Risk Assessment of Neonatal Exposure to Low Frequency Noise Based on Balance in Mice.
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ABSTRACT:
SUBMITTER: Ohgami N
PROVIDER: S-EPMC5319995 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We are routinely exposed to low frequency noise (LFN; below 0.5 kHz) at moderate levels of 60-70 dB sound pressure level (SPL) generated from various sources in occupational and daily environments. LFN has been reported to affect balance in humans. However, there is limited information about the influence of chronic exposure to LFN at moderate levels for balance. In this study, we investigated whether chronic exposure to LFN at a moderate level of 70 dB SPL affects the vestibule, which is one of the organs responsible for balance in mice. Wild-type ICR mice were exposed for 1 month to LFN (0.1 kHz) and high frequency noise (HFN; 16 kHz) at 70 dB SPL at a distance of approximately 10-20 cm. Behavior analyses including rotarod, beam-crossing and footprint analyses showed impairments of balance in LFN-exposed mice but not in non-exposed mice or HFN-exposed mice. Immunohistochemical analysis showed a decreased number of vestibular hair cells and increased levels of oxidative stress in LFN-exposed mice compared to those in non-exposed mice. Our results suggest that chronic exposure to LFN at moderate levels causes impaired balance involving morphological impairments of the vestibule with enhanced levels of oxidative stress. Thus, the results of this study indicate the importance of considering the risk of chronic exposure to LFN at a moderate level for imbalance.
| S-EPMC3387207 | biostudies-literature
Project description:BackgroundLow-frequency (20-200 Hz) noise (LFN) from wind turbines has received much public attention due to potential health concerns. This work tries to estimate the sound power level of wind turbines (L W,A (dB)) at 20-200 Hz, which are not provided by manufacturers but essential for estimating LFN exposure (L Aeq) of nearby residents.MethodsL W,A at 20-200 Hz at three wind farms, each with a different brand of wind turbine (Brands A, B and C, respectively) were estimated using propagation model ISO 9613-2 under different weather conditions (rain, wind speed and wind direction) and validated with LFN monitoring data. The feasibility of using validated L W,A as inputs for ISO 9613-2 to simulate residents' exposure (L Aeq) to LFN were assessed considering interferences from rain, wind speed and wind directions.ResultsThe average L W,A at 20-200 Hz ranged between 93.2 and 100.4 dB, 97.8 and 107.2 dB, and 96.5 and 110.4 dB for turbines of Brands A, B, and C, respectively, operating under wind speeds from 2 to 12 m/s. The L W,A at wind speed of 2-8 m/s increased on average by 1.4, 1.9 and 1.7 dB per 1 m/s increase for Brands A, B and C, respectively. The differences in modeled Leq obtained through the input of L W,A into the ISO 9613-2 model and the measured L Aeq for the three studied wind farms all fall within 1.5 dB.ConclusionThis study successfully determined and validated the L W,A of wind turbines of three brands, and subsequent residents' LFN exposure (with 1.5 dB difference) at three wind farms. Accurately obtaining LFN exposure will serve as the basis for assessing LFN exposure-health relationship. As wind power widely use worldwide, health impact should be assessed based on validated LFN exposure assessment.
| S-EPMC7721757 | biostudies-literature
Project description:BackgroundLow-frequency noise may cause changes in cognitive function. However, there is no established consensus on the effect of low-frequency noise on cognitive function. Therefore, this systematic review and meta-analysis aimed to explore the relationship between low-frequency noise exposure and cognitive function.MethodsWe conducted a systematic review and identified original studies written in English on low-frequency noise and cognition published before December 2022 using the PsycINFO, PubMed, Medline, and Web of Science databases. The risk of bias was evaluated according to established guidelines. A random-effects meta-analysis was performed where appropriate. To explore the association between low-frequency noise exposure and cognitive function, we reviewed eight relevant studies. These studies covered cognitive functions grouped into four domains: attention, executive function, memory, and higher-order cognitive functions. The data extraction process was followed by a random-effects meta-analysis for each domain, which allowed us to quantify the overall effect.ResultsOur analysis of the selected studies suggested that interventions involving low-frequency noise only had a negative impact on higher-order cognitive functions (Z = 2.42, p = 0.02), with a standardized mean difference of -0.37 (95% confidence interval: -0.67, -0.07). A moderate level of heterogeneity was observed among studies (p = 0.24, I2 = 29%, Tau2 = 0.03).ConclusionsOur study findings suggest that low-frequency noise can negatively impact higher-order cognitive functions, such as logical reasoning, mathematical calculation, and data processing. Therefore, it becomes important to consider the potential negative consequences of low-frequency noise in everyday situations, and proactive measures should be taken to address this issue and mitigate the associated potential adverse outcomes.
