Repairing distorted hologram data for sound field reconstruction.

This paper proposes a distorted hologram data repair approach for sound field reconstruction. In this approach, an equivalent source model is established by placing a set of equivalent sources near the hologram surface to represent the measured hologram pressures. Each hologram pressure is simultaneously assigned an indicator to describe whether its measurement is corrupted by errors or not. This model is then formulated within a modal framework by utilizing the modes generated through the singular value decomposition of the transfer matrix between the hologram and nearby equivalent source surfaces. Subsequently, the indicators and modal coefficients are assigned the 0-1 and Gaussian prior distributions, respectively, and their posterior distributions are derived using the Bayesian method. The means of the posterior distributions are calculated to discriminate corrupted measurements and repair distorted hologram pressures. Repaired hologram pressures are finally utilized for reconstructions using the equivalent source method. Results from both numerical simulations conducted under various parameter settings and two experiments demonstrate the effectiveness of the proposed approach in automatically discriminating all the corrupted measurements and accurately repairing the distorted hologram pressures. Furthermore, the accuracy of the reconstructions using the repaired hologram pressures is comparable to that achieved with the correctly measured pressures.

Upregulation of LHPP by saRNA inhibited hepatocellular cancer cell proliferation and xenograft tumor growth.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer worldwide and no pharmacological treatment is available that can achieve complete remission of HCC. Phospholysine phosphohistidine inorganic pyrophosphate phosphatase (LHPP) is a recently identified HCC tumor suppressor gene which plays an important role in the development of HCC and its inactivation and reactivation has been shown to result in respectively HCC tumorigenesis and suppression. Small activating RNAs (saRNAs) have been used to achieve targeted activation of therapeutic genes for the restoration of their encoded protein through the RNAa mechanism. Here we designed and validated saRNAs that could activate LHPP expression at both the mRNA and protein levels in HCC cells. Activation of LHPP by its saRNAs led to the suppression of HCC proliferation, migration and the inhibition of Akt phosphorylation. When combined with targeted anticancer drugs (e.g., regorafenib), LHPP saRNA exhibited synergistic effect in inhibiting in vitro HCC proliferation and in vivo antitumor growth in a xenograft HCC model. Findings from this study provides further evidence for a tumor suppressor role of LHPP and potential therapeutic value of restoring the expression of LHPP by saRNA for the treatment of HCC.

Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults.

The functional connectome changes with aging. We systematically evaluated aging related alterations in the functional connectome using a whole-brain connectome network analysis in 39,675 participants in UK Biobank project. We used adaptive dense network discovery tools to identify networks directly associated with aging from resting-state fMRI data. We replicated our findings in 499 participants from the Lifespan Human Connectome Project in Aging study. The results consistently revealed two motor-related subnetworks (both permutation test p-values <0.001) that showed a decline in resting-state functional connectivity (rsFC) with increasing age. The first network primarily comprises sensorimotor and dorsal/ventral attention regions from precentral gyrus, postcentral gyrus, superior temporal gyrus, and insular gyrus, while the second network is exclusively composed of basal ganglia regions, namely the caudate, putamen, and globus pallidus. Path analysis indicates that white matter fractional anisotropy mediates 19.6% (p<0.001, 95% CI [7.6% 36.0%]) and 11.5% (p<0.001, 95% CI [6.3% 17.0%]) of the age-related decrease in both networks, respectively. The total volume of white matter hyperintensity mediates 32.1% (p<0.001, 95% CI [16.8% 53.0%]) of the aging-related effect on rsFC in the first subnetwork.

Real-time nearfield acoustic holography using particle velocity.

In this paper, a series of impulse response functions between acoustic quantities on the source plane and particle velocity on the hologram plane are derived. In virtue of these functions, real-time nearfield acoustic holography (RT-NAH) is extended from pressure-based to particle velocity. Pressure, normal velocity, acceleration, and displacement radiated from planar sources can be reconstructed by measuring time-dependent particle velocity signals on the hologram plane. A simulation of an excited aluminum plate is performed to evaluate the difference in accuracy between RT-NAHs based on pressure and based on particle velocity. This study also examines the impact of impulse response functions on the reconstruction results, allowing for detailed analysis of the reconstruction accuracy based on these functions. The simulation results demonstrate that using RT-NAH based on particle velocity obtains significantly higher-accuracy reconstruction results when reconstructing normal velocity and displacement and slightly more accurate reconstructed pressure and normal acceleration.

