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The quest for higher spatial and/or temporal resolution in functional MRI (fMRI) while preserving a sufficient temporal signal-to-noise ratio (tSNR) has generated a tremendous amount of methodological contributions in the last decade ranging from Cartesian vs. non-Cartesian readouts, 2D vs. 3D acquisition strategies, parallel imaging and/or compressed sensing (CS) accelerations and simultaneous multi-slice acquisitions to cite a few. In this paper, we investigate the use of a finely tuned version of 3D-SPARKLING. This is a non-Cartesian CS-based acquisition technique for high spatial resolution whole-brain fMRI. We compare it to state-of-the-art Cartesian 3D-EPI during both a retinotopic mapping paradigm and resting-state acquisitions at 1mm3 (isotropic spatial resolution). This study involves six healthy volunteers and both acquisition sequences were run on each individual in a randomly-balanced order across subjects. The performances of both acquisition techniques are compared to each other in regards to tSNR, sensitivity to the BOLD effect and spatial specificity. Our findings reveal that 3D-SPARKLING has a higher tSNR than 3D-EPI, an improved sensitivity to detect the BOLD contrast in the gray matter, and an improved spatial specificity. Compared to 3D-EPI, 3D-SPARKLING yields, on average, 7% more activated voxels in the gray matter relative to the total number of activated voxels. [ABSTRACT FROM AUTHOR]

Background: With recent advances in magnetic resonance imaging (MRI) technology, the practical role of lung MRI is expanding despite the inherent challenges of the thorax. The purpose of our study was to evaluate the current status of the concurrent dephasing and excitation (CODE) ultrashort echo-time sequence and the T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence in the evaluation of thoracic disease by comparing it with the gold standard computed tomography (CT). Methods: Twenty-four patients with lung cancer and mediastinal masses underwent both CT and MRI including T1-weighted VIBE and CODE. For CODE images, data were acquired in free breathing and end-expiratory images were reconstructed using retrospective respiratory gating. All images were evaluated through qualitative and quantitative approaches regarding various anatomical structures and lesions (nodule, mediastinal mass, emphysema, reticulation, honeycombing, bronchiectasis, pleural plaque and lymphadenopathy) inside the thorax in terms of diagnostic performance in making specific decisions. Results: Depiction of the lung parenchyma, mediastinal and pleural lesion was not significant different among the three modalities (p > 0.05). Intra-tumoral and peritumoral features of lung nodules were not significant different in the CT, VIBE or CODE images (p > 0.05). However, VIBE and CODE had significantly lower image quality and poorer depiction of airway, great vessels, and emphysema compared to CT (p < 0.05). Image quality of central airways and depiction of bronchi were significantly better in CODE than in VIBE (p < 0.001 and p = 0.005). In contrast, the depiction of the vasculature was better for VIBE than CODE images (p = 0.003). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were significant greater in VIBE than CODE except for SNRlung and SNRnodule (p < 0.05). Conclusions: Our study showed the potential of CODE and VIBE sequences in the evaluation of localized thoracic abnormalities including solid pulmonary nodules. [ABSTRACT FROM AUTHOR]

For a narrow-brand seismograph with a flat response range limited, it cannot precisely record the signal of a ground motion and output the records with the low-frequency components cut down. A transfer function is usually used to spread the spectrum of the narrow-brand seismic records. However, the accuracy of the commonly used transfer function is not high. The authors derive a new transfer function based on the Laplace transform, bilinear transform, and Nyquist sampling theory to solve this problem. And then, the derived transfer function is used to correct the narrow-band velocity records from the Hi-net. The corrected velocity records are compared with the velocities integrated from the synchronously recorded broad-band acceleration at the same station with Hi-net. Meanwhile, the corrected records are compared with those corrected by the Nakata transfer function. The results show that the calculation accuracy of the derived transfer function is higher than the Nakata transfer function. However, when the signal-to-noise ratio is below 24, its accuracy diminishes, and it is unable to recover signals within the 0.05–0.78Hz frequency band. [ABSTRACT FROM AUTHOR]

