Recovery of spatially varying acoustical properties via automated partial differential equation identification.

Observable dynamics, such as waves propagating on a surface, are generally governed by partial differential equations (PDEs), which are determined by the physical properties of the propagation media. The spatial variations of these properties lead to spatially dependent PDEs. It is useful in many fields to recover the variations from the observations of dynamical behaviors on the material. A method is proposed to form a map of the physical properties' spatial variations for a material via data-driven spatially dependent PDE identification and applied to recover acoustical properties (viscosity, attenuation, and phase speeds) for propagating waves. The proposed data-driven PDE identification scheme is based on ℓ1-norm minimization. It does not require any PDE term that is assumed active from the prior knowledge and is the first approach that is capable of identifying spatially dependent PDEs from measurements of phenomena. In addition, the method is efficient as a result of its non-iterative nature and can be robust against noise if used with an integration transformation technique. It is demonstrated in multiple experimental settings, including real laser measurements of a vibrating aluminum plate. Codes and data are available online at https://tinyurl.com/4wza8vxs.

Pharmacological properties of durva swaras (Cynodon dactylon L. Pers.) in an ovariectomised rat model mimicking chronic menopausal syndrome.

Hormonal replacement therapy (HRT), as the first-line management of chronic menopausal syndrome (CMS) in women, has limited application due to adverse effects. We aimed to evaluate the therapeutic potential of a herbal alternative (HALT), durva swaras (DS) of Cynodon dactylon L. Pers., in a CMS rat model. Female Sprague-Dawley rats were subjected to Sham and ovariectomy (OVX) surgery. OVX rats received either 0.11 mg/kg oestrogen as a positive treatment control or 1 (DS1), 2 (DS2), and 4 (DS3) g/kg DS for 160 days. Vaginal smear tests indicated the menopausal status. Routine clinical examinations, weekly body weights (BW), serum calcium, proinflammatory cytokines, and reproductive hormones levels were monitored. Clinical chemistry, body composition, bone mineral density (BMD), uterotrophic response, bone morphometry, and histopathology of major organs were evaluated. BW of OVX rats increased by 18-25% compared to Sham. Total fat and fat percentage were significantly elevated in the oestrogen group compared to DS2, DS3, and OVX group. DS treatment groups showed the levels of TNF- α was slightly reduced, while IL-1ß and IL-6 levels were significantly reduced (P < 0.05) compared to the oestrogen treated group. DS treatment restored serum calcium levels, while BMD, bone quality, osteoblast/osteoclast ratio, and collagen levels improved in both DS and oestrogen treatment groups. The uterotrophic assay demonstrated non-oestrogenic activity of DS. Endometrial hyperplastic change was observed in oestrogen-treated rats. The preclinical non-oestrogenic activity of DS has therapeutic potential in CMS through anti-inflammatory and osteo-protective effects. Further clinical research into DS, as a viable HALT to HRT, is required.

Functional connectome fingerprinting using shallow feedforward neural networks.

Although individual subjects can be identified with high accuracy using correlation matrices computed from resting-state functional MRI (rsfMRI) data, the performance significantly degrades as the scan duration is decreased. Recurrent neural networks can achieve high accuracy with short-duration (72 s) data segments but are designed to use temporal features not present in the correlation matrices. Here we show that shallow feedforward neural networks that rely solely on the information in rsfMRI correlation matrices can achieve state-of-the-art identification accuracies ([Formula: see text]) with data segments as short as 20 s and across a range of input data size combinations when the total number of data points (number of regions × number of time points) is on the order of [Formula: see text].

Proportionate Adaptive Filtering Algorithms Derived Using an Iterative Reweighting Framework.

In this paper, based on sparsity-promoting regularization techniques from the sparse signal recovery (SSR) area, least mean square (LMS)-type sparse adaptive filtering algorithms are derived. The approach mimics the iterative reweighted ℓ 2 and ℓ 1 SSR methods that majorize the regularized objective function during the optimization process. We show that introducing the majorizers leads to the same algorithm as simply using the gradient update of the regularized objective function, as is done in existing approaches. Different from the past works, the reweighting formulation naturally leads to an affine scaling transformation (AST) strategy, which effectively introduces a diagonal weighting on the gradient, giving rise to new algorithms that demonstrate improved convergence properties. Interestingly, setting the regularization coefficient to zero in the proposed AST-based framework leads to the Sparsity-promoting LMS (SLMS) and Sparsity-promoting Normalized LMS (SNLMS) algorithms, which exploit but do not strictly enforce the sparsity of the system response if it already exists. The SLMS and SNLMS realize proportionate adaptation for convergence speedup should sparsity be present in the underlying system response. In this manner, we develop a new way for rigorously deriving a large class of proportionate algorithms, and also explain why they are useful in applications where the underlying systems admit certain sparsity, e.g., in acoustic echo and feedback cancellation.

