Dual-role transcription factors stabilize intermediate expression levels.

Precise control of gene expression levels is essential for normal cell functions, yet how they are defined and tightly maintained, particularly at intermediate levels, remains elusive. Here, using a series of newly developed sequencing, imaging, and functional assays, we uncover a class of transcription factors with dual roles as activators and repressors, referred to as condensate-forming level-regulating dual-action transcription factors (TFs). They reduce high expression but increase low expression to achieve stable intermediate levels. Dual-action TFs directly exert activating and repressing functions via condensate-forming domains that compartmentalize core transcriptional unit selectively. Clinically relevant mutations in these domains, which are linked to a range of developmental disorders, impair condensate selectivity and dual-action TF activity. These results collectively address a fundamental question in expression regulation and demonstrate the potential of level-regulating dual-action TFs as powerful effectors for engineering controlled expression levels.

Reduction of Noise Levels During Caesarean Births Through Audiovisual Feedback is Associated With Lower Stress Levels for Patients.

OBJECTIVE: Noise reduction during surgical procedures leads to improved surgical performance and results. The caesarean birth (CB) is an exceptional operation and a life changing experience. Through the introduction of staff education and implementation of audiovisual feedback, we intended to reduce noise, and subsequently reduce surgical complications and increase the well-being of patients and staff. METHODS: During Phase I, blinded baseline measurements of noise were conducted. Phase II started after staff education and structured questionnaires on subjective noise and stress were added, and in Phase III audiovisual feedback was introduced. Mean and peak noise levels over the time of the procedure were obtained in A-weighted decibels (dB(A)). Kruskal-Wallis H tests were performed to evaluate the impact of interventions on noise levels. Questionnaires were evaluated using descriptive statistics; stress-scores were compared using independent sample t-tests. RESULTS: Ninety planned CBs were included. Median noise levels were 62.85 dB(A) at baseline. They decreased significantly to 60.60 dB(A) (Phase II) and 59.25 dB(A) (Phase III), respectively. This reduction of 3.6 dB(A) leads to a subjective noise reduction of around 20%. Significant differences for A-weighted and peak noise levels during actual surgery were found after combining staff education with audiovisual feedback. In Phase III, staff reported less stressful noise. Stress also decreased significantly in the patient group. Beeping machines and telephones were identified as the most stressful sources of noise. CONCLUSION: We show that noise reduction during CB is both necessary and possible. Diminished subjective perception of noise and stress are positive impacts of this intervention. Staff education and audiovisual feedback can help to provide a calm and lower stress environment for patients and staff during caesarean births.

Hearing Aid apps: are they safe, practical and beneficial for children and teens in challenging situations?

PURPOSE: An adult version of an app giving users the control over the level of the volume, microphone directionality and noise reduction was adapted for children. The main purpose of this study was to evaluate the effect of changes made to microphone directionality and noise reduction in the myPhonak Junior (the app) on Speech intelligibility in challenging listening environments in children and teens. METHODS: The randomized, non-blinded interventional study with a single group of subjects involved two study visits with a home trial in-between. In the final study session speech assessment in noise was conducted in three different, randomly assigned conditions: default mode (Autosense Sky OS), preffered (self-adjusted) and the extreme condition. Questionnaire based assessment was conducted to assess the subjective benefit of using the app in different daily situations. RESULTS: The best scores (speech results in noise) were achieved with the preferred setting and the default Autosense Sky OS setting was significantly better than the extreme setting. The self-reported benefit through the questionnaire indicates significantly better result when adjusting the hearing aids through the app. CONCLUSION: The app is an easy-to-use way of controlling the level of noise reduction and the beam forming for children 11 years and older. It has the potential to help customizing the hearing aids beyond the default setting and helping to improve speech understanding in noise.

Improving the Accuracy of Direction of Arrival Estimation with Multiple Signal Inputs Using Deep Learning.

In this paper, an innovative cyclic noise reduction method and an improved CAPON algorithm (also the called minimum variance distortionless response (MVDR) algorithm) are proposed to improve the accuracy and reliability of DOA (direction of arrival) estimation. By processing the eigenvalues obtained from the covariance matrix of the received signal, the signal-to-noise ratio (SNR) can be increased by up to 5 dB by the cyclic noise reduction method, which will improve the DOA estimation accuracy. The improved CAPON algorithm has a convolution neural network (CNN) structure, whose input is the processed covariance matrix of the received signal, and the CAPON spectral value is used as the training label to obtain the estimated spatial spectrum. It retains the advantages of the CAPON algorithm, which can achieve blind source estimation, and simulations show that the proposed algorithm exhibits a better performance than the traditional algorithm in conditions of various SNRs and snapshot numbers. The simulation results show that, based on a certain SNR, the root mean square error (RMSE) of the improved CAPON algorithm can be reduced from 0.86° to 0.8° compared to traditional algorithms, and the angle estimation error can be decreased by up to about 0.3°. With the help of the cyclic noise reduction method, the angle estimation error decreases from 0.04° to 0.02°.

