Background Masking Research Articles

Cloning, characterization, and evolutionary patterns of KCNQ4 genes in anurans.

Acoustic communication plays important roles in the survival and reproduction of anurans. The perception and discrimination of conspecific sound signals of anurans were always affected by masking background noise. Previous studies suggested that some frogs evolved the high-frequency hearing to minimize the low-frequency noise. However, the molecular mechanisms underlying the high-frequency hearing in anurans have not been well explored. Here, we cloned and obtained the coding regions of a high-frequency hearing-related gene (KCNQ4) from 11 representative anuran species and compared them with orthologous sequences from other four anurans. The sequence characteristics and evolutionary analyses suggested the highly conservation of the KCNQ4 gene in anurans, which supported their functional importance. Branch-specific analysis showed that KCNQ4 genes were under different evolutionary forces in anurans and most anuran lineages showed a generally strong purifying selection. Intriguingly, one significantly positively selected site was identified in the anuran KCNQ4 gene based on FEL model. Positive selection was also found along the common ancestor of Ranidae and Rhacophoridae as well as the ancestral O. tianmuii based on the branch-site analysis, and the positively selected sites identified were involved in or near the N-terminal ion transport and the potassium ion channel functional domain of the KCNQ4 genes. The present study revealed valuable information regarding the KCNQ4 genes in anurans and provided some new insights for the underpinnings of the high-frequency hearing in frogs.

Effects of noise on the hearing of intensive care unit nurses

This study aims to investigate the hearing problems experienced by nurses working in the intensive care units (ICUs) and gastroenterology departments of a hospital in China. A total of 93 participants who are currently employed in these settings were recruited for the study. The research methodology involved several components. Firstly, the pure tone audiometry (PTA) hearing threshold measurements were conducted in a closed audiometry room. The air conduction hearing and bone conduction hearing thresholds were recorded using calibrated equipment. Secondly, the participants underwent the Digits In Noise (DIN) test to assess their speech reception threshold in a noisy environment. They were instructed to wear headphones and adjust the volume to be a comfortable level while Chinese digit triplets were presented with a background masking noise. Thirdly, a questionnaire with previously validated and subjective components was also administered after the PTA and DIN tests. The survey aimed to assess self-reported auditory dysesthesia, including symptoms such as tinnitus, aural fullness, and otalgia. It was observed that ICU nurses exhibited more hearing loss at high frequencies above 4 kHz compared to non-ICU nurses. Additionally, nurses who worked more hours per week demonstrated worse hearing thresholds. Furthermore, ICU nurses reported more severe self-reported hearing problems compared with non-ICU nurses.

The effects of speech masking on neural tracking of acoustic and semantic features of natural speech

Listening environments contain background sounds that mask speech and lead to communication challenges. Sensitivity to slow acoustic fluctuations in speech can help segregate speech from background noise. Semantic context can also facilitate speech perception in noise, for example, by enabling prediction of upcoming words. However, not much is known about how different degrees of background masking affect the neural processing of acoustic and semantic features during naturalistic speech listening. In the current electroencephalography (EEG) study, participants listened to engaging, spoken stories masked at different levels of multi-talker babble to investigate how neural activity in response to acoustic and semantic features changes with acoustic challenges, and how such effects relate to speech intelligibility. The pattern of neural response amplitudes associated with both acoustic and semantic speech features across masking levels was U-shaped, such that amplitudes were largest for moderate masking levels. This U-shape may be due to increased attentional focus when speech comprehension is challenging, but manageable. The latency of the neural responses increased linearly with increasing background masking, and neural latency change associated with acoustic processing most closely mirrored the changes in speech intelligibility. Finally, tracking responses related to semantic dissimilarity remained robust until severe speech masking (−3 dB SNR). The current study reveals that neural responses to acoustic features are highly sensitive to background masking and decreasing speech intelligibility, whereas neural responses to semantic features are relatively robust, suggesting that individuals track the meaning of the story well even in moderate background sound.