The standard whale algorithm is prone to suboptimal results and inefficiencies in high-dimensional search spaces. Therefore, examining the whale optimization algorithm components is critical. The computer-generated initial populations often exhibit an uneven distribution in the solution space, leading to low diversity. We propose a fusion of this algorithm with a discrete recombinant evolutionary strategy to enhance initialization diversity. We conduct simulation experiments and compare the proposed algorithm with the original WOA on thirteen benchmark test functions. Simulation experiments on unimodal or multimodal benchmarks verified the better performance of the proposed RESHWOA, such as accuracy, minimum mean, and low standard deviation rate. Furthermore, we performed two data reduction techniques, Bhattacharya distance and signal-to-noise ratio. Support Vector Machine (SVM) excels in dealing with high-dimensional datasets and numerical features. When users optimize the parameters, they can significantly improve the SVM's performance, even though it already works well with its default settings. We applied RESHWOA and WOA methods on six microarray cancer datasets to optimize the SVM parameters. The exhaustive examination and detailed results demonstrate that the new structure has addressed WOA's main shortcomings. We conclude that the proposed RESHWOA performed significantly better than the WOA. [ABSTRACT FROM AUTHOR]

This work introduces an open-sourced graphical user interface (GUI) software enabling the combination of multi-channel magnetic resonance spectroscopy data with different literature-based methods for the improvement of the quality and reliability of combined spectra. The multi-channel magnetic resonance spectroscopy graphical user interface (McMRSGUI) is a MATLAB-based spectroscopy processing GUI equipped to load multi-channel MRS data, pre-process, combine, and export combined data for evaluation with open-source quantification software (jMRUI). A literature-based, decision-tree process was incorporated into the combination type selection to serve as a guide to minimize spectral distortion in selecting between weighting methods. Multi-channel, simulated spectra were combined with the different combination techniques and evaluated for spectral distortion to validate the code. The incorporation of the combination methods into a single processing software enables multi-channel magnetic resonance spectroscopy (MRS) data to be combined and compared for improved spectral quality with little user knowledge of combination techniques. Through the spectral peak distortion simulation of the combination methods, combined signal-to-noise ratio (SNR) values from the literature were verified. The spectral peak distortion simulation provides a secondary tool for researchers to estimate the spectral SNR levels when spectral distortion could occur and use this knowledge to further guide the selection of their combination technique. The McMRSGUI provides a software toolkit for evaluating multi-channel MRS data and their combination. Simulations evaluating spectral distortion at different noise levels were performed for each combination method to validate the GUI and demonstrate a method for researchers to assess the combined SNR levels at which they could be introducing spectral distortion. [ABSTRACT FROM AUTHOR]

Drug addiction can cause severe damage to the human brain, leading to significant problems in cognitive processing, such as irritability, speech distortions, and exaggeration of negative stimuli. Speech plays a fundamental role in social interaction, including both the production and perception. The ability to perceive communicative functions conveyed through speech is crucial for successful interpersonal communication and the maintaining good social relationships. However, due to the limited number of previous studies, it remains unclear whether the cognitive disorder caused by drug addiction affects the perception of communicative function conveyed in Mandarin speech. To address this question, we conducted a perception experiment involving sixty male participants, including 25 heroin addicts and 35 healthy controls. The experiment aimed to examine the perception of three communicative functions (i.e., statement, interrogative, and imperative) under three background noise conditions (i.e., no noise, SNR [Signal to Noise Ratio] = 10, and SNR = 0). Eight target sentences were first recorded by two native Mandarin speakers for each of the three communicative functions. Each half was then combined with Gaussian White Noise under two background noise conditions (i.e., SNR = 10 and SNR = 0). Finally, 48 speech stimuli were included in the experiment with four options provided for perceptual judgment. The results showed that, under the three noise conditions, the average perceptual accuracies of the three communicative functions were 80.66% and 38% for the control group and the heroin addicts, respectively. Significant differences were found in the perception of the three communicative functions between the control group and the heroin addicts under the three noise conditions, except for the recognition of imperative under strong noise condition (i.e., SNR = 0). Moreover, heroin addicts showed good accuracy (around 50%) in recognizing imperative and poor accuracy (i.e., lower than the chance level) in recognizing interrogative. This paper not only fills the research gap in the perception of communicative functions in Mandarin speech among drug addicts but also enhances the understanding of the effects of drugs on speech perception and provides a foundation for the speech rehabilitation of drug addicts. [ABSTRACT FROM AUTHOR]