CEEMDAN-LWT De-Noising Method for Pipe-Jacking Inertial Guidance System Based on Fiber Optic Gyroscope.

An inertial guidance system based on a fiber optic gyroscope (FOG) is an effective way to guide long-distance curved pipe jacking. However, environmental disturbances such as vibration, electromagnetism, and temperature will cause the FOG signal to generate significant random noise. The random noise will overwhelm the effective signal. Therefore, it is necessary to eliminate the random noise. This study proposes a hybrid de-noising method, namely complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-lifting wavelet transform (LWT). Firstly, the FOG signal is extracted using a sliding window and decomposed by CEEMDAN to obtain the intrinsic modal function (IMF) with N different scales and one residual. Subsequently, the effective IMF components are selected according to the correlation coefficient between the IMF components and the FOG signal. Due to the low resolution of the CEEMDAN method for high-frequency components, the selected high-frequency IMF components are decomposed with lifting wavelet transform to increase the resolution of the signal. The detailed signals of the LWT decomposition are de-noised using the soft threshold de-noising method, and then the signal is reconstructed. Finally, pipe-jacking dynamic and environmental interference experiments were conducted to verify the effectiveness of the CEEMDAN-LWT de-noising method. The de-noising effect of the proposed method was evaluated by SNR, RMSE, and Deviation and compared with the CEEMDAN and LWT de-noising methods. The results show that the CEEMDAN-LWT de-noising method has the best de-noising effect with good adaptivity and high accuracy. The navigation results of the pipe-jacking attitude before and after de-noising were compared and analyzed in the environmental interference experiment. The results show that the absolute error of the pipe-jacking pitch, roll, and heading angles is reduced by 39.86%, 59.45%, and 14.29% after de-noising. The maximum relative error of the pitch angle is improved from -0.74% to -0.44%, the roll angle is improved from 2.07% to 0.79%, and the heading angle is improved from -0.07% to -0.06%. Therefore, the CEEMDAN-LWT method can effectively suppress the random errors of the FOG signal caused by the environment and improve the measurement accuracy of the pipe-jacking attitude.

Study on the Equivalence Transformation between Blasting Vibration Velocity and Acceleration.

The evaluation of blasting vibrations primarily hinges on two physical quantities: velocity and acceleration. A significant challenge arises when attempting to reference the two types of vibration data in relation to one another, such as different types of seismometers, noise, etc., necessitating a method for their equivalent transformation. To address this, a transformation method is discussed in detail with a case study, and equations for the ratio (Ra) of the particle peak velocity (PPV) to the particle peak acceleration (PPA) are proposed. The findings are twofold: (1) The conventional data conversion processes often suffer from low accuracy due to the presence of trend terms and noise in the signal. To mitigate this, the built-in MATLAB function is used for trend term elimination, complemented by a combined approach that integrates CEEMDAN with WD/WDP for noise reduction. These significantly enhance the accuracy of the transformation. (2) This analysis reveals a positive power function correlation between Ra and the propagation distance of the blast vibrations, contrasted by a negative correlation with the maximum charge per delay. Intriguingly, the Ra values observed in pre-splitting blasting operations are consistently lower than those in bench blasting. The established Ra equations offer a rapid, direct method for assessing the transformation between the PPV and PPA, providing valuable insights for the optimization of blasting design.

Robust Intelligent Monitoring and Measurement System toward Downhole Dynamic Liquid Level.