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees.

Models recently used in the literature proving residual networks (ResNets) are better than linear predictors are actually different from standard ResNets that have been widely used in computer vision. In addition to the assumptions such as scalar-valued output or single residual block, the models fundamentally considered in the literature have no nonlinearities at the final residual representation that feeds into the final affine layer. To codify such a difference in nonlinearities and reveal a linear estimation property, we define ResNEsts, i.e., Residual Nonlinear Estimators, by simply dropping nonlinearities at the last residual representation from standard ResNets. We show that wide ResNEsts with bottleneck blocks can always guarantee a very desirable training property that standard ResNets aim to achieve, i.e., adding more blocks does not decrease performance given the same set of basis elements. To prove that, we first recognize ResNEsts are basis function models that are limited by a coupling problem in basis learning and linear prediction. Then, to decouple prediction weights from basis learning, we construct a special architecture termed augmented ResNEst (A-ResNEst) that always guarantees no worse performance with the addition of a block. As a result, such an A-ResNEst establishes empirical risk lower bounds for a ResNEst using corresponding bases. Our results demonstrate ResNEsts indeed have a problem of diminishing feature reuse; however, it can be avoided by sufficiently expanding or widening the input space, leading to the above-mentioned desirable property. Inspired by the densely connected networks (DenseNets) that have been shown to outperform ResNets, we also propose a corresponding new model called Densely connected Nonlinear Estimator (DenseNEst). We show that any DenseNEst can be represented as a wide ResNEst with bottleneck blocks. Unlike ResNEsts, DenseNEsts exhibit the desirable property without any special architectural re-design.

SSGD: SPARSITY-PROMOTING STOCHASTIC GRADIENT DESCENT ALGORITHM FOR UNBIASED DNN PRUNING.

While deep neural networks (DNNs) have achieved state-of-the-art results in many fields, they are typically over-parameterized. Parameter redundancy, in turn, leads to inefficiency. Sparse signal recovery (SSR) techniques, on the other hand, find compact solutions to overcomplete linear problems. Therefore, a logical step is to draw the connection between SSR and DNNs. In this paper, we explore the application of iterative reweighting methods popular in SSR to learning efficient DNNs. By efficient, we mean sparse networks that require less computation and storage than the original, dense network. We propose a reweighting framework to learn sparse connections within a given architecture without biasing the optimization process, by utilizing the affine scaling transformation strategy. The resulting algorithm, referred to as Sparsity-promoting Stochastic Gradient Descent (SSGD), has simple gradient-based updates which can be easily implemented in existing deep learning libraries. We demonstrate the sparsification ability of SSGD on image classification tasks and show that it outperforms existing methods on the MNIST and CIFAR-10 datasets.

Neglected Bilateral Facet Dislocation of the Cervical Spine with Intact Neurology: Reduction Technique.

Bilateral cervical facet dislocation is a serious injury that in majority cause neurologic deficit requiring prompt medical attention. Rarely, they retain normal neurology due to spontaneous decompression even though patients can have objective myelopathic or root compression signs. Neglected cases with normal neurology are uncommon with only few of them reported in the literature but their management is still a matter of debate. Here, we report a case of a 26-year-old female who had neglected bilateral facet dislocation with neck stiffness that was operated with posteroanterior approach with near-complete reduction and intact neurology. We describe the technique employed and discuss the literature.

A Sparse Conjugate Gradient Adaptive Filter.

In this letter, we propose a novel conjugate gradient (CG) adaptive filtering algorithm for online estimation of system responses that admit sparsity. Specifically, the Sparsity-promoting Conjugate Gradient (SCG) algorithm is developed based on iterative reweighting methods popular in the sparse signal recovery area. We propose an affine scaling transformation strategy within the reweighting framework, leading to an algorithm that allows the usage of a zero sparsity regularization coefficient. This enables SCG to leverage the sparsity of the system response if it already exists, while not compromising the optimization process. Simulation results show that SCG demonstrates improved convergence and steady-state properties over existing methods.

Jointly Leveraging Decorrelation and Sparsity for Improved Feedback Cancellation in Hearing Aids.