Identifying covariate-related subnetworks for whole-brain connectome analysis.

Whole-brain connectome data characterize the connections among distributed neural populations as a set of edges in a large network, and neuroscience research aims to systematically investigate associations between brain connectome and clinical or experimental conditions as covariates. A covariate is often related to a number of edges connecting multiple brain areas in an organized structure. However, in practice, neither the covariate-related edges nor the structure is known. Therefore, the understanding of underlying neural mechanisms relies on statistical methods that are capable of simultaneously identifying covariate-related connections and recognizing their network topological structures. The task can be challenging because of false-positive noise and almost infinite possibilities of edges combining into subnetworks. To address these challenges, we propose a new statistical approach to handle multivariate edge variables as outcomes and output covariate-related subnetworks. We first study the graph properties of covariate-related subnetworks from a graph and combinatorics perspective and accordingly bridge the inference for individual connectome edges and covariate-related subnetworks. Next, we develop efficient algorithms to exact covariate-related subnetworks from the whole-brain connectome data with an $\ell_0$ norm penalty. We validate the proposed methods based on an extensive simulation study, and we benchmark our performance against existing methods. Using our proposed method, we analyze two separate resting-state functional magnetic resonance imaging data sets for schizophrenia research and obtain highly replicable disease-related subnetworks.

APOE4 poses opposite effects of plasma LDL on white matter integrity in older adults.

INTRODUCTION: APOE4 is a strong genetic risk factor of Alzheimer's disease and is associated with changes in metabolism. However, the interactive relationship between APOE4 and plasma metabolites on the brain remains largely unknown. MEHODS: In the UK Biobank, we investigated the moderation effects of APOE4 on the relationship between 249 plasma metabolites derived from nuclear magnetic resonance spectroscopy on whole-brain white matter integrity, measured by fractional anisotropy using diffusion magnetic resonance imaging. RESULTS: The increase in the concentration of metabolites, mainly LDL and VLDL, is associated with a decrease in white matter integrity (b= -0.12, CI= [-0.14, -0.10]) among older APOE4 carriers, whereas an increase (b= 0.05, CI= [0.04, 0.07]) among non-carriers, implying a significant moderation effect of APOE4 (b= -0.18, CI= [-0.20,-0.15]). DISCUSSION: The results suggest that lipid metabolism functions differently in APOE4 carriers compared to non-carriers, which may inform the development of targeted interventions for APOE4 carriers to mitigate cognitive decline.

An in-depth association analysis of genetic variants within nicotine-related loci: Meeting in middle of GWAS and genetic fine-mapping.

In the last two decades of Genome-wide association studies (GWAS), nicotine-dependence-related genetic loci (e.g., nicotinic acetylcholine receptor - nAChR subunit genes) are among the most replicable genetic findings. Although GWAS results have reported tens of thousands of SNPs within these loci, further analysis (e.g., fine-mapping) is required to identify the causal variants. However, it is computationally challenging for existing fine-mapping methods to reliably identify causal variants from thousands of candidate SNPs based on the posterior inclusion probability. To address this challenge, we propose a new method to select SNPs by jointly modeling the SNP-wise inference results and the underlying structured network patterns of the linkage disequilibrium (LD) matrix. We use adaptive dense subgraph extraction method to recognize the latent network patterns of the LD matrix and then apply group LASSO to select causal variant candidates. We applied this new method to the UK biobank data to identify the causal variant candidates for nicotine addiction. Eighty-one nicotine addiction-related SNPs (i.e.,-log(p) > 50) of nAChR were selected, which are highly correlated (average r2>0.8) although they are physically distant (e.g., >200 kilobase away) and from various genes. These findings revealed that distant SNPs from different genes can show higher LD r2 than their neighboring SNPs, and jointly contribute to a complex trait like nicotine addiction.

The causal effect of HbA1c on white matter brain aging by two-sample Mendelian randomization analysis.