Dynamic liquid level monitoring and measurement in oil wells is essential in ensuring the safe and efficient operation of oil extraction machinery and formulating rational extraction policies that enhance the productivity of oilfields. This paper presents an intelligent infrasound-based measurement method for oil wells' dynamic liquid levels; it is designed to address the challenges of conventional measurement methods, including high costs, low precision, low robustness and inadequate real-time performance. Firstly, a novel noise reduction algorithm is introduced to effectively mitigate both periodic and stochastic noise, thereby significantly improving the accuracy of dynamic liquid level detection. Additionally, leveraging the PyQT framework, a software platform for real-time dynamic liquid level monitoring is engineered, capable of generating liquid level profiles, computing the sound velocity and liquid depth and visualizing the monitoring data. To bolster the data storage and analytical capabilities, the system incorporates an around-the-clock unattended monitoring approach, utilizing Internet of Things (IoT) technology to facilitate the transmission of the collected dynamic liquid level data and computed results to the oilfield's central data repository via LoRa and 4G communication modules. Field trials on dynamic liquid level monitoring and measurement in oil wells demonstrate a measurement range of 600 m to 3000 m, with consistent and reliable results, fulfilling the requirements for oil well dynamic liquid level monitoring and measurement. This innovative system offers a new perspective and methodology for the computation and surveillance of dynamic liquid level depths.

An Improved OMP Algorithm for Enhancing the Anti-Interference Performance of Array Antennas.

The demand for precise positioning in noisy environments has propelled the development of research on array antenna radar systems. Although the orthogonal matching pursuit (OMP) algorithm demonstrates superior performance in signal reconstruction, its application efficacy in noisy settings faces challenges. Consequently, this paper introduces an innovative OMP algorithm, DTM_OMP_ICA (a dual-threshold mask OMP algorithm based on independent component analysis), which optimizes the OMP signal reconstruction framework by utilizing two different observation bases in conjunction with independent component analysis (ICA). By implementing a mean mask strategy, it effectively denoises signals received by array antennas in noisy environments. Simulation results reveal that compared to traditional OMP algorithms, the DTM_OMP_ICA algorithm shows significant advantages in noise suppression capability and algorithm stability. Under optimal conditions, this algorithm achieves a noise suppression rate of up to 96.8%, with its stability also reaching as high as 99%. Furthermore, DTM_OMP_ICA surpasses traditional denoising algorithms in practical denoising applications, proving its effectiveness in reconstructing array antenna signals in noisy settings. This presents an efficient method for accurately reconstructing array antenna signals against a noisy backdrop.

Vibration Signal Noise-Reduction Method of Slewing Bearings Based on the Hybrid Reinforcement Chameleon Swarm Algorithm, Variate Mode Decomposition, and Wavelet Threshold (HRCSA-VMD-WT) Integrated Model.

To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic Reverse Learning (CRL), the Whale Optimization Algorithm's (WOA) bubble-net hunting, and the greedy strategy with the Cauchy mutation to diversify the initial population, accelerate convergence, and prevent local optimum entrapment. Furthermore, by optimizing Variate Mode Decomposition (VMD) input parameters with HRCSA, Intrinsic Mode Function (IMF) components are extracted and categorized into noisy and pure signals using cosine similarity. Subsequently, the Wavelet Threshold (WT) denoising targets the noisy IMFs before reconstructing the vibration signal from purified IMFs, achieving significant noise reduction. Comparative experiments demonstrate HRCSA's superiority over Particle Swarm Optimization (PSO), WOA, and Gray Wolf Optimization (GWO) regarding convergence speed and precision. Notably, HRCSA-VMD-WT increases the Signal-to-Noise Ratio (SNR) by a minimum of 74.9% and reduces the Root Mean Square Error (RMSE) by at least 41.2% when compared to both CSA-VMD-WT and Empirical Mode Decomposition with Wavelet Transform (EMD-WT). This study improves fault detection accuracy and efficiency in vibration signals and offers a dependable and effective diagnostic solution for slewing bearing maintenance.

Improved Non-Negative Matrix Factorization-Based Noise Reduction of Leakage Acoustic Signals.

The detection of gas leaks using acoustic signals is often compromised by environmental noise, which significantly impacts the accuracy of subsequent leak identification. Current noise reduction algorithms based on non-negative matrix factorization (NMF) typically utilize the Euclidean distance as their objective function, which can exacerbate noise anomalies. Moreover, these algorithms predominantly rely on simple techniques like Wiener filtering to estimate the amplitude spectrum of pure signals. This approach, however, falls short in accurately estimating the amplitude spectrum of non-stationary signals. Consequently, this paper proposes an improved non-negative matrix factorization (INMF) noise reduction algorithm that enhances the traditional NMF by refining both the objective function and the amplitude spectrum estimation process for reconstructed signals. The improved algorithm replaces the conventional Euclidean distance with the Kullback-Leibler (KL) divergence and incorporates noise and sparse constraint terms into the objective function to mitigate the adverse effects of signal amplification. Unlike traditional methods such as Wiener filtering, the proposed algorithm employs an adaptive Minimum Mean-Square Error-Log Spectral Amplitude (MMSE-LSA) method to estimate the amplitude spectrum of non-stationary signals adaptively across varying signal-to-noise ratios. Comparative experiments demonstrate that the INMF algorithm significantly outperforms existing methods in denoising leakage acoustic signals.

