Impact of signal-to-noise ratio and contrast definition on the sensitivity assessment and benchmarking of fluorescence molecular imaging systems.

Significance: Standardization of fluorescence molecular imaging (FMI) is critical for ensuring quality control in guiding surgical procedures. To accurately evaluate system performance, two metrics, the signal-to-noise ratio (SNR) and contrast, are widely employed. However, there is currently no consensus on how these metrics can be computed. Aim: We aim to examine the impact of SNR and contrast definitions on the performance assessment of FMI systems. Approach: We quantified the SNR and contrast of six near-infrared FMI systems by imaging a multi-parametric phantom. Based on approaches commonly used in the literature, we quantified seven SNRs and four contrast values considering different background regions and/or formulas. Then, we calculated benchmarking (BM) scores and respective rank values for each system. Results: We show that the performance assessment of an FMI system changes depending on the background locations and the applied quantification method. For a single system, the different metrics can vary up to ∼ 35 dB (SNR), ∼ 8.65 a . u . (contrast), and ∼ 0.67 a . u . (BM score). Conclusions: The definition of precise guidelines for FMI performance assessment is imperative to ensure successful clinical translation of the technology. Such guidelines can also enable quality control for the already clinically approved indocyanine green-based fluorescence image-guided surgery.

Defensive behaviors and c-fos expression in the midbrain.

Pavlovian fear conditioning serves as a valuable method for investigating species-specific defensive reactions (SSDRs) such as freezing and flight responses. The present study examines the role of white noise under different experimental conditions. Given that white noise has been shown to elicit both conditional (associative) and unconditional (nonassociative) defensive responses, we compared the response to noise following three separate training conditions: shock-only, white noise paired with shock, and context-only. Results showed that baseline freezing level significantly changed across groups: Both the shock-only group and the white noise paired with shock group froze more than the context-only group on the test day. White noise evoked differential freezing between groups on day 2: The shock-only group froze more than the context-only group although both groups were never exposed to white noise during training. Further, an activity burst triggered by white noise was similar for the shock-only and white noise paired with shock groups during testing, although shock-only group was never exposed to white noise stimuli during training. This aligned with c-fos data, indicating similar c-fos activity levels across different periaqueductal gray (PAG) regions for both shock-only and white noise paired with shock groups. However, the driving force behind c-fos activation-whether freezing, activity burst, or a combination of both-remains uncertain, warranting further analysis to explore specific correlations between SSDRs and c-fos activity within the PAG and related brain areas.

Functional hearing impairment common in Parkinson's disease: Insights from a pilot study.

INTRODUCTION: Hearing impairments in Parkinson's Disease (PD) have received limited attention in the past, possibly because PD patients often report no perceived hearing disability, yet negative consequences of hearing impairment might aggravate communication difficulties and social withdrawal. OBJECTIVE: Our aim was to investigate functional hearing (speech in noise recognition) in PD and evaluate its relationship to neuropsychiatric symptoms, cognition and quality of life. METHODS: Participants with PD were recruited in a tertiary movement disorder clinic. Demographic, audiological, neuropsychiatric and quality of life data were collected. Participants underwent pure tone audiometry (PTA) and Hearing in Noise test (HINT) as a part of their audiological evaluation. RESULTS: A total of 29 participants (mean age: 65.8±8.3 years, M:F= 1.6:1, mean disease duration 5.2 ± 4.0 years) completed the study. All assessments were done in the ON state. 19/29 (65.5 %) participants had normal tone audiometry for age; functional hearing loss, however, was present in 17/29 (58.6 %) according to the HINT. 65 % (11/17) of the affected participants had a disease duration of <4 years. The majority (72.4 %) with poor functional hearing did not perceive any hearing impairment. Hearing deficits did not correlate with non-motor symptoms (NMS), including cognition or other quality of life measures. CONCLUSIONS: Functional hearing loss is common in PD, often presents early in the disease and the majority of PD patients are unaware of their functional hearing loss. Its potential impact on cognition, communication and quality of life requires further investigation and tailored treatment.

Maritime traffic alters distribution of the harbour porpoise in the North Sea.

