Physics-informed neural networks in support of modal wavenumber estimation.

A physics-informed neural network (PINN) enables the estimation of horizontal modal wavenumbers using ocean pressure data measured at multiple ranges. Mode representations for the ocean acoustic pressure field are derived from the Hankel transform relationship between the depth-dependent Green's function in the horizontal wavenumber domain and the field in the range domain. We obtain wavenumbers by transforming the range samples to the wavenumber domain, and maintaining range coherence of the data is crucial for accurate wavenumber estimation. In the ocean environment, the sensitivity of phase variations in range often leads to degradation in range coherence. To address this, we propose using OceanPINN [Yoon, Park, Gerstoft, and Seong, J. Acoust. Soc. Am. 155(3), 2037-2049 (2024)] to manage spatially non-coherent data. OceanPINN is trained using the magnitude of the data and predicts phase-refined data. Modal wavenumber estimation methods are then applied to this refined data, where the enhanced range coherence results in improved accuracy. Additionally, sparse Bayesian learning, with its high-resolution capability, further improves the modal wavenumber estimation. The effectiveness of the proposed approach is validated through its application to both simulated and SWellEx-96 experimental data.

Geoacoustic inversion using Bayesian optimization with a Gaussian process surrogate model.

Geoacoustic inversion can be a computationally expensive task in high-dimensional parameter spaces, typically requiring thousands of forward model evaluations to estimate the geoacoustic environment. We demonstrate Bayesian optimization (BO), an efficient global optimization method capable of estimating geoacoustic parameters in seven-dimensional space within 100 evaluations instead of thousands. BO iteratively searches parameter space for the global optimum of an objective function, defined in this study as the Bartlett power. Each step consists of fitting a Gaussian process surrogate model to observed data and then choosing a new point to evaluate using a heuristic acquisition function. The ideal acquisition function balances exploration of the parameter space in regions with high uncertainty with exploitation of high-performing regions. Three acquisition functions are evaluated: upper confidence bound, expected improvement (EI), and logarithmically transformed EI. BO is demonstrated for both simulated and experimental data from a shallow-water environment and rapidly estimates optimal parameters while yielding results comparable to differential evolution optimization.

Predicting ocean pressure field with a physics-informed neural network.

Ocean sound pressure field prediction, based on partially measured pressure magnitudes at different range-depths, is presented. Our proposed machine learning strategy employs a trained neural network with range-depth as input and outputs complex acoustic pressure at the location. We utilize a physics-informed neural network (PINN), fitting sampled data while considering the additional information provided by the partial differential equation (PDE) governing the ocean sound pressure field. In vast ocean environments with kilometer-scale ranges, pressure fields exhibit rapidly fluctuating phases, even at frequencies below 100 Hz, posing a challenge for neural networks to converge to accurate solutions. To address this, we utilize the envelope function from the parabolic-equation technique, fundamental in ocean sound propagation modeling. The envelope function shows slower variations across ranges, enabling PINNs to predict sound pressure in an ocean waveguide more effectively. Additional PDE information allows PINNs to capture PDE solutions even with a limited amount of training data, distinguishing them from purely data-driven machine learning approaches that require extensive datasets. Our approach is validated through simulations and using data from the SWellEx-96 experiment.

Graph-based sequential beamforming.

This paper presents a Bayesian estimation method for sequential direction finding. The proposed method estimates the number of directions of arrivals (DOAs) and their DOAs performing operations on the factor graph. The graph represents a statistical model for sequential beamforming. At each time step, belief propagation predicts the number of DOAs and their DOAs using posterior probability density functions (pdfs) from the previous time and a different Bernoulli-von Mises state transition model. Variational Bayesian inference then updates the number of DOAs and their DOAs. The method promotes sparse solutions through a Bernoulli-Gaussian amplitude model, is gridless, and provides marginal posterior pdfs from which DOA estimates and their uncertainties can be extracted. Compared to nonsequential approaches, the method can reduce DOA estimation errors in scenarios involving multiple time steps and time-varying DOAs. Simulation results demonstrate performance improvements compared to state-of-the-art methods. The proposed method is evaluated using ocean acoustic experimental data.

Bayesian optimization with Gaussian process surrogate model for source localization.