Purpose: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). Methods: The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. Results: DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P <.001). In general, DLIR showed superior subjective image quality for ULDCT images (P <.001) and comparable quality for LDCT images compared to ASIR-V50 (P =.01–1). The per-nodule sensitivities of observers for Lung-RADS category 3–4 nodules were 70.6–88.2% and 64.7–82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P =.50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P =.65). Conclusion: DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images. [ABSTRACT FROM AUTHOR]

To further enhance the residual current detection capability of low-voltage distribution networks, an improved adaptive residual current detection method that combines variational modal decomposition (VMD) and BP neural network (BPNN) is proposed. Firstly, the method employs the envelope entropy as the adaptability function, optimizes the [k, ɑ] combination value of the VMD decomposition using the bacterial foraging-particle swarm algorithm (BFO-PSO), and utilizes the interrelation number R as the classification index with the Least Mean Square Algorithm (LMS) to classify, filter, and extract the effective signal from the decomposed signal. Then, the extracted signals are detected by BPNN, and the training data are utilized to predict the residual current signals. Simulation and experimental data demonstrate that the proposed algorithm exhibits strong robustness and high detection accuracy. With an ambient noise of 10dB, the signal-to-noise ratio is 16.3108dB, the RMSE is 0.4359, and the goodness-of-fit is 0.9627 after processing by the algorithm presented in this paper, which are superior to the Variational Modal Decomposition-Long Short-Term Memory (VMD-LSTM) and Normalized-Least Mean Square (N-LMS) detection methods. The results were also statistically analyzed in conjunction with the Kolmogorov-Smirnov test, which demonstrated significance at the experimental data level, indicating the high accuracy of the algorithms presented in this paper and providing a certain reference for new residual current protection devices for biological body electrocution. [ABSTRACT FROM AUTHOR]

Single-cell transcriptomics has the potential to provide novel insights into poorly studied microbial eukaryotes. Although several such technologies are available and benchmarked on mammalian cells, few have been tested on protists. Here, we applied a microarray single-cell sequencing (MASC-seq) technology, that generates microscope images of cells in parallel with capturing their transcriptomes, on three species representing important plankton groups with different cell structures; the ciliate Tetrahymena thermophila, the diatom Phaeodactylum tricornutum, and the dinoflagellate Heterocapsa sp. Both the cell fixation and permeabilization steps were adjusted. For the ciliate and dinoflagellate, the number of transcripts of microarray spots with single cells were significantly higher than for background spots, and the overall expression patterns were correlated with that of bulk RNA, while for the much smaller diatom cells, it was not possible to separate single-cell transcripts from background. The MASC-seq method holds promise for investigating "microbial dark matter", although further optimizations are necessary to increase the signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