We propose a new adaptive feedback cancellation (AFC) system in hearing aids (HAs) based on a well-posed optimization criterion that jointly considers both decorrelation of the signals and sparsity of the underlying channel. We show that the least squares criterion on subband errors regularized by a p-norm-like diversity measure can be used to simultaneously decorrelate the speech signals and exploit sparsity of the acoustic feedback path impulse response. Compared with traditional subband adaptive filters that are not appropriate for incorporating sparsity due to shorter sub-filters, our proposed framework is suitable for promoting sparse characteristics, as the update rule utilizing subband information actually operates in the fullband. Simulation results show that the normalized misalignment, added stable gain, and other objective metrics of the AFC are significantly improved by choosing a proper sparsity promoting factor and a suitable number of subbands. More importantly, the results indicate that the benefits of subband decomposition and sparsity promoting are complementary and additive for AFC in HAs.

Single dose Intravenous Tranexamic acid may not be adequate to reduce blood loss and blood transfusion requirement in patients undergoing single stage bilateral total knee arthroplasty.

Simultaneous bilateral total knee arthroplasty (TKA) causes increased blood loss and increases the risk of venous thromboembolism. Tranexamic acid (TXA) is commonly used to minimize blood loss and transfusion requirements. However, the optimal regimen of TXA in single stage bilateral TKA is still not defined. In this retrospective study, 35 patients who received TXA and 31 patients who did not receive TXA were evaluated for blood loss and transfusion requirement. Both the groups were comparable in terms of age, sex, body mass index and preoperative haemoglobin (Hb) and haematocrit (Hct). There was no significant difference in the change in Hb levels (2.42 ± 1.28 vs 2.44 ± 1.31 ; p=0.95) and Hct (1.37 ± 0.96 vs 1.62 ± 0.98, p=0.22) between the groups. There were no significant differences between the study and control groups in the intraoperative blood loss (163.71 vs 165.32 ml, p=0.92), drain output (621.71 vs 695.65 ml, p=0.65) and total blood loss (785.0 vs 860.97, p=0.40). There was no significant difference in allogeneic blood transfusion between the groups (62.85% received blood in the study group vs 58.06% in the control group, p>0.05). Single intraoperative dose of TXA may not be adequate to reduce blood loss and blood transfusion requirement in bilateral TKA.

Role of Amputation in Improving Mobility, Pain Outcomes, and Emotional and Psychological Well-Being in Children With Metastatic Osteosarcoma.

BACKGROUND:: Few studies have analyzed the benefit of limb amputations in children with metastatic osteosarcoma and limited life span. OBJECTIVE:: We studied outcomes of limb amputations in children with metastatic osteosarcoma. DESIGN:: We performed a retrospective review of patients who underwent limb amputations (January 1995-June 2015) and died within 1 year of surgery. SETTING/PARTICIPANTS:: We studied 12 patients with osteosarcoma at a single institution. MEASUREMENTS:: Data on mobility, pain, and emotional and psychological well-being were retrieved from medical records from 1 month before surgery to 6 months after surgery. RESULTS:: Of the 12 patients (7 females and 5 males; median age at surgery 13 years [range, 7-20 years]) meeting study criteria, 3 patients and 9 patients had primary osteosarcoma in upper and lower limbs, respectively. Mobility improved postamputation in 8 bedridden/wheelchair-bound patients. Postamputation, emotional, and psychological well-being improved for 9 patients, 3 patients had persistent psychological and/or emotional symptoms, and no patient experienced signs of regret. Daily mean pain scores were significantly lower at 1 week (median 3 [range, 0-6]; P = .03) and 3 months (median 0 [range, 0-8]; P = .02) postsurgery than at 1 week presurgery (median 5.5 [range, 0-10]). Morphine consumption (mg/kg/d) showed a trend toward higher values at 1 week (median 0.2 [range, 0-7.6]; P = .6) and 3 months (median 0.2 [range, 0-0.5]; P = .3) postsurgery than at 1 week presurgery (median 0.1 [range, 0-0.5]). CONCLUSIONS:: Patients undergoing limb amputations had reduced pain and improved mobility and emotional and psychological well-being. Amputations are likely to benefit children with limited life expectancy.

Nuisance effects and the limitations of nuisance regression in dynamic functional connectivity fMRI.

