No abstract available

Crackling noise is a scale-invariant phenomenon found in various driven nonlinear dynamical material systems as a response to external stimuli such as force or external fields. Jerky material movements in the form of avalanches can span many orders of magnitude in size and follow universal scaling rules described by power laws. The concept was originally studied as Barkhausen noise in magnetic materials and now is used in diverse fields from earthquake research and building materials monitoring to fundamental research involving phase transitions and neural networks. Here, we demonstrate a method for nanoscale crackling noise measurements based on AFM nanoindentation, where the AFM probe can be used to study the crackling of individual nanoscale features, a technique we call crackling noise microscopy. The method is successfully applied to investigate the crackling of individual topological defects, i.e. ferroelectric domain walls. We show that critical exponents for avalanches are altered at these nanoscale features, leading to a suppression of mixed-criticality, which is otherwise present in domains. The presented concept opens the possibility of investigating the crackling of individual nanoscale features in a wide range of material systems.

To explore the association of lifestyles, caspase gene (CASP), and noise kurtosis with noise-induced hearing loss (NIHL).

Noise sensitivity moderates the association between environmental noise exposure and annoyance and health outcomes.

The impact of different transportation noise sources and noise environments on arterial stiffness remains unknown.

Animal communication systems evolved in the presence of noise generated by natural sources. Many species can increase the source levels of their sounds to maintain effective communication in elevated noise conditions, i.e. they have a Lombard response. Human activities generate additional noise in the environment creating further challenges for these animals. Male humpback whales are known to adjust the source levels of their songs in response to wind noise, which although variable is always present in the ocean. Our study investigated whether this Lombard response increases when singing males are exposed to additional noise generated by motor vessels. Humpback whale singers were recorded off eastern Australia using a fixed hydrophone array. The source levels of the songs produced while the singers were exposed to varying levels of wind noise and vessel noise were measured. Our results show that, even when vessel noise is dominant, singing males still adjust the source levels of their songs to compensate for the underlying wind noise, and do not further increase their source levels to compensate for the additional noise produced by the vessel. Understanding humpback whales' response to noise is important for developing mitigation policies for anthropogenic activities at sea.

The role of neuronal oscillations in the processing of speech has recently come to prominence. Since resting-state (RS) brain activity has been shown to predict both task-related brain activation and behavioural performance, we set out to establish whether inter-individual differences in spectrally-resolved RS-MEG power are associated with variations in words-in-noise recognition in a sample of 88 participants made available by the Human Connectome Project. Positive associations with resilience to noise were observed with power in the range 21 and 29 Hz in a number of areas along the left temporal gyrus and temporo-parietal association areas peaking in left posterior superior temporal gyrus (pSTG). Significant associations were also found in the right posterior superior temporal gyrus in the frequency range 30-40 Hz. We propose that individual differences in words-in-noise performance are related to baseline excitability levels of the neural substrates of phonological processing.

No abstract available

The present study used fMRI/BOLD neuroimaging to investigate how visual-verbal working memory is updated when exposed to three different background-noise conditions: speech noise, aircraft noise and silence. The number-updating task that was used can distinguish between "substitution processes," which involve adding new items to the working memory representation and suppressing old items, and "exclusion processes," which involve rejecting new items and maintaining an intact memory set. The current findings supported the findings of a previous study by showing that substitution activated the dorsolateral prefrontal cortex, the posterior medial frontal cortex and the parietal lobes, whereas exclusion activated the anterior medial frontal cortex. Moreover, the prefrontal cortex was activated more by substitution processes when exposed to background speech than when exposed to aircraft noise. These results indicate that (a) the prefrontal cortex plays a special role when task-irrelevant materials should be denied access to working memory and (b) that, when compensating for different types of noise, either different cognitive mechanisms are involved or those cognitive mechanisms that are involved are involved to different degrees.

The number of operations of Unmanned Aerial Vehicles (UAV), commonly referred to as "drones", has strongly increased in the past and is likely to further grow in the future. Therefore, drones are becoming a growing new source of environmental noise pollution, and annoyance reactions to drone noise are likely to occur in an increasing share of the population. To date, research on drone noise emission characteristics, and in particular also on health impacts, seems scarce, but systematic overviews on these topics are missing. The objective of this study was to establish a systematic literature review on drone noise emissions and noise effects on humans. The paper presents the methodology of the systematic reviews performed separately for noise emission and noise effects, assembles current literature, gives an overview on the state of knowledge, and identifies research gaps. Current literature suggests that drone noise is substantially more annoying than road traffic or aircraft noise due to special acoustic characteristics such as pure tones and high-frequency broadband noise. A range of open questions remains to be tackled by future studies.