This study aims to restore grating lobe artifacts and improve the image resolution of sparse array ultrasonography via a deep learning predictive model. A deep learning assisted sparse array was developed using only 64 or 16 channels out of the 128 channels in which the pitch is two or eight times the original array. The deep learning assisted sparse array imaging system was demonstrated on ex vivo porcine teeth. 64- and 16-channel sparse array images were used as the input and corresponding 128-channel dense array images were used as the ground truth. The structural similarity index measure, mean squared error, and peak signal-to-noise ratio of predicted images improved significantly (p < 0.0001). The resolution of predicted images presented close values to ground truth images (0.18 mm and 0.15 mm versus 0.15 mm). The gingival thickness measurement showed a high level of agreement between the predicted sparse array images and the ground truth images, as indicated with a bias of -0.01 mm and 0.02 mm for the 64- and 16-channel predicted images, respectively, and a Pearson's r = 0.99 (p < 0.0001) for both. The gingival thickness bias measured by deep learning assisted sparse array imaging and clinical probing needle was found to be <0.05 mm. Additionally, the deep learning model showed capability of generalization. To conclude, the deep learning assisted sparse array can reconstruct high-resolution ultrasound image using only 16 channels of 128 channels. The deep learning model performed generalization capability for the 64-channel array, while the 16-channel array generalization would require further optimization. [ABSTRACT FROM AUTHOR]

To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected ILD were retrospectively reviewed. Datasets were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction Veo (ASiR-V), and DLM. For image quality analysis, the signal, noise, signal-to-noise ratio (SNR), blind/referenceless image spatial quality evaluator (BRISQUE), and visual scoring were evaluated. Also, CT patterns of usual interstitial pneumonia (UIP) were classified according to the 2022 idiopathic pulmonary fibrosis (IPF) diagnostic criteria. The differences between CT images subjected to FBP, ASiR-V 30%, and DLM were evaluated. The image noise and BRISQUE scores of DLM images was lower and SNR was higher than that of the ASiR-V and FBP images (ASiR-V vs. DLM, p < 0.001 and FBP vs. DLR-M, p < 0.001, respectively). The agreement of the diagnostic categorization of IPF between the three reconstruction methods was almost perfect (κ = 0.992, CI 0.990–0.994). Image quality was improved with DLM compared to ASiR-V and FBP. [ABSTRACT FROM AUTHOR]

Cooperative spectrum sensing (CSS) involves multiple secondary users (SUs) reporting primary user (PU) channel sensing states to the fusion center (FC). However, the high overheads associated with multi-user CSS impose power limitations that limit its usefulness in unmanned aerial vehicle (UAV) networks. To address this challenge, we propose a virtual CSS, where a single UAV conducts CSS while following a circular flight trajectory in the air. The novelty of our approach is presenting a working frame structure for the UAV flight, including sensing and data transmission periods with further division of the sensing time into mini-sensing slots. In the virtual CSS, UAV performs local sensing decisions in each mini-slot and accumulates them for a final decision. The proposed virtual CSS scheme exploits sequential decision fusion (SDF), which sequentially adds individual mini-slot decisions. Additionally, we leverage machine learning (ML), employing AdaBoost ensembling classifier (ENC), to inspect flight conditions and reconfigure mini-slot periods dynamically for both traditional decision fusion (TDF) and our proposed SDF schemes. Furthermore, we identify an optimal decision threshold (ODT) for the proposed SDF, enabling the comparison of sequential results with an adjustable threshold through majority voting. This novel approach results in energy efficiency and improved throughput for virtual CSS using SDF, surpassing the performance of TDF, which relies on collecting entire mini-slot reports for its final decision. Simulation results demonstrate the effectiveness of the proposed SDF following the ENCODT (SDF-ENCODT) scheme compared to existing techniques from the literature. We explore varying levels of UAV flight velocities, moving radius, detection probability demand, and channel signal-to-noise ratio (SNR), reinforcing the significance of our contribution. Our research highlights the motivation to address spectrum scarcity in UAV communication by proposing an innovative virtual CSS scheme based on SDF. The proposed approach enhances spectrum utilization, overcomes power limitations, and substantially improves CSS for UAV networks. [ABSTRACT FROM AUTHOR]

