunotherapy has shown promise in cancer treatment with improved therapeutic efficacy and reduced side effects. These biomaterials have illuminated fundamental mechanisms underlying the immunoediting process, while greatly improving the efficacy of chimeric antigen receptor (CAR) T-cell therapy, cancer vaccine therapy, and immune checkpoint blockade therapy. This Minireview discusses recent advances in engineered biomaterials that address limitations associated with conventional cancer immunotherapies.

Donor/Acceptor-Induced Ratiometric Photoelectrochemical Paper Analytical Device with a Hollow Double-Hydrophilic-Walls Channel for microRNA Quantification.

Integrating ratiometric photoelectrochemical (PEC) techniques with paper microfluidics to construct a ratiometric PEC paper analytical device for practical application is often restricted by the grave dependence of ratiometric assay on photoactive materials and low mass-transfer rates of the paper channel. Herein, a universal donor/acceptor-induced ratiometric PEC paper analytical device with a hollow double-hydrophilic-walls channel (HDHC) was fabricated for high-performance microRNA-141 (miRNA-141) quantification. Concretely, a photoanode and photocathode were integrated on the paper-based sensing platform in which the photocathode served as a biosensing site for the pursuit of higher selectivity. For formulation of a cascading signal amplification strategy, a unique duplex-specific nuclease-induced target recycling reaction was engineered for the output of a double amount of all useful DNA linkers instead of conventional output of only one available DNA product, which could guarantee the output of abundant DNA linkers with the initiation of a cascade of hybridization chain reaction on both the trunk and branch in the presence of miRNA-141. Then the formed dendriform polymeric DNA duplex structures were further decorated with glucose oxidase (GOx)-mimicking gold nanoparticles by the electrostatic interaction to form a branchy gold tree (BGT). Profiting from the perfect GOx-mimicking activity of BGT and high mass-transfer rates of HDHC, the cathodic photocurrent from Ag2S/Cu2O hybrid structure was in a "signal off" state while the anodic photocurrent from graphene quantum dots (GQDs) and Ag2Se QDs cosensitized ZnO nanosheets was in a "signal on" state because BGT-catalyzed glucose oxidation reaction evoked the consumption of dissolved O2 as an electron acceptor and the generation of H2O2 as an electron donor. With calculation of the ratio of two photocurrent intensities, the quantitative detection of miRNA-141 was achieved with high sensitivity, accuracy, and reliability.

Long-term ambient noise statistics in the northeast South China Sea.

This paper reports on the long-term statistics of ambient noise (50-2000 Hz) in the northeast South China Sea. The data were collected from July 2016 to March 2018. The long-term statistics, seasonal, and diel variations of ambient noise are analyzed. There are significant seasonal variations from 500 to 2000 Hz. The ambient noise level at 1000 Hz is 6-10 dB higher in winter than in summer. There is only a small difference between the day and night comparison (less than 1.6 dB). The results are significant to evaluate and improve the performance of underwater acoustic systems in this area.

Deep-learning source localization using multi-frequency magnitude-only data.

A deep learning approach based on big data is proposed to locate broadband acoustic sources using a single hydrophone in ocean waveguides with uncertain bottom parameters. Several 50-layer residual neural networks, trained on a huge number of sound field replicas generated by an acoustic propagation model, are used to handle the bottom uncertainty in source localization. A two-step training strategy is presented to improve the training of the deep models. First, the range is discretized in a coarse (5 km) grid. Subsequently, the source range within the selected interval and source depth are discretized on a finer (0.1 km and 2 m) grid. The deep learning methods were demonstrated for simulated magnitude-only multi-frequency data in uncertain environments. Experimental data from the China Yellow Sea also validated the approach.

Dual-Stimuli Responsive Bismuth Nanoraspberries for Multimodal Imaging and Combined Cancer Therapy.

Development of stimuli-responsive theranostics is of great importance for precise cancer diagnosis and treatment. Herein, bovine serum albumin (BSA) modified bismuth nanoraspberries (Bi-BSA NRs) are developed as cancer theranostic agents for multimodal imaging and chemo-photothermal combination therapy. The Bi-BSA NRs are synthesized in aqueous phase via a facile reduction method using Bi2O3 nanospheres as the sacrificial template. The morphology, biocompatibility, photothermal effect, drug loading/releasing abilities, chemotherapy effect, synergistic chemo-photothermal therapy efficacy, and multimodal imaging capacities of Bi-BSA NRs have been investigated. The results show that the NRs possess multiple unique features including (i) raspberry-like morphology with high specific surface area (∼52.24 m2·g-1) and large cavity (total pore volume ∼0.30 cm3·g-1), promising high drug loading capacity (∼69 wt %); (ii) dual-stimuli responsive drug release, triggered by acidic pH and NIR laser irradiation; (iii) infrared thermal (IRT), photoacoustic (PA) and X-ray computed tomography (CT) trimodality imaging with the CT contrast enhanced efficiency as high as ∼66.7 HU·mL·mg-1; (iv) 100% tumor elimination through the combination chemo-photothermal therapy. Our work highlights the great potentials of Bi-BSA NRs as a versatile theranostics for multimodal imaging and combination therapy.