Background: Poor glycemic control with elevated levels of hemoglobin A1c (HbA1c) is associated with increased risk of cognitive impairment, with potentially varying effects between sexes. However, the causal impact of poor glycemic control on white matter brain aging in men and women is uncertain. Methods: We used two nonoverlapping data sets from UK Biobank cohort: gene-outcome group (with neuroimaging data, (N = 15,193; males/females: 7,101/8,092)) and gene-exposure group (without neuroimaging data, (N = 279,011; males/females: 122,638/156,373)). HbA1c was considered the exposure and adjusted "brain age gap" (BAG) was calculated on fractional anisotropy (FA) obtained from brain imaging as the outcome, thereby representing the difference between predicted and chronological age. The causal effects of HbA1c on adjusted BAG were studied using the generalized inverse variance weighted (gen-IVW) and other sensitivity analysis methods, including Mendelian randomization (MR)-weighted median, MR-pleiotropy residual sum and outlier, MR-using mixture models, and leave-one-out analysis. Results: We found that for every 6.75 mmol/mol increase in HbA1c, there was an increase of 0.49 (95% CI = 0.24, 0.74; p-value = 1.30 × 10-4) years in adjusted BAG. Subgroup analyses by sex and age revealed significant causal effects of HbA1c on adjusted BAG, specifically among men aged 60-73 (p-value = 2.37 × 10-8). Conclusion: Poor glycemic control has a significant causal effect on brain aging, and is most pronounced among older men aged 60-73 years, which provides insights between glycemic control and the susceptibility to age-related neurodegenerative diseases.

Span of regularization for solution of inverse problems with application to magnetic resonance relaxometry of the brain.

We present a new regularization method for the solution of the Fredholm integral equation (FIE) of the first kind, in which we incorporate solutions corresponding to a range of Tikhonov regularizers into the end result. This method identifies solutions within a much larger function space, spanned by this set of regularized solutions, than is available to conventional regularization methods. An additional key development is the use of dictionary functions derived from noise-corrupted inversion of the discretized FIE. In effect, we combine the stability of solutions with greater degrees of regularization with the resolution of those that are less regularized. The span of regularizations (SpanReg) method may be widely applicable throughout the field of inverse problems.

Input layer regularization for magnetic resonance relaxometry biexponential parameter estimation.

Many methods have been developed for estimating the parameters of biexponential decay signals, which arise throughout magnetic resonance relaxometry (MRR) and the physical sciences. This is an intrinsically ill-posed problem so that estimates can depend strongly on noise and underlying parameter values. Regularization has proven to be a remarkably efficient procedure for providing more reliable solutions to ill-posed problems, while, more recently, neural networks have been used for parameter estimation. We re-address the problem of parameter estimation in biexponential models by introducing a novel form of neural network regularization which we call input layer regularization (ILR). Here, inputs to the neural network are composed of a biexponential decay signal augmented by signals constructed from parameters obtained from a regularized nonlinear least-squares estimate of the two decay time constants. We find that ILR results in a reduction in the error of time constant estimates on the order of 15%-50% or more, depending on the metric used and signal-to-noise level, with greater improvement seen for the time constant of the more rapidly decaying component. ILR is compatible with existing regularization techniques and should be applicable to a wide range of parameter estimation problems.

Sound field reconstruction using block sparse Bayesian learning equivalent source method.

Nearfield acoustic holography based on the compressed sensing theory can realize the accurate reconstruction of sound fields with fewer measurement points on the premise that an appropriate sparse basis is obtained. However, for different types of sound sources, the appropriate sparse bases are diverse and should be constructed elaborately. In this paper, a block sparse Bayesian learning (SBL) equivalent source method is proposed for realizing the reconstruction of the sound fields radiated by different types of sources, including the spatially sparse sources, the spatially extended sources, and the mixed ones of the above two, without the elaborate construction of the sparse basis. The proposed method constructs a block sparse equivalent source model and promotes a block sparse solution by imposing a structured prior on the equivalent source model and estimating the posterior of the model by using the SBL, which can achieve the accurate reconstruction of the radiated sound fields of different types of sources simply by adjusting the block size. Numerical simulation and experimental results demonstrate the validity and superiority of the proposed method, and the effects of two key parameters, the block size, and sparsity pruning threshold value are investigated through simulations.

Stabilization of parameter estimates from multiexponential decay through extension into higher dimensions.

Analysis of multiexponential decay has remained a topic of active research for over 200 years. This attests to the widespread importance of this problem and to the profound difficulties in characterizing the underlying monoexponential decays. Here, we demonstrate the fundamental improvement in stability and conditioning of this classic problem through extension to a second dimension; we present statistical analysis, Monte-Carlo simulations, and experimental magnetic resonance relaxometry data to support this remarkable fact. Our results are readily generalizable to higher dimensions and provide a potential means of circumventing conventional limits on multiexponential parameter estimation.

A free field recovery technique based on the boundary element method and three-dimensional scanning measurements.