Brain computer interfaces (BCI) depend on reliable realtime detection of conscious EEG changes for example to control a video game. However, scalp recordings are contaminated with non-stationary noise, such as facial muscle activity and eye movements. This interferes with the detection process making it potentially unreliable or even impossible. We have developed a new methodology which provides a hard and measurable criterion if conscious EEG changes can be detected in the presence of non-stationary noise by requiring the signal-to-noise ratio of a scalp recording to be greater than the SNR-wall which in turn is based on the highest and lowest noise variances of the recording. As an instructional example, we have recorded signals from the central electrode Cz during eight different activities causing non-stationary noise such as playing a video game or reading out loud. The results show that facial muscle activity and eye-movements have a strong impact on the detectability of EEG and that minimising both eye-movement artefacts and muscle noise is essential to be able to detect conscious EEG changes. [ABSTRACT FROM AUTHOR]

Despite the plethora of studies investigating listening effort and the amount of research concerning music perception by cochlear implant (CI) users, the investigation of the influence of background noise on music processing has never been performed. Given the typical speech in noise recognition task for the listening effort assessment, the aim of the present study was to investigate the listening effort during an emotional categorization task on musical pieces with different levels of background noise. The listening effort was investigated, in addition to participants' ratings and performances, using EEG features known to be involved in such phenomenon, that is alpha activity in parietal areas and in the left inferior frontal gyrus (IFG), that includes the Broca's area. Results showed that CI users performed worse than normal hearing (NH) controls in the recognition of the emotional content of the stimuli. Furthermore, when considering the alpha activity corresponding to the listening to signal to noise ratio (SNR) 5 and SNR10 conditions subtracted of the activity while listening to the Quiet condition—ideally removing the emotional content of the music and isolating the difficulty level due to the SNRs- CI users reported higher levels of activity in the parietal alpha and in the homologous of the left IFG in the right hemisphere (F8 EEG channel), in comparison to NH. Finally, a novel suggestion of a particular sensitivity of F8 for SNR-related listening effort in music was provided. [ABSTRACT FROM AUTHOR]

Background: Accurate assessment of plaque accumulation near the carotid bifurcation is important for the effective prevention and treatment of stroke. However, vessel and plaque delineation using MRI can be limited by low contrast-to-noise ratio (CNR) and long acquisition times. In this work, a 10-channel phased-array receive coil design for bilateral imaging of the carotid bifurcation using 3T MRI is proposed. Methods: The proposed 10-channel receive coil was compared to a commercial 4-channel receive coil configuration using data acquired from phantoms and healthy volunteers (N = 9). The relative performance of the coils was assessed, by comparing signal-to-noise ratio (SNR), noise correlation, g-factor noise amplification, and the CNR between vessel wall and lumen using black-blood sequences. Patient data were acquired from 12 atherosclerotic carotid artery disease patients. Results: The 10-channel coil consistently provided substantially increased SNR in phantoms (+77 ± 27%) and improved CNR in healthy carotid arteries (+62 ± 11%), or reduced g-factor noise amplification. Patient data showed excellent delineation of atherosclerotic plaque along the length of the carotid bifurcation using the 10-channel coil. Conclusions: The proposed 10-channel coil design allows for improved visualization of the carotid arteries and the carotid bifurcation and increased parallel imaging acceleration factors relative to a commercial 4-channel coil design. [ABSTRACT FROM AUTHOR]

Increasing utilization of computed tomography (CT) has raised concerns regarding CT radiation dose and technology has been developed to achieve an appropriate balance between image quality, radiation dose, and the amount of contrast material. This study was planned to evaluate the image quality and radiation dose in pancreatic dynamic computed tomography (PDCT) with 90-kVp tube voltage and reduction of the standard amount of contrast agent, compared with 100-kVp PDCT of the research hospital's convention. Total of 51 patients with both CT protocols were included. The average Hounsfield units (HU) values of the abdominal organs and image noise were measured for objective image quality analysis. Two radiologists evaluated five categories of image qualities such as subjective image noise, visibility of small structure, beam hardening or streak artifact, lesion conspicuity and overall diagnostic performance for subjective image quality analysis. The total amount of contrast agent, radiation dose, and image noise decreased in the low-kVp group, by 24.4%, 31.7%, and 20.6%, respectively (p < 0.001). The intraobserver and interobserver agreements were moderate to substantial (k = 0.4−0.8). The contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and figure of merit of the almost organs except psoas muscle in the low-kVp group were significantly higher (p < 0.001). Except for lesion conspicuity, both reviewers judged that subjective image quality of the 90-kVp group was better (p < 0.001). With 90-kVp tube voltage, 25% reduced contrast agent volume with advanced iteration algorithm and high tube current modulation achieved radiation dose reduction of 31.7%, as well as better image quality and diagnostic confidence. [ABSTRACT FROM AUTHOR]