Activator Protein-2ß Promotes Tumor Growth and Predicts Poor Prognosis in Breast Cancer.

BACKGROUND/AIMS: Activator protein-2 (AP-2) transcription factors have been proved to be essential in maintaining cellular homeostasis and regulating the transformation from normal growth to neoplasia. However, the role of AP-2ß, a key member of AP-2 family, in breast cancer is rarely reported. METHODS: The effect of AP-2 on cell growth, migration and invasion in breast cancer cells were measured by MTT, colony formation, wound-healing and transwell assays, respectively. The expression levels of AP-2ß and other specific markers in breast cancer cell lines and tissue microarrays from the patients were detected using RT-PCR, Western blot and immunohistochemical staining. The regulation of AP-2ß on tumor growth in vivo was analyzed in a mouse xenograft model. RESULTS: We demonstrated the tumor-promoting function of AP-2ß in breast cancer. AP-2ß was found to be highly expressed in breast cancer cell lines and tumor tissues of breast cancer patients. The shRNA-mediated silencing of AP-2ß led to the dramatic inhibition of cell proliferation, colony formation ability, migration and invasiveness in breast cancer cells accompanied by the down-regulated expression of some key proteins involved in cancer progression, including p75, MMP-2, MMP-9, C-Jun, p-ERK and STAT3. Overexpression of AP-2ß markedly up-regulated the levels of these proteins. Consistent with the in vitro study, the silencing or overexpression of AP-2ß blocked or promoted tumor growth in the mice with xenografts of breast cancers. Notably, the high AP-2ß expression levels was correlated with poor prognosis and advanced malignancy in patients with breast cancer. CONCLUSIONS: Our study demonstrates that AP-2ß promotes tumor growth and predicts poor prognosis, and may represent a potential therapeutic target for breast cancer.

Liquid Exfoliation of Colloidal Rhenium Disulfide Nanosheets as a Multifunctional Theranostic Agent for In Vivo Photoacoustic/CT Imaging and Photothermal Therapy.

Near-infrared light-mediated theranostic agents with superior tissue penetration and minimal invasion have captivated researchers in cancer research in the past decade. Herein, a probe sonication-assisted liquid exfoliation approach for scalable and continual synthesis of colloidal rhenium disulfide nanosheets, which is further explored as theranostic agents for cancer diagnosis and therapy, is reported. Due to high-Z element of Re (Z = 75) and significant photoacoustic effect, the obtained PVP-capped ReS2 nanosheets are evaluated as bimodality contrast agents for computed tomography and photoacoustic imaging. In addition, utilizing the strong near-infrared absorption and ultrahigh photothermal conversion efficiency (79.2%), ReS2 nanosheets could also serve as therapeutic agents for photothermal ablation of tumors with a tumor elimination rate up to 100%. Importantly, ReS2 nanosheets show no obvious toxicity based on the cytotoxicity assay, serum biochemistry, and histological analysis. This work highlights the potentials of ReS2 nanosheets as a single-component theranostic nanoplatform for bioimaging and antitumor therapy.

Phase-Transition Induced Conversion into a Photothermal Material: Quasi-Metallic WO2.9 Nanorods for Solar Water Evaporation and Anticancer Photothermal Therapy.

Phase transition from WO3 to sub-stoichiometric WO2.9 by a facile method has varied the typical semiconductor to be quasi-metallic with a narrowed band gap and a shifted Femi energy to the conduction band, while maintaining a high crystallinity. The resultant WO2.9 nanorods possess a high total absorption capacity (ca. 90.6 %) over the whole solar spectrum as well as significant photothermal conversion capability, affording a conversion efficiency as high as around 86.9 % and a water evaporation efficiency of about 81 % upon solar light irradiation. Meanwhile, the promising potential of the nanorods for anticancer photothermal therapy have been also demonstrated, with a high photothermal conversion efficiency (ca. 44.9 %) upon single wavelength near-infrared irradiation and a high tumor inhibition rate (ca. 98.5 %). This study may have opened up a feasible route to produce high-performance photothermal materials from well-developed oxides.