| S-EPMC10775542 | biostudies-literature
Project description:IntroductionNoise is one of the most common worldwide environmental pollutants, especially in occupational fields. As a stressor, it affects not only the ear but also the entire body. Its physiological and psychological impacts have been well established in many conditions such as cardiovascular diseases. However, there is a dearth of evidence regarding diabetes risk related to noises.AimTo evaluate the relationship between occupational exposure to noise and the risk of developing diabetes.MethodsThis is a cross-sectional analytical study enrolling two groups of 151 workers each. The first group (noise exposed group: EG) included the employees of a Tunisian power plant, who worked during the day shift and had a permanent position. The second group (unexposed to noise group: NEG) included workers assigned to two academic institutions, who were randomly selected in the Occupational Medicine Department of the Farhat Hached University Hospital in Sousse, during periodical fitness to work visits. Both populations (exposed and unexposed) were matched by age and gender. Data collection was based on a preestablished questionnaire, a physical examination, a biological assessment, and a sonometric study.ResultsThe mean equivalent continuous sound level was 89 dB for the EG and 44.6 dB for the NEG. Diabetes was diagnosed in 24 workers from EG (15.9%) and 14 workers from NEG (9.3%), with no statistically significant difference (p=0.08). After multiple binary logistic regression, including variables of interest, noise did not appear to be associated with diabetes.ConclusionOur results did not reveal a higher risk of developing diabetes in workers exposed to noise. Further studies assessing both level and duration of noise exposure are needed before any definitive conclusion.
| S-EPMC8004364 | biostudies-literature
Project description:The paper proposes a method of automatic detection of parameters of a distribution transformer (model, type, and power) from a distance, based on its low-frequency noise spectra. The spectra are registered by sensors and processed by a method based on evolutionary algorithms and machine learning. The method, as input data, uses the frequency spectra of sound pressure levels generated during operation by transformers in the real environment. The model also uses the background characteristic to take under consideration the changing working conditions of the transformers. The method searches for frequency intervals and its resolution using both a classic genetic algorithm and particle swarm optimization. The interval selection was verified using five state-of-the-art machine learning algorithms. The research was conducted on 16 different distribution transformers. As a result, a method was proposed that allows the detection of a specific transformer model, its type, and its power with an accuracy greater than 84%, 99%, and 87%, respectively. The proposed optimization process using the genetic algorithm increased the accuracy by up to 5%, at the same time reducing the input data set significantly (from 80% up to 98%). The machine learning algorithms were selected, which were proven efficient for this task.
| S-EPMC7435980 | biostudies-literature
Project description:This report deals with a 1/f noise in homogeneous classical semiconductor samples on the base of silicon. We perform detail calculations of resistance fluctuations of the silicon sample due to both a) the charge carrier number changes due to their capture-emission processes, and b) due to screening effect of those negative charged centers, and show that proportionality of noise level to square mobility appears as a presentation parameter, but not due to mobility fluctuations. The obtained calculation results explain well the observed experimental results of 1/f noise in Si, Ge, GaAs and exclude the mobility fluctuations as the nature of 1/f noise in these materials and their devices. It is also shown how from the experimental 1/f noise results to find the effective number of defects responsible for this noise in the measured frequency range.