Remote Sensing Image Scene Classification via Self-Supervised Learning and Knowledge Distillation

The main challenges of remote sensing image scene classification are extracting discriminative features and making full use of the training data. The current mainstream deep learning methods usually only use the hard labels of the samples, ignoring the potential soft labels and natural labels. Self-supervised learning can take full advantage of natural labels. However, it is difficult to train a self-supervised network due to the limitations of the dataset and computing resources. We propose a self-supervised knowledge distillation network (SSKDNet) to solve the aforementioned challenges. Specifically, the feature maps of the backbone are used as supervision signals, and the branch learns to restore the low-level feature maps after background masking and shuffling. The “dark knowledge” of the branch is transferred to the backbone through knowledge distillation (KD). The backbone and branch are optimized together in the KD process without independent pre-training. Moreover, we propose a feature fusion module to fuse feature maps dynamically. In general, SSKDNet can make full use of soft labels and has excellent discriminative feature extraction capabilities. Experimental results conducted on three datasets demonstrate the effectiveness of the proposed approach.

Integrating Genetic and Chromosome Maps of Allium cepa: From Markers Visualization to Genome Assembly Verification.

The ability to directly look into genome sequences has opened great opportunities in plant breeding. Yet, the assembly of full-length chromosomes remains one of the most difficult problems in modern genomics. Genetic maps are commonly used in de novo genome assembly and are constructed on the basis of a statistical analysis of the number of recombinations. This may affect the accuracy of the ordering and orientation of scaffolds within the chromosome, especially in the region of recombination suppression. Moreover, it is impossible to assign contigs lacking DNA markers. Here, we report the use of Tyr-FISH to determine the position of the short DNA sequence of markers and non-mapped unique copy sequence on the physical chromosomes of a large-genome onion (Allium cepa L.). In order to minimize potential background masking of the target signal, we improved our earlier developed pipeline for probe design. A total of 23 markers were located on physical chromosomes 2 and 6. The order of markers was corrected by the integration of genetic, pseudochromosome maps and cytogenetic maps. Additionally, the position of the mlh1 gene, which was not on the genetic map, was defined on physical chromosome 2. Tyr-FISH mapping showed that the order of 23.1% (chromosome 2) and 27.3% (chromosome 6) of the tested genes differed between physical chromosomes and pseudochromosomes. The results can be used for the improvement of pseudochromosome 2 and 6 assembly. The present study aims to demonstrate the value of the in situ visualization of DNA sequences in chromosome-scaffold genome assembly.

Know What You Don’t Know: Assessment of Overlooked Microplastic Particles in FTIR Images

Assessing data analysis routines (DARs) for microplastics (MP) identification in Fourier-transform infrared (FTIR) images left the question ‘Do we overlook any MP particles in our sample?’ widely unanswered. Here, a reference image of microplastics, RefIMP, is presented to answer this question. RefIMP contains over 1200 MP and non-MP particles that serve as a ground truth that a DAR’s result can be compared to. Together with our MatLab® script for MP validation, MPVal, DARs can be evaluated on a particle level instead of isolated spectra. This prevents over-optimistic performance expectations, as testing of three hypotheses illustrates: (I) excessive background masking can cause overlooking of particles, (II) random decision forest models benefit from high-diversity training data, (III) among the model hyperparameters, the classification threshold influences the performance most. A minimum of 7.99% overlooked particles was achieved, most of which were polyethylene and varnish-like. Cellulose was the class most susceptible to over-segmentation. Most false assignments were attributed to confusion of polylactic acid for polymethyl methacrylate and of polypropylene for polyethylene. Moreover, a set of over 9000 transmission FTIR spectra is provided with this work, that can be used to set up DARs or as standard test set.

Deep learning-based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data.