The North Sea is one of the most industrialised marine regions globally. We integrated cetacean-dedicated aerial surveys (2015-2022) with environmental covariates and ship positions from the Automatic Identification System (AIS) to investigate the disturbance radius and duration on harbour porpoise distribution. This study is based on 81,511 km of line-transect survey effort, during which 6511 harbour porpoise groups (8597 individuals) were sighted. Several proxies for ship disturbance were compared, identifying those best explaining the observed distribution. Better model performance was achieved by integrating maritime traffic, with frequent traffic representing the most significant disturbance to harbour porpoise distribution. Porpoises avoided areas frequented by numerous vessels up to distances of 9 km. The number of ships and average approach distance over time improved model performance, while reasons for the lower performance of predicted ship sound levels remain unclear. This study demonstrates the short-term effects of maritime traffic on harbour porpoise distribution.

The efficient method to get better raw brain signal on rat anesthetics experiment.

This paper introduces an efficient methodology for conducting rat anesthesia experiments, aimed at enhancing the quality of raw brain signals obtained. The proposed approach enables the acquisition of animal brain signals during experiments without the confounding influence of muscle noise. Initially, the use of alpha-chloralose (a-c) in conjunction with Isoflurane is introduced to induce anesthesia in rats. Subsequently, Dexdomitor is administered to prevent muscular movements during the collection of brain signals, further refining the signal quality. Experimental outcomes conclusively demonstrate that our anesthesia method produces cleaner raw signals and exhibits improved robustness during data acquisition, outperforming existing methods that rely solely on Isoflurane or the Ketamine-Xylazine combination. Notably, this improved performance is achieved with minimal alterations to vital physiological parameters, including body temperature, respiration, and heart rates. Moreover, the efficacy of a-c in maintaining anesthesia for up to 7 h stands in contrast to the shorter durations achievable with continuous Isoflurane administration or the 30-min window offered by Ketamine-Xylazine, highlighting the practical advantages of our proposed method. Finally, post-experiment observations confirmed that the animals gradually returned to normal behavior without any signs of distress or adverse effects, indicating that our method was both effective and safe.

Road traffic noise prediction model based on artificial neural networks.

This paper proposes a model based on machine learning for the prediction of road traffic noise for the city of Bogota-Colombia. The input variables of the model were: vehicle capacity, speed, type of flow and number of lanes. The input data were obtained through measurement campaigns in which audio and video recordings were made. The audio recordings, made with a measuring microphone calibrated at a height of 4 meters, made it possible to calculate the noise levels through software processing. On the other hand, by processing the video data, the capacity, and speed of the vehicle were obtained. This process was carried out by means of a classifier trained with images of vehicles taken in the field and free databases. In order to determine the machine learning algorithm to be used, five models were compared, which were configured with their respective hyperparameters obtained through mesh search. The results showed that the Multilayer Perceptron (MLP) regression had the best fit with an MAE of 0.86 dBA for the test data. Finally, the proposed MLP regressor was compared with some classical statistical models used for road traffic noise prediction. The main conclusion is that the MLP regressor obtained the best error and fit indicators with respect to traditional statistical models.

Inversion method of NHV based on novel model parameter space acquisition strategy and its application in the Tonghai basin site in Yunnan, China.

The imaging of subsurface soil velocity structures from ambient noise inversion is a difficult problem. Few recording points and a simplified 1-D layered profile lead to important non-uniqueness. From our point of view, improving the reliability of processing methods of the observed data to obtain noise horizontal-to-vertical spectral ratio (NHV) curves and setting a complete model parameter space are important tasks to reduce the non-uniqueness of inversion. In this study, using a local site near the border of the Tonghai Basin, China, as a case study, we first demonstrate how to identify and mitigate the influence of industrial sources using surface observations to obtain more reliable NHV curves. Then, a new strategy to determine model parameter space is proposed, that is, stratifying soil layers based on the number of NHV peaks and determining the shear wave velocities, thicknesses, and their ranges based on the empirical relationship between sedimentary thickness and resonant frequency (h-f r). Subsequently, combining the model parameter space acquisition strategy with the NHV inversion, a novel NHV inversion approach is developed and applied to obtain the 2-D V S profile of the investigated Tonghai site. The inverted 2-D V S profile aligns favorably with the frequency-depth conversion results of the measured NHV curves (NHV-profiling) and the measured borehole profiles, affirming the reliability of the proposed NHV inversion method. Finally, by comparing the empirical transfer functions from the strong-motion recordings, we validated the applicability of the inverted models for characterizing site effects. The model parameter space acquisition strategy proposed in this paper and the analysis procedure of the observed data are also applicable to other study areas, which can provide a referable approach to quickly and effectively acquire the soil layer velocity structure of the site.