Impact of Mask Type as Training Target for Speech Intelligibility and Quality in Cochlear-Implant Noise Reduction.

The selection of a target when training deep neural networks for speech enhancement is an important consideration. Different masks have been shown to exhibit different performance characteristics depending on the application and the conditions. This paper presents a comprehensive comparison of several different masks for noise reduction in cochlear implants. The study incorporated three well-known masks, namely the Ideal Binary Mask (IBM), Ideal Ratio Mask (IRM) and the Fast Fourier Transform Mask (FFTM), as well as two newly proposed masks, based on existing masks, called the Quantized Mask (QM) and the Phase-Sensitive plus Ideal Ratio Mask (PSM+). These five masks are used to train networks to estimate masks for the purpose of separating speech from noisy mixtures. A vocoder was used to simulate the behavior of a cochlear implant. Short-time Objective Intelligibility (STOI) and Perceptual Evaluation of Speech Quality (PESQ) scores indicate that the two new masks proposed in this study (QM and PSM+) perform best for normal speech intelligibility and quality in the presence of stationary and non-stationary noise over a range of signal-to-noise ratios (SNRs). The Normalized Covariance Measure (NCM) and similarity scores indicate that they also perform best for speech intelligibility/gauging the similarity of vocoded speech. The Quantized Mask performs better than the Ideal Binary Mask due to its better resolution as it approximates the Wiener Gain Function. The PSM+ performs better than the three existing benchmark masks (IBM, IRM, and FFTM) as it incorporates both magnitude and phase information.

Noise Prediction and Reduction of Single Electron Spin by Deep-Learning-Enhanced Feedforward Control.

Noise-induced control imperfection is an important problem in applications of diamond-based nanoscale sensing, where measurement-based strategies are generally utilized to correct low-frequency noises in realtime. However, the spin-state readout requires a long time due to the low photon-detection efficiency. This inevitably introduces a delay in the noise-reduction process and limits its performance. Here we introduce the deep learning approach to relax this restriction by predicting the trend of noise and compensating for the delay. We experimentally implement feedforward quantum control of the nitrogen-vacancy center in diamond to protect its spin coherence and improve the sensing performance against noise. The new approach effectively enhances the decoherence time of the electron spin, which enables exploration of more physics from its resonant spectroscopy. A theoretical model is provided to explain the improvement. This scheme could be applied in general sensing schemes and extended to other quantum systems.

GC-STCL: A Granger Causality-Based Spatial-Temporal Contrastive Learning Framework for EEG Emotion Recognition.

EEG signals capture information through multi-channel electrodes and hold promising prospects for human emotion recognition. However, the presence of high levels of noise and the diverse nature of EEG signals pose significant challenges, leading to potential overfitting issues that further complicate the extraction of meaningful information. To address this issue, we propose a Granger causal-based spatial-temporal contrastive learning framework, which significantly enhances the ability to capture EEG signal information by modeling rich spatial-temporal relationships. Specifically, in the spatial dimension, we employ a sampling strategy to select positive sample pairs from individuals watching the same video. Subsequently, a Granger causality test is utilized to enhance graph data and construct potential causality for each channel. Finally, a residual graph convolutional neural network is employed to extract features from EEG signals and compute spatial contrast loss. In the temporal dimension, we first apply a frequency domain noise reduction module for data enhancement on each time series. Then, we introduce the Granger-Former model to capture time domain representation and calculate the time contrast loss. We conduct extensive experiments on two publicly available sentiment recognition datasets (DEAP and SEED), achieving 1.65% improvement of the DEAP dataset and 1.55% improvement of the SEED dataset compared to state-of-the-art unsupervised models. Our method outperforms benchmark methods in terms of prediction accuracy as well as interpretability.

Fault Diagnosis Method for Wind Turbine Gearbox Based on Ensemble-Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy.