To enable a fast and automatic deep learning-based QSM reconstruction of tissues with diverse chemical shifts, relevant to most regions outside the brain. A UNET was trained to reconstruct susceptibility maps using synthetically generated, unwrapped, multi-echo phase data as input. The RMS error with respect to synthetic validation data was computed. The method was tested on two in vivo knee and two pelvis data sets. Comparisons were made to a conventional fat-water separation pipeline by applying a commonly used graph-cut algorithm, both without and with an extended mask for background field removal (FWS-CONV-QSM and FWS-MASK-CONV-QSM, respectively). Several regions of interest were segmented and compared. Furthermore, the approach was tested on a prostate cancer patient receiving low-dose-rate brachytherapy, to detect and localize the seeds by MRI. The RMS error was 0.292ppm with FWS-CONV-QSM and 0.123ppm for the UNET approach. Susceptibility maps were reconstructed much faster (< 10s) and completely automatically (no background masking needed) by the UNET compared with the other applied techniques (5min 51 s and 22 min 44 s for CONV-QSM and FWS-MASK-CONV-QSM, respectively. Background artifacts, fat-water swaps, and hypointense artifacts between I-125 seeds of a patient receiving low-dose brachytherapy in the prostate were largely reduced in the UNET approach. Deep learning-based QSM reconstruction, trained solely with synthetic data, is well-suited to rapidly reconstructing high-quality susceptibility maps in the presence of fat without needing masking for background field removal.

Comparative transcriptome analysis provides insights into the molecular mechanisms of high-frequency hearing differences between the sexes of Odorrana tormota

BackgroundAcoustic communication is important for the survival and reproduction of anurans and masking background noise is a critical factor for their effective acoustic communication. Males of the concave-eared frog (Odorrana tormota) have evolved an ultrasonic communication capacity to avoid masking by the widespread background noise of local fast-flowing streams, whereas females exhibit no ultrasonic sensitivity. However, the molecular mechanisms underlying the high-frequency hearing differences between the sexes of O. tormota are still poorly understood.ResultsIn this study, we sequenced the brain transcriptomes of male and female O. tormota, and compared their differential gene expression. A total of 4,605 differentially expressed genes (DEGs) between the sexes of O. tormota were identified and eleven of them were related to auditory based on the annotation and enrichment analysis. Most of these DEGs in males showed a higher expression trend than females in both quantity and expression quantity. The highly expressed genes in males were relatively concentrated in neurogenesis, signal transduction, ion transport and energy metabolism, whereas the up-expressed genes in females were mainly related to the growth and development regulation of specific auditory cells.ConclusionsThe transcriptome of male and female O. tormota has been sequenced and de novo assembled, which will provide gene reference for further genomic studies. In addition, this is the first research to reveal the molecular mechanisms of sex differences in ultrasonic hearing between the sexes of O. tormota and will provide new insights into the genetic basis of the auditory adaptation in amphibians during their transition from water to land.

Integrated healthcare acoustics for patients, patient advocates, and staff in environments of care

COVID surges have demonstrated worldwide that overloaded critical care facilities cannot transform environments of care rapidly enough to satisfy patient, patient advocate, or staff expectations. Healthcare acoustics is identified as a vital yet understudied factor, linking the front lines of healthcare to patient well-being. Acoustic concerns are categorized, and existing architectural hardware and software means and proposed strategies are here discussed to be applied in both new and existing critical care facilities. Priority concerns focus on staff verbal and machine communications between single patient rooms with integral toilet/shower, adjacent corridors, nurse stations, and remote staff locations. The intent is to maintain uniformly high patient/staff communications and relevant documentation (e.g., for continuity of care, and liability) across all shifts. Integrated visual and acoustic communications between staff and equipment are explored, including projected machine display images minus noisy signals. Staff speech, background masking, and messaging to patients are considered via an “acoustic pillow,” and via a wireless headset for staff, permitting hands-free focus on empathetic patient care. HVAC can be supplied via low-velocity displacement air, served via perimeter floor ducting, and exhausted via toilet/showers. Integrated healthcare acoustics, arguably, can reduce patient and staff stresses critical to recovery and retention, respectively.