Source localization with a geoacoustic model requires optimizing the model over a parameter space of range and depth with the objective of matching a predicted sound field to a field measured on an array. We propose a sample-efficient sequential Bayesian optimization strategy that models the objective function as a Gaussian process (GP) surrogate model conditioned on observed data. Using the mean and covariance functions of the GP, a heuristic acquisition function proposes a candidate in parameter space to sample, balancing exploitation (sampling around the best observed objective function value) and exploration (sampling in regions of high variance in the GP). The candidate sample is evaluated, and the GP conditioned on the updated data. Optimization proceeds sequentially until a fixed budget of evaluations is expended. We demonstrate source localization for a shallow-water waveguide using Monte Carlo simulations and experimental data from an acoustic source tow. Compared to grid search and quasi-random sampling strategies, simulations and experimental results indicate the Bayesian optimization strategy converges on optimal solutions rapidly.

Compressive frequency-difference direction-of-arrival estimation.

Direction-of-arrival estimation is difficult for signals spatially undersampled by more than half the wavelength. Frequency-difference beamforming [Abadi, Song, and Dowling (2012). J. Acoust. Soc. Am. 132, 3018-3029] offers an alternative approach to avoid such spatial aliasing by using multifrequency signals and processing them at a lower frequency, the difference-frequency. As with the conventional beamforming method, lowering the processing frequency sacrifices spatial resolution due to a beam broadening. Thus, unconventional beamforming is detrimental to the ability to distinguish between closely spaced targets. To overcome spatial resolution deterioration, we propose a simple yet effective method by formulating the frequency-difference beamforming as a sparse signal reconstruction problem. Similar to compressive beamforming, the improvement (compressive frequency-difference beamforming) promotes sparse nonzero elements to obtain a sharp estimate of the spatial direction-of-arrival spectrum. Analysis of the resolution limit demonstrates that the proposed method outperforms the conventional frequency-difference beamforming in terms of separation if the signal-to-noise ratio exceeds 4 dB. Ocean data from the FAF06 experiment support the validity.

Gridless sparse covariance-based beamforming via alternating projections including co-prime arrays.

This paper presents gridless sparse processing for direction-of-arrival (DOA) estimation. The method solves a gridless version of sparse covariance-based estimation using alternating projections. Gridless sparse DOA estimation is represented by the reconstruction of Toeplitz-structured low-rank matrices, which our method recovers by alternatively projecting a solution matrix. Compared to the existing gridless sparse methods, our method improves speed and accuracy and considers non-uniformly configured linear arrays. High-resolution and reliable DOA estimation are achieved even with single-snapshot data, coherent sources, and non-uniform arrays. Simulation results demonstrate performance improvements compared to the existing DOA estimators, including gridless sparse methods. The method is illustrated using experimental data from a real ocean experiment.

Response to the letter 'Absent regulation of iron acquisition by the copper regulator Mac1 in A. fumigatus'.

The answer to the letter 'Absent regulation of iron acquisition by the copper regulator Mac1 in A. fumigatus' has been prepared. We explained our data and showed supplementary information to answer the questions. And we respect the results of other groups first and explain the differences from our results.

Sequential sparse Bayesian learning for time-varying direction of arrival.

This paper presents methods for the estimation of the time-varying directions of arrival (DOAs) of signals emitted by moving sources. Following the sparse Bayesian learning (SBL) framework, prior information of unknown source amplitudes is modeled as a multi-variate Gaussian distribution with zero-mean and time-varying variance parameters. For sequential estimation of the unknown variance, we present two sequential SBL-based methods that propagate statistical information across time to improve DOA estimation performance. The first method heuristically calculates the parameters of an inverse-gamma hyperprior based on the source signal estimate from the previous time step. In addition, a second sequential SBL method is proposed, which performs a prediction step to calculate the prior distribution of the current variance parameter from the variance parameter estimated at the previous time step. The SBL-based sequential processing provides high-resolution DOA tracking capabilities. Performance improvements are demonstrated by using simulated data as well as real data from the SWellEx-96 experiment.

Block-sparse two-dimensional off-grid beamforming with arbitrary planar array geometry.