In resting-state fMRI, dynamic functional connectivity (DFC) measures are used to characterize temporal changes in the brain's intrinsic functional connectivity. A widely used approach for DFC estimation is the computation of the sliding window correlation between blood oxygenation level dependent (BOLD) signals from different brain regions. Although the source of temporal fluctuations in DFC estimates remains largely unknown, there is growing evidence that they may reflect dynamic shifts between functional brain networks. At the same time, recent findings suggest that DFC estimates might be prone to the influence of nuisance factors such as the physiological modulation of the BOLD signal. Therefore, nuisance regression is used in many DFC studies to regress out the effects of nuisance terms prior to the computation of DFC estimates. In this work we examined the relationship between seed-specific sliding window correlation-based DFC estimates and nuisance factors. We found that DFC estimates were significantly correlated with temporal fluctuations in the magnitude (norm) of various nuisance regressors. Strong correlations between the DFC estimates and nuisance regressor norms were found even when the underlying correlations between the nuisance and fMRI time courses were relatively small. We then show that nuisance regression does not necessarily eliminate the relationship between DFC estimates and nuisance norms, with significant correlations observed between the DFC estimates and nuisance norms even after nuisance regression. We present theoretical bounds on the difference between DFC estimates obtained before and after nuisance regression and relate these bounds to limitations in the efficacy of nuisance regression with regards to DFC estimates.

A Generalized Proportionate-Type Normalized Subband Adaptive Filter.

We show that a new design criterion, i.e., the least squares on subband errors regularized by a weighted norm, can be used to generalize the proportionate-type normalized subband adaptive filtering (PtNSAF) framework. The new criterion directly penalizes subband errors and includes a sparsity penalty term which is minimized using the damped regularized Newton's method. The impact of the proposed generalized PtNSAF (GPtNSAF) is studied for the system identification problem via computer simulations. Specifically, we study the effects of using different numbers of subbands and various sparsity penalty terms for quasi-sparse, sparse, and dispersive systems. The results show that the benefit of increasing the number of subbands is larger than promoting sparsity of the estimated filter coefficients when the target system is quasi-sparse or dispersive. On the other hand, for sparse target systems, promoting sparsity becomes more important. More importantly, the two aspects provide complementary and additive benefits to the GPtNSAF for speeding up convergence.

A Wearable, Extensible, Open-Source Platform for Hearing Healthcare Research.

Hearing loss is one of the most common conditions affecting older adults worldwide. Frequent complaints from the users of modern hearing aids include poor speech intelligibility in noisy environments and high cost, among other issues. However, the signal processing and audiological research needed to address these problems has long been hampered by proprietary development systems, underpowered embedded processors, and the difficulty of performing tests in real-world acoustical environments. To facilitate existing research in hearing healthcare and enable new investigations beyond what is currently possible, we have developed a modern, open-source hearing research platform, Open Speech Platform (OSP). This paper presents the system design of the complete OSP wearable platform, from hardware through firmware and software to user applications. The platform provides a complete suite of basic and advanced hearing aid features which can be adapted by researchers. It serves web apps directly from a hotspot on the wearable hardware, enabling users and researchers to control the system in real time. In addition, it can simultaneously acquire high-quality electroencephalography (EEG) or other electrophysiological signals closely synchronized to the audio. All of these features are provided in a wearable form factor with enough battery life for hours of operation in the field.

On Mitigating Acoustic Feedback in Hearing Aids with Frequency Warping by All-Pass Networks.

Acoustic feedback control continues to be a challenging problem due to the emerging form factors in advanced hearing aids (HAs) and hearables. In this paper, we present a novel use of well-known all-pass filters in a network to perform frequency warping that we call "freping." Freping helps in breaking the Nyquist stability criterion and improves adaptive feedback cancellation (AFC). Based on informal subjective assessments, distortions due to freping are fairly benign. While common objective metrics like the perceptual evaluation of speech quality (PESQ) and the hearing-aid speech quality index (HASQI) may not adequately capture distortions due to freping and acoustic feedback artifacts from a perceptual perspective, they are still instructive in assessing the proposed method. We demonstrate quality improvements with freping for a basic AFC (PESQ: 2.56 to 3.52 and HASQI: 0.65 to 0.78) at a gain setting of 20; and an advanced AFC (PESQ: 2.75 to 3.17 and HASQI: 0.66 to 0.73) for a gain of 30. From our investigations, freping provides larger improvement for basic AFC, but still improves overall system performance for many AFC approaches.

Proportionate Adaptive Filters Based on Minimizing Diversity Measures for Promoting Sparsity.

In this paper, a novel way of deriving proportionate adaptive filters is proposed based on diversity measure minimization using the iterative reweighting techniques well-known in the sparse signal recovery (SSR) area. The resulting least mean square (LMS)-type and normalized LMS (NLMS)-type sparse adaptive filtering algorithms can incorporate various diversity measures that have proved effective in SSR. Furthermore, by setting the regularization coefficient of the diversity measure term to zero in the resulting algorithms, Sparsity promoting LMS (SLMS) and Sparsity promoting NLMS (SNLMS) are introduced, which exploit but do not strictly enforce the sparsity of the system response if it already exists. Moreover, unlike most existing proportionate algorithms that design the step-size control factors based on heuristics, our SSR-based framework leads to designing the factors in a more systematic way. Simulation results are presented to demonstrate the convergence behavior of the derived algorithms for systems with different sparsity levels.