HANDLING CRITICAL ASPECTS IN MASSIVE PHOTOGRAMMETRIC DIGITIZATION OF MUSEUM ASSETS

Abstract. In recent years, a growing interest in the 3D digitisation of museum assets has been pushed by the evident advantages of digital copies in supporting and advancing the knowledge, preservation and promotion of historical artefacts. Realising photo-realistic and precise digital twins of medium and small-sized movable objects implies several operations, still hiring open research problems and hampering the complete automation and derivation of satisfactory results while limiting processing time. The work examines some recurrent issues and potential solutions, summing up several experiences of photogrammetric-based massive digitisation projects. In particular, the article presents some insights into three crucial aspects of the photogrammetric pipeline. The first experiments tackle the Depth of Field (DoF) problem, especially when digitising small artefacts with macro-lenses. On the processing side, two decisive and time-consuming tasks are instead investigated: background masking and point cloud editing, exploring and proposing automatic solutions for speeding up the reconstruction process.

An enhanced artificial neural network model using the Harris Hawks optimiser for predicting food liking in the presence of background noise

This paper presents an enhanced artificial neural network (ANN) model for predicting the liking of food using the Harris Hawks optimiser (HHO) in the presence of different masking background noise types and levels, relative to the ambient background noise (i.e. no noise conditions). The results showed that the proposed model can predict the relative liking food ratings with higher performance (R2 = 0.70, RMSE = 0.8), as compared to traditional ANNs using feedforward neural networks (FFNNs) (R2 = 0.61, RMSE = 1.1) and statistical mixed models (R2 = 0.42, RMSE = 1.8). This model was used to find the threshold level that gives maximum relative food liking ratings for different types of noise. This threshold level varied between 30 and 35 dBA for three noise types. The liking of food in the presence of background noise depends on acoustic and non-acoustic factors. A feature analysis using the “RReliefF” algorithm was used to investigate the relative importance of these acoustic and non-acoustic factors on the relative food liking using predicted model outcomes. Acoustic factors such as noise type and level had higher importance weights on the relative liking of food in the presence of background noise than non-acoustic factors such as gender, sensitivity, and age. The results presented herein are relevant for future and more targeted noise assessment and mitigation strategies in dining areas.

Negative emotions in the target speaker's voice enhance speech recognition under "cocktail-party" environments.

Under a "cocktail-party" environment with simultaneous multiple talkers, recognition of target speech is effectively improved by a number of perceptually unmasking cues. It remains unclear whether emotions embedded in the target-speaker's voice can either improve speech perception alone or interact with other cues facilitating speech perception against a masker background. This study used two target-speaker voices with different emotional valences to examine whether recognition of target speech is modulated by the emotional valence when the target speech and the maskers were perceptually co-located or separated. The results showed that both the speech recognition against the masker background and the separation-induced unmasking effect were higher for the target speaker with a negatively emotional voice than for the target speaker with a positively emotional voice. Moreover, when the negative voice was fear conditioned, the target-speech recognition was further improved against speech informational masking. These results suggested that the emotionally vocal unmasking cue interacts significantly with the perceived spatial-separation unmasking cue, facilitating the unmasking effect against a masking background. Thus, emotional features embedded in the target-speaker's vocal timbre are also useful for unmasking the target speech in "cocktail-party" environments.

Deep learning-based diatom taxonomy on virtual slides

Deep convolutional neural networks are emerging as the state of the art method for supervised classification of images also in the context of taxonomic identification. Different morphologies and imaging technologies applied across organismal groups lead to highly specific image domains, which need customization of deep learning solutions. Here we provide an example using deep convolutional neural networks (CNNs) for taxonomic identification of the morphologically diverse microalgal group of diatoms. Using a combination of high-resolution slide scanning microscopy, web-based collaborative image annotation and diatom-tailored image analysis, we assembled a diatom image database from two Southern Ocean expeditions. We use these data to investigate the effect of CNN architecture, background masking, data set size and possible concept drift upon image classification performance. Surprisingly, VGG16, a relatively old network architecture, showed the best performance and generalizing ability on our images. Different from a previous study, we found that background masking slightly improved performance. In general, training only a classifier on top of convolutional layers pre-trained on extensive, but not domain-specific image data showed surprisingly high performance (F1 scores around 97%) with already relatively few (100–300) examples per class, indicating that domain adaptation to a novel taxonomic group can be feasible with a limited investment of effort.