Evaluating the Contribution of Model Complexity in Predicting Robustness in Synthetic Genetic Circuits.

The design-build-test-learn workflow is pivotal in synthetic biology as it seeks to broaden access to diverse levels of expertise and enhance circuit complexity through recent advancements in automation. The design of complex circuits depends on developing precise models and parameter values for predicting the circuit performance and noise resilience. However, obtaining characterized parameters under diverse experimental conditions is a significant challenge, often requiring substantial time, funding, and expertise. This work compares five computational models of three different genetic circuit implementations of the same logic function to evaluate their relative predictive capabilities. The primary focus is on determining whether simpler models can yield conclusions similar to those of more complex ones and whether certain models offer greater analytical benefits. These models explore the influence of noise, parametrization, and model complexity on predictions of synthetic circuit performance through simulation. The findings suggest that when developing a new circuit without characterized parts or an existing design, any model can effectively predict the optimal implementation by facilitating qualitative comparison of designs' failure probabilities (e.g., higher or lower). However, when characterized parts are available and accurate quantitative differences in failure probabilities are desired, employing a more precise model with characterized parts becomes necessary, albeit requiring additional effort.

Effect of Noise Filtering in an Ultraviolet Detector of UHPLC on the Statistical Reliability of Chemometrically Evaluated Repeatability.

A noise filter, which is usually attached to a detector for chromatography, was applied for the improvement of a signal-to-noise ratio (S/N) on a chromatogram. The objective of this paper is to elucidate the effect of noise filtering in an UV detector of ultra HPLC (UHPLC) on the statistical reliability of chemometrically evaluated repeatability by the function of mutual information (FUMI) theory. To examine the statistical reliability of chemometrically evaluated repeatability in the UHPLC system associated with noise filtering, the standard deviation (SD) values of the area in baseline fluctuations with peak region k (s(k)) were obtained from six chromatograms with noise filtering. Further, the average of s(k) values (σ̂) was calculated from the s(k) values (n = 6) to be alternatively applied as the population SD. All s(k)/σ̂ values were within the 95% confidence intervals (CIs) at the freedom degree of 50, indicating the chemometrically estimated relative SD (RSD) of a peak area and RSD by repeated measurements of at least 50 times had equivalent reliability.

Effects of seismic water guns on the peristomial membrane of sea urchins (Arbacia lixula, Linnaeus 1758).

The seismic water gun is widely used and plays an important role in seabed imaging acquisition; however, acoustic impacts on marine organisms are currently poorly understood. The aim of this study was to analyse the biochemical responses on the peristomial membrane (PM) of the sea urchin, Arbacia lixula, when exposed to water gun shots in open water. The PM (located around the mouth) is involved in vital functions, such as nutrition and protection. Individuals of sea urchins (n = 7 for each time slot) were sampled before, at the end, and at intervals of 3 h and 24 h after acoustic emission (duration of 20 min). Significant increases in superoxide dismutase, peroxidase, esterase and alkaline were observed immediately after water gun shots, highlighting an increase in the oxidative and inflammatory state of the tissue. Our results showed that acoustic impacts could interfere with PM vital functions, compromising the health, survival and ultimately the conservation of the species. Understanding these effects is crucial to predicting consequences on sea urchin populations and marine ecosystems.

Bead-by-bead normalization of single antigen assays: A necessary step for accurate detection of weak anti-HLA antibodies.