The role of routine imaging in pediatric cutaneous melanoma.

BACKGROUND: Optimal imaging for children with pediatric malignant melanoma (MM) is unknown. METHODS: We reviewed clinical and imaging findings of patients with American Joint Commission on Cancer (AJCC) stage IIC-IV MM treated on our institutional MEL06 trial. All patients had baseline brain magnetic resonance imaging/computed tomography (MRI/CT), positron emission tomography/computed tomography (PET/CT), CT chest, abdomen, and pelvis (CTCAP). Patients on stratum A (PEG-interferon, where PEG is pegylated; AJCC IIC, IIIA, IIIB; n = 16) had imaging every 6 months; stratum B1 (PEG-interferon and temozolomide; unresectable measurable disease, metastatic, or recurrent; n = 2) had PET/CT scans every 2 months and brain imaging studies every 4 months; stratum B2 patients (PEG-interferon and temozolomide; unresectable nonmeasurable, metastatic, or recurrent, n = 3) had imaging every 4 months. Off-therapy imaging was done every 6 months for 3 years. RESULTS: There were 21 patients (11 females, 11 spitzoid, median age 14 years, head/neck [6], trunk [7], extremities [8]). Patients with spitzoid melanoma underwent 236 imaging studies in total (86 PET/CT, 81 CTCAP, 11 CT chest, 10 CT brain, 48 MRI brain) at a median cost per patient of $32,718. Thirteen studies (5.8%) had findings that led to two biopsies (one positive). For conventional MM, 162 studies (61 PET/CT, 57 CTCAP, 8 CT chest, 7 CT brain, and 29 MRI brain) were performed with a median cost per patient of $23,420. Twenty (14%) had findings leading to six biopsies (four positive). At 6.3 years (range 0.4-9.2), 17 patients remain disease-free. CONCLUSION: Children with spitzoid melanoma require minimal imaging at diagnosis and follow-up. Patients with conventional MM should be imaged according to adult guidelines.

Empirical Bayes based relative impulse response estimation.

Relative impulse responses (ReIRs) have several applications in speech enhancement, noise suppression and source localization for multi-channel speech processing in reverberant environments. Estimating the ReIRs can be reduced to a system identification problem. A system identification method using an empirical Bayes framework is proposed and its application for spatial source subtraction in audio signal processing is evaluated. The proposed estimator allows for incorporating prior structure information of the system into the estimation procedure, leading to an improved performance especially in the presence of noise. The estimator utilizes the sparse Bayesian learning algorithm with appropriate priors to characterize both the early reflections and reverberant tails. The mean squared error of the proposed estimator is studied and an extensive experimental study with real-world recordings is conducted to show the efficacy of the proposed approach over other competing approaches.

Impact of ovarian transposition before pelvic irradiation on ovarian function among long-term survivors of childhood Hodgkin lymphoma: A report from the St. Jude Lifetime Cohort Study.

BACKGROUND: We reviewed the effect of ovarian transposition (OT) on ovarian function among long-term survivors of childhood Hodgkin lymphoma (HL) treated with pelvic radiotherapy. PROCEDURE: Female participants (age 18+ years) with HL in the St. Jude Lifetime Cohort Study (SJLIFE) were clinically evaluated for premature ovarian insufficiency (POI) 10 or more years after pelvic radiotherapy. Reproductive history including age at menopause and pregnancy/live births was available on all patients. RESULTS: Of 127 eligible females with HL, 90 (80%) participated in SJLIFE, including 49 who underwent OT before pelvic radiotherapy. Median age at STLIFE evaluation was 38 years (range 25-60). In a multiple regression adjusted for age at diagnosis, pelvic radiotherapy doses > 1,500 cGy (hazard ratio [HR] = 25.2, 95% confidence interval [CI] = 3.1-207.3; P = 0.0027) and cumulative cyclophosphamide equivalent doses of alkylating agents > 12,000 mg/m2 (HR = 11.2, 95% CI = 3.4-36.8; P < 0.0001) were significantly associated with POI. There was no significant association between OT and occurrence of POI (HR = 0.6, 95% CI = 0.2-1.9; P = 0.41). CONCLUSIONS: OT did not appear to modify risk of POI in this historic cohort of long-term survivors of HL treated with gonadotoxic therapy. Modern fertility preservation modalities, such as mature oocyte cryopreservation, should be offered to at-risk patients whenever feasible.