The boundary element method- (BEM-) based free field recovery technique (FFRT) has been proposed to recover the free field radiated by an arbitrarily shaped source from the mixed field that would be measured in a noisy environment. However, that technique requires that the boundary integral equation should be established on an enclosed hologram surface surrounding the source, which means that the hologram surface should be discretized into elements and the measurement points should be located on the nodes of the elements. For large-scale or mid-high frequency problems, it makes the total number of measurement points huge since it should obey the criterion of more than six elements per wavelength, which put forward very high requirements for holographic data measurement. To overcome this problem, a more flexible BEM-based FFRT without the restriction on the locations of measurement points is proposed in this study. In virtue of this, a three-dimensional scanning measurement method can be applied to acquire holographic data with high efficiency. The effectiveness of the proposed method is validated by two numerical simulations and an experiment.

Separation of nonstationary sound fields based on the time-domain equivalent source method with single layer pressure-velocity measurements.

This paper proposes a sound field separation technique based on the time-domain equivalent source method with single layer pressure-velocity measurements to extract the nonstationary sound field radiated by the target source in a reverberant environment. This technique constructs a formulation that relates the pressures and particle velocities on a measurement surface to the strengths of time-domain equivalent sources arranged for modelling the outgoing and incoming waves. By solving the strengths of time-domain equivalent sources, the sounds coming from different sides of the measurement surface can be separated independently. In the proposed technique, the use of a time-domain equivalent source model allows the measurement surface to be arbitrarily shaped, thus providing the ability to analyze the arbitrarily shaped sources in a reverberant environment. Numerical simulations investigated the performance of the proposed technique when using different types of arrays, including planar, semi-cylindrical, and semi-spherical arrays, and an experiment with three loudspeakers located at two sides of the measurement surface was carried out to test the validity of the proposed technique. Both numerical and experimental results demonstrate that the proposed technique can remove the influence of disturbing sources in both time and space domains and separate out the target sound fields effectively.

Reconstruction of time-dependent forces acting on a vibrating structure from pressure measurements.

This paper proposes an approach to reconstruct the time-dependent forces acting on a vibrating structure from pressure measurements. In the approach, the pressures measured in the near field of the structure are related to the exciting forces at the reconstruction points by the transfer functions determined in an experimental way, whereupon the time-dependent forces can be reconstructed with these pressures as inputs. In the reconstruction process, an additional regularization with a mixed lp , q-norm term is introduced to resolve the ill-posed inverse problem, which is able to take advantage of the prior knowledge of space and time characteristics of the forces. A numerical simulation of reconstructing the time-dependent forces acting on a plate and two experiments of reconstructing the impact forces acting on a semi-cylindrical shell and an elliptically shaped structure are carried out. The results demonstrate the feasibility of the proposed approach for reconstructing the forces in both temporal and spatial domains from pressure measurements. The proposed approach provides a non-contact and real-time way to identify the locations of forces and reconstruct their time histories, which can be further used to reveal the mechanical cause of the radiated noise.

Reconstruction of nonstationary sound fields based on a time domain angular spectrum method.

A time domain angular spectrum method is proposed to reconstruct nonstationary sound fields. In this method, the sound field is expressed as a superposition of a series of plane wave bases, and the plane wave basis is constructed by an impulse response function that relates the time domain angular spectrum to the field point pressure. The impulse response function consists of two parts, the propagating plane waves and the evanescent plane waves, and their physical interpretation is provided. By discretizing the time convolution between the plane wave strength and the impulse response function, the reconstruction can be carried out at each time step, thus providing the advantage of real-time reconstructing sound fields. Since the real-time reconstruction process is non-recursive, it can provide a stable reconstruction. In the reconstruction process, the Tikhonov regularization is introduced at each time step to obtain an appropriate estimation of the plane wave strength. Numerical simulations with an unsteady excitation plate and an experiment with an impacted plate were carried out to demonstrate the feasibility of the proposed method on reconstructing nonstationary sound fields. The effect of numerical parameters on the reconstruction accuracy was also investigated in the numerical simulations.

A Tutorial Introduction to Inverse Problems in Magnetic Resonance.