Ascertaining the presence of weakly positive anti-HLA donor-specific antibodies (DSA) in organ transplantation with multiplex single antigen beads assays may be challenging despite their high sensitivity due to technical variability issues. Through extensive datasets of Next-Generation Sequencing HLA typings and single antigen analyses, we reassessed the mean fluorescence intensity (MFI) positivity threshold of the assay to enhance accuracy. By showing that some beads were more prone to false positivity than others, we propose a nuanced approach that accounts for nonspecific intrinsic reactivities at the HLA antigen level, that is, on a bead-by-bead basis, as it enhances assay precision and reliability. This is substantiated by a comprehensive statistical analysis of MFI values and the implementation of the determination of a "Quantile Adjusted Threshold 500" (QAT500) value for each bead. Applied to DSA detection during patients' follow-up, this approach discriminated better and earlier low-strength DSA that would later raise their MFI above the clinically relevant threshold of 3000. Moving from a subjective interpretation to a more objective and precise methodology allows for standardizing HLA antibody and DSA detection. The study emphasizes the need for further research with real clinical data to validate and refine this approach.

[Research on dynamic blood oxygen saturation measurement based on motion noise reconstruction combined with convex combination least mean square adaptive filter].

The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07, respectively, with means absolute percentage error (MAPE) of 0.05% and 0.07%, respectively. The overall Pearson correlation coefficient reaches 0.971 2. The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.

An energy-based framework for predicting vehicle noise source intensity: From energy consumption to noise.

The vehicle noise source strength prediction model is a crucial component in the field of traffic noise prediction. Despite the establishment of noise source strength localized models in various countries, the theoretical underpinnings of the sound power level models within these frameworks remains unclear. This study addresses this gap by analyzing the correlation between vehicle noise and energy consumption. An energy-based source strength model framework (E-SSIM) is proposed, focusing on developing nonlinear models for basic noise level. E-SSIM is built on acoustical principles and the energy flow of vehicles, integrating noise and energy consumption through the application of multivariate regression theory, characterized by a transient or simplified mathematical framework. Furthermore, sensitivity analysis and road experiments are conducted to validate the proposed framework. The findings reveal that E-SSIM effectively integrates vehicle energy flow and principles of acoustics, thereby providing a theoretical foundation for the logarithmic mathematical structure in classical noise source strength models. The study reveals that in low-speed driving conditions (17-40 km/h), the sensitivity of noise energy to aerodynamic drag energy consumption reaches its peak. Specifically, the sensitivity of E-SSIM, as assessed by the A-weighted sound level, progressively decreases with increasing speed. On the contrary, for the Z-weighted sound level, the sensitivity initially decreases before rising again, reaching its peak stability and robustness at a speed of 23.8 km/h. E-SSIM exhibits superior precision in predicting A/Z-weighted sound pressure levels. Compared to classic logarithmic structural prediction models, the mean absolute percentage error of E-SSIM was reduced by 4.19% and 0.07%. Compared to typical models such as ASJ developed by the Acoustical Society of Japan and CNOSSOS-EU used by the European Commission, E-SSIM yielded a mean absolute percentage error reduction of 68% and 67%. Interestingly, as vehicle internal energy consumption increases, the prediction deviations of E-SSIM, ASJ, and CNOSSOS-EU gradually decrease, possibly because vehicle operating conditions approach stability. E-SSIM can utilize abundant vehicle data to develop generic models, promoting the advancement of noise prediction.

Adaptive and self-learning Bayesian filtering algorithm to statistically characterize and improve signal-to-noise ratio of heart-rate data in wearable devices.