The effects of cell-to-cell variation (noise) in gene expression have proven difficult to quantify because of the mechanistic coupling of noise to mean expression. To independently quantify the effects of changes in mean expression and noise we determine the fitness landscapes in mean-noise expression space for 33 genes in yeast. For most genes, short-lived (noise) deviations away from the expression optimum are nearly as detrimental as sustained (mean) deviations. Fitness landscapes can be classified by a combination of each gene's sensitivity to protein shortage or surplus. We use this classification to explore evolutionary scenarios for gene expression and find that certain landscape topologies can break the mechanistic coupling of mean and noise, thus promoting independent optimization of both properties. These results demonstrate that noise is detrimental for many genes and reveal non-trivial consequences of mean-noise-fitness topologies for the evolution of gene expression systems.

Music can be experienced in various acoustic qualities. In this study, we investigated how the acoustic quality of the music can influence strong emotional experiences, such as musical chills, and the neural activity. The music's acoustic quality was controlled by adding noise to musical pieces. Participants listened to clear and noisy musical pieces and pressed a button when they experienced chills. We estimated neural activity in response to chills under both clear and noisy conditions using functional magnetic resonance imaging (fMRI). The behavioral data revealed that compared with the clear condition, the noisy condition dramatically decreased the number of chills and duration of chills. The fMRI results showed that under both noisy and clear conditions the supplementary motor area, insula, and superior temporal gyrus were similarly activated when participants experienced chills. The involvement of these brain regions may be crucial for music-induced emotional processes under the noisy as well as the clear condition. In addition, we found a decrease in the activation of the right superior temporal sulcus when experiencing chills under the noisy condition, which suggests that music-induced emotional processing is sensitive to acoustic quality.

The advent of new devices, technology, machine learning techniques, and the availability of free large speech corpora results in rapid and accurate speech recognition. In the last two decades, extensive research has been initiated by researchers and different organizations to experiment with new techniques and their applications in speech processing systems. There are several speech command based applications in the area of robotics, IoT, ubiquitous computing, and different human-computer interfaces. Various researchers have worked on enhancing the efficiency of speech command based systems and used the speech command dataset. However, none of them catered to noise in the same. Noise is one of the major challenges in any speech recognition system, as real-time noise is a very versatile and unavoidable factor that affects the performance of speech recognition systems, particularly those that have not learned the noise efficiently. We thoroughly analyse the latest trends in speech recognition and evaluate the speech command dataset on different machine learning based and deep learning based techniques. A novel technique is proposed for noise robustness by augmenting noise in training data. Our proposed technique is tested on clean and noisy data along with locally generated data and achieves much better results than existing state-of-the-art techniques, thus setting a new benchmark.

Predators frequently must detect and localize their prey in challenging environments. Noisy environments have been prevalent across the evolutionary history of predator-prey relationships, but now with increasing anthropogenic activities noise is becoming a more prominent feature of many landscapes. Here, we use the gleaning pallid bat, Antrozous pallidus, to investigate the mechanism by which noise disrupts hunting behaviour. Noise can primarily function to mask-obscure by spectrally overlapping a cue of interest, or distract-occupy an animal's attentional or other cognitive resources. Using band-limited white noise treatments that either overlapped the frequencies of a prey cue or did not overlap this cue, we find evidence that distraction is a primary driver of reduced hunting efficacy in an acoustically mediated predator. Under exposure to both noise types successful prey localization declined by half, search time nearly tripled, and bats used 25% more sonar pulses than when hunting in ambient conditions. Overall, the pallid bat does not seem capable of compensating for environmental noise. These findings have implications for mitigation strategies, specifically the importance of reducing sources of noise on the landscape rather than attempting to reduce the bandwidth of anthropogenic noise.