Audiotactile integration in the Pacinian corpusclel's maximum sensitivity frequency range.

It has been shown that vibrotactile stimuli elicit sound perception either on their own or by enhancing otherwise inaudible sounds. For taking advantage of this phenomenon in the design of vibrotactile interfaces, it is important to identify its properties with respect to the level of the excitation frequency. The aim of this work is to further substantiate previous research results that indicate a prevalence of this phenomenon at a specific range of frequencies (200-390 Hz), which roughly pertains to the Pacinian corpuscle's maximum sensitivity range. Thirteen young adults participated in the study, which included comparison between sound-and-vibration versus sound-only signals. Masking background noise and no-touch control experiments were included to further support the outcome. The results validate the hypothesis that vibrotactile excitation at the index fingertip can enhance otherwise inaudible tones in the specific range of frequencies.

The medial agranular cortex mediates attentional enhancement of prepulse inhibition of the startle reflex

The startle reflex, which interferes with on-going cognitive/behavioral activities, is of important protective function for humans and animals. Prepulse inhibition (PPI), as an operational measure of sensorimotor gating, is the suppression of the startle reflex in response to an intense startling stimulus (pulse) when this startling stimulus is shortly preceded by a weaker non-startling stimulus (prepulse). In both humans and laboratory animals, PPI can be enhanced by facilitating selective attention to the prepulse, suggesting that higher-order cognitive/perceptual processes modulate PPI. It has been well known that both the cholinergic system located in the basal forebrain and the deep layers of the superior colliculus in the PPI-mediating circuit are top-down modulated by the medial agranular cortex (AGm), which is a subdivision of the medial prefrontal cortex (PFC) and has wide axonal connections with both cortical regions (including the posterior parietal cortex) and subcortical structures critical for attention/orientation processes. This study investigated whether the AGm is involved in attentional modulation of PPI. The results showed that PPI was enhanced by fear conditioning of the prepulse, and then further enhanced by perceived spatial separation between the conditioned prepulse and a back-ground masking noise based on the auditory precedence effect. Bilateral injection of 2-mM kynurenic acid, a broad spectrum antagonist of glutamate receptors, into the AGm, but not the primary somatosensory cortex, eliminated these two types of attentional enhancement of PPI. Thus, the AGm plays a role in facilitating attention to the prepulse and is involved in the top-down modulation of PPI.

Multi-Person Pose Estimation Using Thermal Images

Human pose estimation is an important task for several applications, such as video surveillance systems. However, color (i.e., RGB) images may not always be available under certain conditions, such as privacy issues and lack of illumination. In these scenarios, thermal images are more prominent than color images. We introduce in this study ThermalPose, which is a neural network system that parses thermal images and extracts accurate 2D human poses. ThermalPose uses lightweight neural network models that can be easily matched to the design requirements for Internet-of-Things applications. The performance of ThermalPose in visible scenes only slightly decreases compared with that of the state-of-the-art vision-based pose estimator. Meanwhile, in complex scenes with masking background textures or lack of illumination, ThermalPose does not degenerate in terms of performance, whereas the vision-based system completely fails.

Efficient collection and automatic annotation of real-world object images by taking advantage of post-diminished multiple visual markers

ABSTRACTTo collect a human-annotated dataset for training deep convolutional neural networks is a very time-consuming and laborious process. To reduce this burden, we previously proposed an automated annotation by placing one visual marker above the detection target object in the training phase. However, in this approach, occasionally the marker hides the object surface. To avoid this issue, we propose placing a pedestal with multiple markers at the bottom of the object. If we use multiple markers, the object can be annotated even when the object hides some of the markers. Besides that, the simple modification of placing the markers on the bottom allows the use of simple background masking to avoid the neural network learning the remaining markers in the training image as a feature of the object. Background masking can completely remove the markers during the training process. Experiments showed the proposed vision system using our automatic object annotation outperformed the vision system using manual annotation in terms of object detection, orientation estimation, and 2D position estimation while reducing the time required for dataset collection from 16.1 hours to 7.30 hours.