The use of wearable sensors to monitor vital signs is increasingly important in assessing individual health. However, their accuracy often falls short of that of dedicated medical devices, limiting their usefulness in a clinical setting. This study introduces a new Bayesian filtering (BF) algorithm that is designed to learn the statistical characteristics of signal and noise, allowing for optimal smoothing. The algorithm is able to adapt to changes in the signal-to-noise ratio (SNR) over time, improving performance through windowed analysis and Bayesian criterion-based smoothing. By evaluating the algorithm on heart-rate (HR) data collected from Garmin Vivoactive 4 smartwatches worn by individuals with amyotrophic lateral sclerosis and multiple sclerosis, it is demonstrated that BF provides superior SNR tracking and smoothing compared with non-adaptive methods. The results show that BF accurately captures SNR variability, reducing the root mean square error from 2.84 bpm to 1.21 bpm and the mean absolute relative error from 3.46% to 1.36%. These findings highlight the potential of BF as a preprocessing tool to enhance signal quality from wearable sensors, particularly in HR data, thereby expanding their applications in clinical and research settings.

Wireless In-Ear Communication for Total Joint Arthroplasty: A Simulated Operating Room Evaluation.

Effective communication is vital for patient safety, yet failures are common, often due to outdated methods. This study aimed to assess whether in-ear communication devices improve communication in orthopedic surgery simulations compared to traditional loud voice methods. Fifteen participants underwent simulations using both in-ear wireless devices and standard communication. Results showed significant improvements with in-ear devices in correctly identifying phrases (78.6% vs 44%), effectiveness (7.9/10 vs 4.9/10), and clarity (8/10 vs 4/10), all P < .001. Participants also favored in-ear devices in usability assessments. Sound levels recorded were comparable between groups. In conclusion, in-ear communication is safe and effective in orthopedic settings, potentially enhancing efficiency and safety. These devices can mitigate loud noises, benefiting surgeon well-being and patient outcomes.

Prevalence and Factors Associated With Occupational Noise-Induced Hearing Loss Among Palm Oil Mill Workers in Selangor, Malaysia.

Introduction Occupational noise-induced hearing loss (NIHL) continues to be a significant public health issue globally, with Malaysia being no exception. In Malaysia, the majority of NIHL cases are reported from the manufacturing sector, with Selangor among the states with the highest number of confirmed cases. This study aimed to assess the prevalence of and factors associated with occupational NIHL among palm oil mill workers in Selangor, Malaysia. Methods A cross-sectional study was conducted to analyze the data from the data collection form, noise risk assessment reports, and audiometric test results done between 2021 and 2022 with a comparable baseline audiometric test. Results A total of 143 participants from three palm oil mills joined this study. The prevalence of NIHL was 42.7% (n = 61). Following the logistic regression model, NIHL was significantly associated with a duration of employment of 10 years and above, a history of occupational noise exposure at the previous workplace, and the use of personal hearing protectors at the current workplace with an adjusted OR of 2.41 (95% CI (1.14, 5.07)), 5.89 (95% CI (2.38, 14.53)), and 0.36 (95% CI (0.16, 0.83)), respectively. Conclusion The prevalence of NIHL among the study participants was high, and the associated factors are modifiable factors that can be prevented with a comprehensive hearing conservation program in the palm oil mills.

Correlated crustal and mantle melting documents proto-Tibetan Plateau growth.

The mechanism that causes the rapid uplift and active magmatism of the Hoh-Xil Basin in the northern Tibetan Plateau and hence the outward growth of the proto-plateau is highly debated, more specifically, over the relationship between deep dynamics and surface uplift. Until recently the Hoh-Xil Basin remained uncovered by seismic networks due to inaccessibility. Here, based on linear seismic arrays across the Hoh-Xil Basin, we present a three-dimensional S-wave velocity (VS) model of the crust and uppermost mantle structure beneath the Tibetan Plateau from ambient noise tomography. This model exhibits a widespread partially molten crust in the northern Tibetan Plateau but only isolated pockets in the south manifested as low-VS anomalies in the middle crust. The spatial correlation of the widespread low-VS anomalies with strong uppermost mantle low-VS anomalies and young exposed magmatic rocks in the Hoh-Xil Basin suggests that the plateau grew through lithospheric mantle removal and its driven magmatism.

Comparative analysis of noise and music exposure on inflammatory responses on lipopolysaccharide-induced septic rats.