For a sound field observed on a planar sensor array, compressive sensing (CS) reconstructs the two-dimensional (2D) direction-of-arrival (DOA) of multiple sources using a sparsity constraint. Conventional compressive beamforming methods suffer from grid mismatch, where true DOAs do not fall on the discretized angular search grid. This paper adopts a CS-based model, which can reconstruct block-sparse signals, and the model treats DOAs and the off-grid DOA compensation parts as blocks to deal with the off-grid 2D beamforming. The method is illustrated by numerical simulations and shows high estimation accuracy. Also, the approach does not require a specific array configuration and is suitable for arbitrary planar array geometry, which is practically useful. Since propeller tip vortex cavitation induces noise sources located sparsely near the propeller tip, the high-resolution of the method is demonstrated with experimental data from cavitation tunnel experiments.

Incipient tip vortex cavitation localization using block-sparse compressive sensing.

Noise induced by incipient-propeller tip vortex cavitation (TVC) has a few sources near the propeller tips, which radiate a broadband signal. This article describes a compressive sensing (CS)-based TVC localization technique for coherent multiple-frequency processing, which jointly processes the measured data at multiple frequencies. Block-sparse CS, which groups several single-frequency measurements into blocks, is adopted for coherent multiple-frequency processing. The coherent multiple-frequency processing improves localization performance over that of single-frequency processing. Unlike single-frequency processing using conventional CS, which combines independent single-frequency measurement treatments by averaging, coherent multiple-frequency processing produces accurate localization without requiring a sufficient number of treated frequencies, long-time-sampled data with a time-invariant signal assumption, or even a single cavitation event. The approach is demonstrated on experimental data from a transducer source experiment and a cavitation source experiment.

Detection of Direction-Of-Arrival in Time Domain Using Compressive Time Delay Estimation with Single and Multiple Measurements.

The compressive time delay estimation (TDE) is combined with delay-and-sum beamforming to obtain direction-of-arrival (DOA) estimates in the time domain. Generally, the matched filter that detects the arrivals at the hydrophone is used with beamforming. However, when the ocean noise smears the arrivals, ambiguities appear in the beamforming results, degrading the DOA estimation. In this work, compressive sensing (CS) is applied to accurately evaluate the arrivals by suppressing the noise, which enables the correct detection of arrivals. For this purpose, CS is used in two steps. First, the candidate time delays for the actual arrivals are calculated in the continuous time domain using a grid-free CS. Then, the dominant arrivals constituting the received signal are selected by a conventional CS using the time delays in the discrete time domain. Basically, the compressive TDE is used with a single measurement. To further reduce the noise, common arrivals over multiple measurements, which are obtained using the extended compressive TDE, are exploited. The delay-and-sum beamforming technique using refined arrival estimates provides more pronounced DOAs. The proposed scheme is applied to shallow-water acoustic variability experiment 15 (SAVEX15) measurement data to demonstrate its validity.

The Role of Zinc in Copper Homeostasis of Aspergillus fumigatus.

Copper is an essential metal ion that performs many physiological functions in living organisms. Deletion of Afmac1, which is a copper-responsive transcriptional activator in A. fumigatus, results in a growth defect on aspergillus minimal medium (AMM). Interestingly, we found that zinc starvation suppressed the growth defect of the Δafmac1 strain on AMM. In addition, the growth defect of the Δafmac1 strain was recovered by copper supplementation or introduction of the CtrC gene into the Δafmac1 strain. However, chelation of copper by addition of BCS to AMM failed to recover the growth defect of the Δafmac1 strain. Through Northern blot analysis, we found that zinc starvation upregulated CtrC and CtrA2, which encode membrane copper transporters. Interestingly, we found that the conserved ZafA binding motif 5'-CAA(G)GGT-3' was present in the upstream region of CtrC and CtrA2 and that mutation of the binding motif led to failure of ZafA binding to the upstream region of CtrC and upregulation of CtrC expression under zinc starvation. Furthermore, the binding activity of ZafA to the upstream region of CtrC was inversely proportional to the zinc concentration, and copper inhibited the binding of ZafA to the upstream region of CtrC under a low zinc concentration. Taken together, these results suggest that ZafA upregulates copper metabolism by binding to the ZafA binding motif in the CtrC promoter region under low zinc concentration, thus regulating copper homeostasis. Furthermore, we found that copper and zinc interact in cells to maintain metal homeostasis.

A copper transcription factor, AfMac1, regulates both iron and copper homeostasis in the opportunistic fungal pathogen Aspergillus fumigatus.