The diagnosis of faults in wind turbine gearboxes based on signal processing represents a significant area of research within the field of wind power generation. This paper presents an intelligent fault diagnosis method based on ensemble-refined composite multiscale fluctuation-based reverse dispersion entropy (ERCMFRDE) for a wind turbine gearbox vibration signal that is nonstationary and nonlinear and for noise problems. Firstly, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and stationary wavelet transform (SWT) are adopted for signal decomposition, noise reduction, and restructuring of gearbox signals. Secondly, we extend the single coarse-graining processing method of refined composite multiscale fluctuation-based reverse dispersion entropy (RCMFRDE) to the multiorder moment coarse-grained processing method, extracting mixed fault feature sets for denoised signals. Finally, the diagnostic results are obtained based on the least squares support vector machine (LSSVM). The dataset collected during the gearbox fault simulation on the experimental platform is employed as the research object, and the experiments are conducted using the method proposed in this paper. The experimental results demonstrate that the proposed method is an effective and reliable approach for accurately diagnosing gearbox faults, exhibiting high diagnostic accuracy and a robust performance.

[Improving adaptive noise reduction performance of body sound auscultation through linear preprocessing].

Adaptive filtering methods based on least-mean-square (LMS) error criterion have been commonly used in auscultation to reduce ambient noise. For non-Gaussian signals containing pulse components, such methods are prone to weights misalignment. Unlike the commonly used variable step-size methods, this paper introduced linear preprocessing to address this issue. The role of linear preprocessing in improving the denoising performance of the normalized least-mean-square (NLMS) adaptive filtering algorithm was analyzed. It was shown that, the steady-state mean square weight deviation of the NLMS adaptive filter was proportional to the variance of the body sounds and inversely proportional to the variance of the ambient noise signals in the secondary channel. Preprocessing with properly set parameters could suppress the spikes of body sounds, and decrease the variance and the power spectral density of the body sounds, without significantly reducing or even with increasing the variance and the power spectral density of the ambient noise signals in the secondary channel. As a result, the preprocessing could reduce weights misalignment, and correspondingly, significantly improve the performance of ambient-noise reduction. Finally, a case of heart-sound auscultation was given to demonstrate how to design the preprocessing and how the preprocessing improved the ambient-noise reduction performance. The results can guide the design of adaptive denoising algorithms for body sound auscultation.

Denoising task-related fMRI: Balancing noise reduction against signal loss.

Preprocessing fMRI data requires striking a fine balance between conserving signals of interest and removing noise. Typical steps of preprocessing include motion correction, slice timing correction, spatial smoothing, and high-pass filtering. However, these standard steps do not remove many sources of noise. Thus, noise-reduction techniques, for example, CompCor, FIX, and ICA-AROMA have been developed to further improve the ability to draw meaningful conclusions from the data. The ability of these techniques to minimize noise while conserving signals of interest has been tested almost exclusively in resting-state fMRI and, only rarely, in task-related fMRI. Application of noise-reduction techniques to task-related fMRI is particularly important given that such procedures have been shown to reduce false positive rates. Little remains known about the impact of these techniques on the retention of signal in tasks that may be associated with systemic physiological changes. In this paper, we compared two ICA-based, that is FIX and ICA-AROMA, two CompCor-based noise-reduction techniques, that is aCompCor, and tCompCor, and standard preprocessing using a large (n = 101) fMRI dataset including noxious heat and non-noxious auditory stimulation. Results show that preprocessing using FIX performs optimally for data obtained using noxious heat, conserving more signals than CompCor-based techniques and ICA-AROMA, while removing only slightly less noise. Similarly, for data obtained during non-noxious auditory stimulation, FIX noise-reduction technique before analysis with a covariate of interest outperforms the other techniques. These results indicate that FIX might be the most appropriate technique to achieve the balance between conserving signals of interest and removing noise during task-related fMRI.

Estimation of the generation interval using pairwise relative transmission probabilities.

The generation interval (the time between infection of primary and secondary cases) and its often used proxy, the serial interval (the time between symptom onset of primary and secondary cases) are critical parameters in understanding infectious disease dynamics. Because it is difficult to determine who infected whom, these important outbreak characteristics are not well understood for many diseases. We present a novel method for estimating transmission intervals using surveillance or outbreak investigation data that, unlike existing methods, does not require a contact tracing data or pathogen whole genome sequence data on all cases. We start with an expectation maximization algorithm and incorporate relative transmission probabilities with noise reduction. We use simulations to show that our method can accurately estimate the generation interval distribution for diseases with different reproductive numbers, generation intervals, and mutation rates. We then apply our method to routinely collected surveillance data from Massachusetts (2010-2016) to estimate the serial interval of tuberculosis in this setting.