OBJECTIVE: Environmental factors such as noise and music can significantly impact physiological responses, including inflammation. This study explored how environmental factors like noise and music affect lipopolysaccharide (LPS)-induced inflammation, with a focus on systemic and organ-specific responses. MATERIALS AND METHODS: 24 Wistar rats were divided into four groups (n = 6 per group): Control group, LPS group, noise-exposed group, and music-exposed group. All rats, except for the Control group, received 10 mg/kg LPS intraperitoneally. The rats in the noise-exposed group were exposed to 95 dB noise, and the music-exposed group listened to Mozart's K. 448 music (65-75 dB) for 1 h daily over 7 days. An enzyme-linked immunosorbent assay was utilized to detect the levels of inflammatory cytokines, including tumor necrosis factor-α (TNF-α) and interleukin-1ß (IL-1ß), in serum and tissues (lung, liver, and kidney). Western blot examined the phosphorylation levels of nuclear factor-κB (NF-κB) p65 in organ tissues. RESULTS: Compared with the Control group, LPS-induced sepsis rats displayed a significant increase in the levels of TNF-α and IL-1ß in serum, lung, liver, and kidney tissues, as well as a remarkable elevation in the p-NF-κB p65 protein expression in lung, liver, and kidney tissues. Noise exposure further amplified these inflammatory markers, while music exposure reduced them in LPS-induced sepsis rats. CONCLUSION: Noise exposure exacerbates inflammation by activating the NF-κB pathway, leading to the up-regulation of inflammatory markers during sepsis. On the contrary, music exposure inhibits NF-κB signaling, indicating a potential therapeutic effect in reducing inflammation.

No Effects of Auditory and Visual White Noise on Oculomotor Control in Children with ADHD.

BACKGROUND: White noise stimulation has demonstrated efficacy in enhancing working memory in children with ADHD. However, its impact on other executive functions commonly affected by ADHD, such as inhibitory control, remains largely unexplored. This research aims to explore the effects of two types of white noise stimulation on oculomotor inhibitory control in children with ADHD. METHOD: Memory guided saccade (MGS) and prolonged fixation (PF) performance was compared between children with ADHD (N = 52) and typically developing controls (TDC, N = 45), during auditory and visual white noise stimulation as well as in a no noise condition. RESULTS: Neither the auditory nor the visual white noise had any beneficial effects on performance for either group. CONCLUSIONS: White noise stimulation does not appear to be beneficial for children with ADHD in tasks that target oculomotor inhibitory control. Potential explanations for this lack of noise benefit will be discussed.

Males miss and females forgo: Auditory masking from vessel noise impairs foraging efficiency and success in killer whales.

Understanding how the environment mediates an organism's ability to meet basic survival requirements is a fundamental goal of ecology. Vessel noise is a global threat to marine ecosystems and is increasing in intensity and spatiotemporal extent due to growth in shipping coupled with physical changes to ocean soundscapes from ocean warming and acidification. Odontocetes rely on biosonar to forage, yet determining the consequences of vessel noise on foraging has been limited by the challenges of observing underwater foraging outcomes and measuring noise levels received by individuals. To address these challenges, we leveraged a unique acoustic and movement dataset from 25 animal-borne biologging tags temporarily attached to individuals from two populations of fish-eating killer whales (Orcinus orca) in highly transited coastal waters to (1) test for the effects of vessel noise on foraging behaviors-searching (slow-click echolocation), pursuit (buzzes), and capture and (2) investigate the mechanism of interference. For every 1 dB increase in maximum noise level, there was a 4% increase in the odds of searching for prey by both sexes, a 58% decrease in the odds of pursuit by females and a 12.5% decrease in the odds of prey capture by both sexes. Moreover, all but one deep (≥75 m) foraging attempt with noise ≥110 dB re 1 µPa (15-45 kHz band; n = 6 dives by n = 4 whales) resulted in failed prey capture. These responses are consistent with an auditory masking mechanism. Our findings demonstrate the effects of vessel noise across multiple phases of odontocete foraging, underscoring the importance of managing anthropogenic inputs into soundscapes to achieve conservation objectives for acoustically sensitive species. While the timescales for recovering depleted prey species may span decades, these findings suggest that complementary actions to reduce ocean noise in the short term offer a critical pathway for recovering odontocete foraging opportunities.