Although iron and copper are co-ordinately regulated in living cells, the homeostatic effects of each of these metals on the other remain unknown. Here, we show the function of AfMac1, a transcriptional activator of the copper and iron regulons of Aspergillus fumigatus, on the interaction between iron and copper. In addition to the copper-specific AfMac1-binding motif 5'-TGTGCTCA-3' found in the promoter region of ctrC, the iron-specific AfMac1-binding motif 5'-AT(C/G)NN(A/T)T(A/C)-3' was identified in the iron regulon but not in the copper regulon by ChIP sequence analysis. Furthermore, mutation of the AfMac1-binding motif of sit1 eliminated AfMac1-mediated sit1 up-regulation. Interestingly, the regulation of gene expression in the iron regulon by AfMac1 was not affected by copper and vice versa AfMac1 localized to the nucleus under iron- or copper-depleted conditions, and AfMac1 was mostly detected in the cytoplasm under iron- or copper-replete conditions. Taken together, these results suggest that A. fumigatus independently regulates iron and copper homeostasis in a manner that involves AfMac1 and mutual interactions.

Grid-free compressive mode extraction.

A grid-free compressive sensing (CS) based method for extracting the normal modes of acoustic propagation in the ocean waveguide from vertical line array (VLA) data is presented. Extracting the normal modes involves the estimation of mode horizontal wavenumbers and the corresponding mode shapes. Sparse representation of the waveguide propagation using modes at discrete horizontal wavenumbers enables CS to be applied. Grid-free CS, based on group total-variation norm minimization, is adopted to mitigate the issues of the wavenumber search grid discretization in the conventional CS. In addition, the suggested method can process multiple sensor data jointly, which improves performance in estimation over single sensor data processing. The method here uses data on a VLA from a source at several ranges, and processes the multiple sensor data at different depths jointly. The grid-free CS extracts the mode wavenumbers and shapes even with no a priori environmental knowledge, a partial water column spanning array data, and without the mode orthogonality condition. The approach is illustrated by numerical simulations and experimental SWellEx-96 (shallow water evaluation cell experiment 1996) data.

The Role of Zinc in Gliotoxin Biosynthesis of Aspergillus fumigatus.

Zinc performs diverse physiological functions, and virtually all living organisms require zinc as an essential trace element. To identify the detailed function of zinc in fungal pathogenicity, we carried out cDNA microarray analysis using the model system of Aspergillus fumigatus, a fungal pathogen. From microarray analysis, we found that the genes involved in gliotoxin biosynthesis were upregulated when zinc was depleted, and the microarray data were confirmed by northern blot analysis. In particular, zinc deficiency upregulated the expression of GliZ, which encodes a Zn2-Cys6 binuclear transcription factor that regulates the expression of the genes required for gliotoxin biosynthesis. The production of gliotoxin was decreased in a manner inversely proportional to the zinc concentration, and the same result was investigated in the absence of ZafA, which is a zinc-dependent transcription activator. Interestingly, we found two conserved ZafA-binding motifs, 5'-CAAGGT-3', in the upstream region of GliZ on the genome and discovered that deletion of the ZafA-binding motifs resulted in loss of ZafA-binding activity; gliotoxin production was decreased dramatically, as demonstrated with a GliZ deletion mutant. Furthermore, mutation of the ZafA-binding motifs resulted in an increase in the conidial killing activity of human macrophage and neutrophil cells, and virulence was decreased in a murine model. Finally, transcriptomic analysis revealed that the expression of ZafA and GliZ was upregulated during phagocytosis by macrophages. Taken together, these results suggest that zinc plays an important role in the pathogenicity of A. fumigatus by regulating gliotoxin production during the phagocytosis pathway to overcome the host defense system.

Switchable electrorheological activity of polyacrylonitrile microspheres by thermal treatment: from negative to positive.

The study focuses on the effect of thermal deformation degree of polyacrylonitrile (PAN) particles on the electrorheological (ER) properties of their suspensions. The heat-treated PAN particles are manufactured as ER materials using a thermocatalytic processes. The molecular structures of ER materials are analyzed to confirm a stabilization or a carbonization degree. We categorized the prepared ER particles into three basic types according to their deformation degree: Thermal dried PAN, stabilized PAN, and pre-carbonized PAN. The raw, stabilized, and pre-carbonized PAN particle-dispersed suspensions showed positive ER properties. The ER properties are enhanced as the heat-treatment temperature increases due to improved dielectric property. However, the thermal dried PAN particle ER suspensions showed negative ER behavior though the particles have higher conductivity and dielectric constants than those of the host fluid, which is contrary to the conduction model. XRD results indicate that the ER materials could show contradictory ER behavior even if they have the same molecular structures due to their crystallinity. This discovery is expected to boost the development of both positive ER and negative ER suspensions based on carbonaceous ER materials.