Silent active device tracking for MR-guided interventional procedures.

PURPOSE: To evaluate a silent MR active catheter tracking sequence that allows conducting catheter interventions with low acoustic noise levels. METHODS: To reduce the acoustic noise associated with MR catheter tracking, we implemented a technique previously used in conventional MRI. The gradient waveforms are modified to reduce the sound pressure level (SPL) and avoid acoustic resonances of the MRI system. The efficacy of the noise reduction was assessed by software-predicted SPL and verified by measurements. Furthermore, the quality of the catheter tracking signal was assessed in a phantom experiment and during interventional cardiovascular MRI sessions targeted at isthmus-related flutter ablation. RESULTS: The maximum measured SPL in the scanner room was 104 dB(A) for real-time imaging, and 88 dB(A) and 69 dB(A) for conventional and silent tracking, respectively. The SPL measured at different positions in the MR suite using silent tracking were 65-69 dB(A), and thus within the range of a normal conversation. Equivalent signal quality and tracking accuracy were obtained using the silent variant of the catheter tracking sequence. CONCLUSION: Our results indicate that silent MR catheter tracking capabilities are identical to conventional catheter tracking. The achieved acoustic noise reduction comes at no penalty in terms of tracking quality or temporal resolution, improves comfort and safety, and can overcome the need for MR-compatible communication equipment and background noise suppression during the actual interventional procedure.

Acoustic noise reduction for spiral MRI by gradient derating.

PURPOSE: To show that the acoustic noise of spiral MRI can be reduced by derating the gradients with minimal penalty to image quality and scan time, and to illustrate an algorithm for optimal choice of derating parameters. THEORY AND METHODS: Acoustic noise level was measured and compared for various values of maximum gradient amplitude and slew rate for T1 -weighted spin-echo spiral scans while maintaining image contrast, FOV and resolution, and readout time. A full gradient trajectory and a derated gradient (undersampled) trajectory were chosen for a volunteer scan followed by parallel imaging-aided reconstruction to illustrate comparable image SNR. Two auto-derating methods, which prioritize slew rate and gradient amplitude, respectively, were derived using analytical results from the WHIRLED PEAS variant of spiral waveforms and compared in their acoustic noise level under test use cases. RESULTS: Derating the gradients made the scan quieter by 16.6 dB(A) on average than a full gradient trajectory and required an undersampling factor R = 2 in order to maintain scan time, with no appreciable penalty in image SNR. Prioritizing reduced slew rate resulted in maximal loudness reduction. CONCLUSION: Scanner gradients can often be derated to reduce the acoustic noise for spiral MRI with minimal penalty in scan time and image quality with the help of parallel imaging. An automatic slew-priority derating method that maximizes loudness reduction is given.

Assessment of a hearing protection device on infant sleep in the neonatal intensive care unit.

Premature infants often require prolonged hospitalisation in the neonatal intensive care unit (NICU) where they are exposed to adverse noise that may disrupt sleep and further compromise recovery and developmental outcomes. This single-session trial assessed the effects of a novel circumaural hearing protection device (DREAMIES®; NEATCAP Medical LLC) on sleep in 10 premature infants (mean 34.1 weeks GA) in a Level III NICU. Using polysomnography (PSG), the infant's sleep was compared between three interfeed periods throughout which DREAMIES® was ON or OFF. Each infant received the same condition order, OFF1-ON-OFF2. The PSG 30 s epochs were scored by a rater masked to the condition as Quiet Sleep, Active Sleep, Indeterminate Sleep, and Wake. There was a 14.1% increase in sleep from OFF1 to ON (p = 0.05) and an 18.4% decrease in sleep from ON to OFF2 (p = 0.02); an analogous inverse effect was observed for wake (χ2  = 5.03, p = 0.08). There was a main effect of DREAMIES on active sleep (χ2  = 7.4, p = 0.025) due to more active sleep for ON1 (46%) compared with OFF2 (32%; p = 0.074). No significant effect was observed for quiet sleep or indeterminate sleep. On average, the sound level was 51 dBA (range 36-113 dBA) and did not differ significantly among the three periods. The strongest relationship between the minute-by-minute maximum sound level and movement actigraphy was observed for the OFF1 condition (ρ0.301, p < 0.001). These findings suggest that DREAMIES® may augment sleep in premature infants by reducing acute episodes of adverse noise in the NICU.