| S-EPMC4682134 | biostudies-other
Project description:Vowel identification in noise using consonant-vowel-consonant (CVC) logatomes was used to investigate a possible interplay of speech information from different frequency regions. It was hypothesized that the periodicity conveyed by the temporal envelope of a high frequency stimulus can enhance the use of the information carried by auditory channels in the low-frequency region that share the same periodicity. It was further hypothesized that this acts as a strobe-like mechanism and would increase the signal-to-noise ratio for the voiced parts of the CVCs. In a first experiment, different high-frequency cues were provided to test this hypothesis, whereas a second experiment examined more closely the role of amplitude modulations and intact phase information within the high-frequency region (4-8 kHz). CVCs were either natural or vocoded speech (both limited to a low-pass cutoff-frequency of 2.5 kHz) and were presented in stationary 3-kHz low-pass filtered masking noise. The experimental results did not support the hypothesized use of periodicity information for aiding low-frequency perception.
| S-EPMC4701218 | biostudies-literature
Project description:Charge noise is critical in the performance of gate-controlled quantum dots (QDs). Such information is not yet available for QDs made out of the new material graphene, where both substrate and edge states are known to have important effects. Here we show the 1/f noise for a microscopic graphene QD is substantially larger than that for a macroscopic graphene field-effect transistor (FET), increasing linearly with temperature. To understand its origin, we suspended the graphene QD above the substrate. In contrast to large area graphene FETs, we find that a suspended graphene QD has an almost-identical noise level as an unsuspended one. Tracking noise levels around the Coulomb blockade peak as a function of gate voltage yields potential fluctuations of order 1??eV, almost one order larger than in GaAs/GaAlAs QDs. Edge states and surface impurities rather than substrate-induced disorders, appear to dominate the 1/f noise, thus affecting the coherency of graphene nano-devices.
| S-EPMC4311243 | biostudies-literature
Project description:Cetaceans rely critically on sound for navigation, foraging and communication and are therefore potentially affected by increasing noise levels from human activities at sea. Shipping is the main contributor of anthropogenic noise underwater, but studies of shipping noise effects have primarily considered baleen whales due to their good hearing at low frequencies, where ships produce most noise power. Conversely, the possible effects of vessel noise on small toothed whales have been largely ignored due to their poor low-frequency hearing. Prompted by recent findings of energy at medium- to high-frequencies in vessel noise, we conducted an exposure study where the behaviour of four porpoises (Phocoena phocoena) in a net-pen was logged while they were exposed to 133 vessel passages. Using a multivariate generalised linear mixed-effects model, we show that low levels of high frequency components in vessel noise elicit strong, stereotyped behavioural responses in porpoises. Such low levels will routinely be experienced by porpoises in the wild at ranges of more than 1000 meters from vessels, suggesting that vessel noise is a, so far, largely overlooked, but substantial source of disturbance in shallow water areas with high densities of both porpoises and vessels.
| S-EPMC4476045 | biostudies-literature
Project description:BackgroundHuman-generated noise pollution now permeates natural habitats worldwide, presenting evolutionarily novel acoustic conditions unprecedented to most landscapes. These acoustics not only harm humans, but threaten wildlife, and especially birds, via changes to species densities, foraging behavior, reproductive success, and predator-prey interactions. Explanations for negative effects of noise on birds include disruption of acoustic communication through energetic masking, potentially forcing species that rely upon acoustic communication to abandon otherwise suitable areas. However, this hypothesis has not been adequately tested because confounding stimuli often co-vary with noise and are difficult to separate from noise exposure.Methodology/principal findingsUsing a natural experiment that controls for confounding stimuli, we evaluate whether species vocal features or urban-tolerance classifications explain their responses to noise measured through habitat use. Two data sets representing nesting and abundance responses reveal that noise filters bird communities nonrandomly. Signal duration and urban tolerance failed to explain species-specific responses, but birds with low-frequency signals that are more susceptible to masking from noise avoided noisy areas and birds with higher frequency vocalizations remained. Signal frequency was also negatively correlated with body mass, suggesting that larger birds may be more sensitive to noise due to the link between body size and vocal frequency.Conclusions/significanceOur findings suggest that acoustic masking by noise may be a strong selective force shaping the ecology of birds worldwide. Larger birds with lower frequency signals may be excluded from noisy areas, whereas smaller species persist via transmission of higher frequency signals. We discuss our findings as they relate to interspecific relationships among body size, vocal amplitude and frequency and suggest that they are immediately relevant to the global problem of increases in noise by providing critical insight as to which species traits influence tolerance of these novel acoustics.
| S-EPMC3212537 | biostudies-literature