There has been a tremendous increase in applications of the inverse problem framework to parameter estimation in magnetic resonance. Attempting to capture both the basics of this formalism and modern developments would require an article of inordinate length. Therefore, in the following, we provide basic material as a practical introduction to the topic and an entree to the literature. First, we describe the formulation of linear and nonlinear inverse problems, with an emphasis on signal equations arising in magnetic resonance. We then describe the Fredholm equation of the first kind as a paradigm for these problems. This is followed by much more detailed considerations for determining solutions in the linear case, including central concepts such as condition number, regularization, and stability. Solution methods for nonlinear inverse problems are described next, followed by a treatment of their stability and regularization. Finally, we provide an introduction to compressed sensing, with signal reconstruction formulated as the solution to an inverse problem, making use of much of the previous material. Throughout, the emphasis is on outlines of the theory and on numerical examples, rather than on mathematical rigor and completeness.

Estimating the acoustical properties of locally reactive finite materials using the boundary element method.

The finite size of a sound-absorbing material may lead to inaccurate results when measuring the acoustical properties of the material using the free-field measurement methods. In this study, a method of estimating the acoustical properties of locally reactive finite materials is proposed by combining a sound field model established by the boundary element method with an iteration algorithm. The proposed method takes the finiteness of the material into account, meaning that the size effect is removed and accurate results can be obtained. Numerical simulations and experiments of two kinds of materials, including a rigid floor and a porous material, are carried out to verify the validity of the proposed method. Results demonstrate that the proposed method is effective in estimating the acoustical properties of these two kinds of materials. Besides, a detailed analysis of the influences of the sample size, the source location, and the receiving point position is done in the simulations.

A boundary element eigensolver for acoustic resonances in cavities with impedance boundary conditions.

This letter presents a boundary element scheme for analysis of acoustic resonances in cavities with impedance boundary conditions. The resultant eigenproblem, which is nonlinear and difficult to solve directly, is transformed to a linear one through a contour integral method. A variant-parameter scheme based on the Burton-Miller combined formulation is given to identify spurious eigenfrequencies, which are complex and similar to true eigenfrequencies. A numerical example is used to show the accuracy and effectiveness of the proposed method.

Mild encephalitis/encephalopathy with a reversible splenial lesion secondary to encephalitis complicated by hyponatremia: A case report and literature review.

RATIONALE: Mild encephalitis/encephalopathy with a reversible splenial lesion (MERS) is an infection-associated encephalitis/encephalopathy syndrome that is predominately caused by a virus. MERS has no direct association with central nervous system (CNS) infections or inflammation. Non-CNS infections may cause reversible lesion in the splenium of corpus callosum. Recently, there have been reports of many patients with hyponatremia related MERS. Interleukin-6 (IL-6) was also found elevated in serum and in cerebrospinal fluid (CSF) in patients with MERS. The role of IL-6 in the non-osmotic release of vasopressin is crucial. Persistent hyponatremia may be linked to this effect. The following is a case report of MERS secondary to encephalitis, complicated by hyponatremia. We will summarize the latest research and progress regarding MERS. PATIENT CONCERNS: A 31-year-old man was admitted to our department with a 5-day history of fever and headache. His initial diagnosis was encephalitis and hyponatremia; during this period the patient also developed MERS secondary to the encephalitis. DIAGNOSES: Encephalitis was diagnosed by reviewing the history of fever, headache, neck rigidity and Kerning sign (+) on clinical examination. Lab tests revealed: serum VCA IgG (+), EBNA-1 IgG (-), EBV IgM (-), and inflammation in the analysis of CSF. Cranial MRI+C showed that the blood vessels on the surface of the brain were obviously increasing and thickening and diffuse slow waves were detected on the electroencephalogram (EEG). The patient's hyponatremia aggravated on the third day of hospitalization. On the fourth day of hospitalization, the patient was somnolent, apathetic, and slow. Magnetic resonance imaging (MRI) of the brain, with a T2-weighted fluid attenuated inversion recovery image, showed high-signal intensity in the splenium of the corpus callosum (SCC) on the fifth day of hospitalization. Diffusion-weighted imaging (DWI) showed splenial hyperintensity as a "boomerang sign" and reduced diffusion on apparent diffusion coefficient (ADC) maps. Cranial MRI findings returned to normal after 1 month. The diagnosis of MERS was confirmed. INTERVENTIONS: We administered an intravenous drip infusion of acyclovir and prescribed oral sodium supplementation. OUTCOMES: The patient's neurological symptoms gradually improved. The MRI lesion in the SCC disappeared on the 30th day. LESSONS: In patients with encephalitis accompanied by hyponatremia, elevated IL-6 or urinary ß2-microglobulin (ß2MG), and exacerbations such as sudden somnolence, delirium, confusion, and seizures, the possibility of secondary MERS should be investigated, in addition to the progression of encephalitis.