Functional characterization of the copper transcription factor AfMac1 from Aspergillus fumigatus.

Although copper functions as a cofactor in many physiological processes, copper overload leads to harmful effects in living cells. Thus, copper homeostasis is tightly regulated. However, detailed copper metabolic pathways have not yet been identified in filamentous fungi. In this report, we investigated the copper transcription factor AfMac1 ( Aspergillus fumigatusMac1 homolog) and identified its regulatory mechanism in A. fumigatus AfMac1 has domains homologous to the DNA-binding and copper-binding domains of Mac1 from Saccharomyces cerevisiae, and AfMac1 efficiently complemented Mac1 in S. cerevisiae Expression of Afmac1 resulted in CTR1 up-regulation, and mutation of the DNA-binding domain of Afmac1 failed to activate CTR1 expression in S. cerevisiae The Afmac1 deletion strain of A. fumigatus failed to grow in copper-limited media, and its growth was restored by introducing ctrC We found that AfMac1 specifically bound to the promoter region of ctrC based on EMSA. The AfMac1-binding motif 5'-TGTGCTCA-3' was identified from the promoter region of ctrC, and the addition of mutant ctrC lacking the AfMac1-binding motif failed to up-regulate ctrC in A. fumigatus Furthermore, deletion of Afmac1 significantly reduced strain virulence and activated conidial killing activity by neutrophils and macrophages. Taken together, these results suggest that AfMac1 is a copper transcription factor that regulates cellular copper homeostasis in A. fumigatus.

Multiple snapshot grid free compressive beamforming.

Compressive sensing (CS) based estimation technique utilizes a sparsity promoting constraint and solves the direction-of-arrival (DOA) estimation problem efficiently with high resolution. In this paper a grid free CS based DOA estimation technique is proposed, which uses sequential multiple snapshot data. Conventional CS technique suffers from a basis mismatch issue, while grid free CS technique is relieved of basis mismatch problem. Moreover, when the DOAs are stationary, multiple snapshot processing provides stable estimates over fluctuating single snapshot processing results. For multiple snapshot processing, the generalized version of total variation norm (group total variation norm) is implemented to impose a common sparsity pattern of multiple snapshot solution vectors in a continuous angular domain. Furthermore, an extended version is proposed using the singular value decomposition technique to mitigate computational complexity resulting from a large number of multiple snapshots. Data from SWellEx-96 are used to examine the proposed method. From the experimental data, it was observed that the present method not only offers high resolution even when the sources are coherent, but also the basis mismatch in the conventional CS method can be avoided.

Identification of ferrichrome- and ferrioxamine B-mediated iron uptake by Aspergillus fumigatus.

Aspergillus fumigatus is an opportunistic fungal pathogen for immunocompromised patients, and genes involved in siderophore metabolism have been identified as virulence factors. Recently, we identified the membrane transporters sit1 and sit2, which are putative virulence factors of A. fumigatus; sit1 and sit2 are homologous to yeast Sit1, and sit1 and sit2 gene expression was up-regulated after iron depletion. When expressed heterologously in Saccharomyces cerevisiae, sit1 and sit2 were localized to the plasma membrane; sit1 efficiently complemented ferrichrome (FC) and ferrioxamine B (FOB) uptake in yeast cells, whereas sit2 complemented only FC uptake. Deletion of sit1 resulted in a decrease in FOB and FC uptake, and deletion of sit2 resulted in a decrease in FC uptake in A. fumigatus It is of interest that a sit1 and sit2 double-deletion mutant resulted in a synergistic decrease in FC uptake activity. Both sit1 and sit2 were localized to the plasma membrane in A. fumigatus The expression levels of the sit1 and sit2 genes were dependent on hapX under low-but not high-iron conditions. Furthermore, mirB, and sidA gene expression was up-regulated and sreA expression down-regulated when sit1 and sit2 were deleted. Although sit1 and sit2 failed to affect mouse survival rate, these genes affected conidial killing activity. Taken together, our results suggest that sit1 and sit2 are siderophore transporters and putative virulence factors localized to the plasma membrane.