Auditory enhancement under forward masking in normal-hearing and hearing-impaired listeners.

A target within a spectrally notched masker can be enhanced by a preceding copy of the masker. Enhancement can also increase the effectiveness of the target as a forward masker. Enhancement has been reported in hearing-impaired listeners under simultaneous but not forward masking. However, previous studies of enhancement under forward masking did not fully assess the potential effect of differences in sensation level or spectral resolution between the normal-hearing and hearing-impaired listeners. This study measured enhancement via forward masking in hearing-impaired and age-matched normal-hearing listeners with different spectral notches in the masker, to account for potential differences in frequency selectivity, and with levels equated by adding a background masking noise to equate both sensation level and sound pressure level or by reducing the sound pressure level of the stimuli to equate sensation level. Hearing-impaired listeners showed no significant enhancement, regardless of spectral notch width. Normal-hearing listeners showed enhancement at high levels, but showed less enhancement when sensation levels were reduced to match those of the hearing-impaired group, either by reducing sound levels or by adding a masking noise. The results confirm a lack of forward-masked enhancement in hearing-impaired listeners but suggest this may be partly due to reduced sensation level.

Multi-atlas segmentation of the skeleton from whole-body MRI-Impact of iterative background masking.

To improve multi-atlas segmentation of the skeleton from whole-body MRI. In particular, we study the effect of employing the atlas segmentations to iteratively mask tissues outside of the region of interest to improve the atlas alignment and subsequent segmentation. An improved atlas registration scheme is proposed. Starting from a suitable initial alignment, the alignment is refined by introducing additional stages of deformable registration during which the image sampling is limited to the dilated atlas segmentation label mask. The performance of the method was demonstrated using leave-one-out cross-validation using atlases of 10 whole-body 3D-T1 images of prostate cancer patients with bone metastases and healthy male volunteers, and compared to existing state of the art. Both registration accuracy and resulting segmentation quality, using four commonly used label fusion strategies, were evaluated. The proposed method showed significant improvement in registration and segmentation accuracy with respect to the state of the art for all validation criteria and label fusion strategies, resulting in a Dice coefficient of 0.887 (STEPS label fusion). The average Dice coefficient for the multi-atlas segmentation showed over 11% improvement with a decrease of false positive rate from 28.3% to 13.2%. For this application, repeated application of the background masking did not lead to significant improvement of the segmentation result. A registration strategy, relying on the use of atlas segmentations as mask during image registration was proposed and evaluated for multi-atlas segmentation of whole-body MRI. The approach significantly improved registration and final segmentation accuracy and may be applicable to other structures of interest.

Bats Actively Use Leaves as Specular Reflectors to Detect Acoustically Camouflaged Prey

Filtering relevant signals from noisy sensory input is a crucial challenge for animals [1, 2]. Many bats are acoustic specialists relying on sound to find prey. They discern salient acoustic signals from irrelevant background masking noise. It has long been considered a sensory impossibility for bats to use solely echolocation for the detection of silent and motionless prey resting directly on foliage due to the masking effects of background echoes [3, 4]. Some bats, however, do successfully perform this seemingly impossible task [5], raising the question-what underlying acoustic and behavioral mechanisms do bats use to solve this conundrum? To address this question, we used biomimetic sonar to record high-resolution measurements of echoes from insects resting on leaves. Based on our echo recordings, we predicted optimal approach angles from which masking echoes can best be avoided. In behavioral experiments, we put these predictions to test. We recorded the prey approach behavior of wild bats in a flight cage equipped with an ultrasonic microphone synchronized with two high-speed cameras for 3D flightpath reconstructions. Bats approached prey from our predicted optimal oblique angles, using the leaf as a specular reflector to uncover previously acoustically hidden prey. Our findings disclose key behavioral and acoustic mechanisms enabling the detection of prey echoes that background clutter would otherwise mask. This work adds to the fundamental understanding of how bat echolocation strategies can override acoustic camouflage by silent, motionless prey, thus providing new insights into the evolutionary arms race between predators and their prey.