In this study, we investigated the effects of varying intensity levels of repeated moderate noise exposures on hearing. The aim was to define an appropriate intensity level that could be repeated several times without giving rise to a permanent hearing loss, and thus establish a model for early adulthood moderate noise exposure in rats. Female Sprague-Dawley rats were exposed to broadband noise for 90 min, with a 50 % duty cycle at levels of 101, 104, 107, or 110 dB sound pressure level (SPL), and compared to a control group of non-exposed animals. Exposure was repeated every 6 weeks for a maximum of six repetitions or until a permanent hearing loss was observed. Hearing was assessed by the auditory brainstem response (ABR). Rats exposed to the higher intensities of 107 and 110 dB SPL showed permanent threshold shifts following the first exposure, while rats exposed to 101 and 104 dB SPL could be exposed at least six times without a sustained change in hearing thresholds. ABR amplitudes decreased over time for all groups, including the non-exposed control group, while the latencies were unaffected. A possible change in noise susceptibility following the repeated moderate noise exposures was tested by subjecting the animals to high-intensity noise exposure of 110 dB for 4 h. Rats previously exposed repeatedly to 104 dB SPL were slightly more resistant to high-intensity noise exposure than non-exposed rats or rats exposed to 101 dB SPL. Repeated moderate exposure to 104 dB SPL broadband noise is a viable model for early adulthood noise exposure in rats and may be useful for the study of noise exposure on age-related hearing loss.

Nuclear spin noise spectroscopy in the absence of radio frequency pulses was studied under the influence of pulsed field gradients (PFGs) on pure and mixed liquids. Under conditions where the radiation-damping-induced line broadening is smaller than the gradient-dependent inhomogeneous broadening, echo responses can be observed in difference spectra between experiments employing pulsed field gradient pairs of the same and opposite signs. These observed spin noise gradient echoes (SNGEs) were analyzed through a simple model to describe the effects of transient phenomena. Experiments performed on high-resolution nuclear magnetic resonance (NMR) probes demonstrate how refocused spin noise behaves and how it can be exploited to determine sample properties. In bulk liquids and their mixtures, transverse relaxation times and translational diffusion constants can be determined from SNGE spectra recorded following tailored sequences of magnetic field gradient pulses.

Signal-to-noise ratio, the ratio between signal and noise, is a quantity that has been well established for MRI data but is still subject of ongoing debate and confusion when it comes to fMRI data. fMRI data are characterised by small activation fluctuations in a background of noise. Depending on how the signal of interest and the noise are identified, signal-to-noise ratio for fMRI data is reported by using many different definitions. Since each definition comes with a different scale, interpreting and comparing signal-to-noise ratio values for fMRI data can be a very challenging job. In this paper, we provide an overview of existing definitions. Further, the relationship with activation detection power is investigated. Reference tables and conversion formulae are provided to facilitate comparability between fMRI studies.

It is generally accepted that prokaryotes can tune gene expression noise independently of protein mean abundance by varying the relative levels of transcription and translation. Here, we address this question quantitatively, using a custom-made library of 40 Bacillus subtilis strains expressing a fluorescent protein under the control of different transcription and translation control elements. We quantify noise and mean protein abundance by fluorescence microscopy and show that for most of the natural transcription range of B. subtilis, expression noise is equally sensitive to variations in the transcription or translation rate because of the prevalence of extrinsic noise. In agreement, analysis of whole-genome transcriptomic and proteomic datasets suggests that noise optimization through transcription and translation tuning during evolution may only occur in a regime of weak transcription. Therefore, independent control of mean abundance and noise can rarely be achieved, which has strong implications for both genome evolution and biological engineering.

The study of spontaneous fluctuations of brain activity, often referred as brain noise, is getting increasing attention in functional magnetic resonance imaging (fMRI) studies. Despite important efforts, much of the statistical properties of such fluctuations remain largely unknown. This work scrutinizes these fluctuations looking at specific statistical properties which are relevant to clarify its dynamical origins. Here, three statistical features which clearly differentiate brain data from naive expectations for random processes are uncovered: First, the variance of the fMRI mean signal as a function of the number of averaged voxels remains constant across a wide range of observed clusters sizes. Second, the anomalous behavior of the variance is originated by bursts of synchronized activity across regions, regardless of their widely different sizes. Finally, the correlation length (i.e., the length at which the correlation strength between two regions vanishes) as well as mutual information diverges with the cluster's size considered, such that arbitrarily large clusters exhibit the same collective dynamics than smaller ones. These three properties are known to be exclusive of complex systems exhibiting critical dynamics, where the spatio-temporal dynamics show these peculiar type of fluctuations. Thus, these findings are fully consistent with previous reports of brain critical dynamics, and are relevant for the interpretation of the role of fluctuations and variability in brain function in health and disease.

There is a need for a clear cutoff to use noise sensitivity (NS) scale as a prediagnostic tool in research and clinical use.