For the problem of direction-of-arrival (DOA) estimation using a coprime array, there are high spatial spectrum outputs of false alarms caused by the overlap of main and grating lobes from subarrays. In this paper, a DOA estimation method of more than two co-frequency sources for a coprime vector hydrophone array is proposed. The method is based on vector cross terms (VCTs), making full use of the directivity of channel combinations for vector hydrophones. Based on VCTs, the characteristic data point identification method is conducted and ensures that the bearing data with the characteristic can be preserved. For further interference rejection, the paper designs Queue Selection (QS) method based on inverse beamforming. The influence of grating lobes can be weakened with the QS, further improving the accuracy of direction extraction. The algorithm in this work does not require decoherence processing, and the simulation work shows that it achieves stable DOA estimation with a low signal-to-noise ratio (SNR). [ABSTRACT FROM AUTHOR]

Purpose: We aimed to validate the diagnostic performance of accelerated post-contrast magnetization-prepared rapid gradient-echo (MPRAGE) using wave–controlled aliasing in parallel imaging (Wave-CAIPI) for enhancing intracranial lesions, compared with conventional MPRAGE. Methods: A total of 233 consecutive patients who underwent post-contrast Wave-CAIPI and conventional MPRAGE (scan time: 2 min 39 s vs. 4 min 30 s) were retrospectively evaluated. Two radiologists independently assessed whole images for the presence and diagnosis of enhancing lesions. The diagnostic performance for non-enhancing lesions, quantitative parameters (diameter of enhancing lesions, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and contrast rate), qualitative parameters (grey-white matter differentiation and conspicuity of enhancing lesions), and image qualities (overall image quality and motion artifacts) were also surveyed. The weighted kappa and percent agreement were used to evaluate the diagnostic agreement between the two sequences. Results: Wave-CAIPI MPRAGE achieved significantly high agreement for the detection (98.7%[460/466], κ = 0.965) and diagnosis (97.8%[455/466], κ = 0.955) of enhancing intracranial lesions with conventional MPRAGE in pooled analysis. Detection and diagnosis of non-enhancing lesions (97.6% and 96.9% agreement), and diameter of enhancing lesions (P>0.05) also demonstrated high agreements between two sequences. Although Wave-CAIPI MPRAGE show lower SNR (P<0.01) than conventional MRAGE, it fulfilled comparable CNR (P = 0.486) and higher contrast rate (P<0.01). The qualitative parameters show similar value (P>0.05). The overall image quality was slightly poor, however, motion artifacts were better in Wave-CAIPI MPRAGE (both P = 0.005). Conclusion: Wave-CAIPI MPRAGE provides reliable diagnostic performance for enhancing intracranial lesions within half of the scan time compared with conventional MPRAGE. [ABSTRACT FROM AUTHOR]

Feature selection in high dimensional gene expression datasets not only reduces the dimension of the data, but also the execution time and computational cost of the underlying classifier. The current study introduces a novel feature selection method called weighted signal to noise ratio (WSNR) by exploiting the weights of features based on support vectors and signal to noise ratio, with an objective to identify the most informative genes in high dimensional classification problems. The combination of two state-of-the-art procedures enables the extration of the most informative genes. The corresponding weights of these procedures are then multiplied and arranged in decreasing order. Larger weight of a feature indicates its discriminatory power in classifying the tissue samples to their true classes. The current method is validated on eight gene expression datasets. Moreover, results of the proposed method (WSNR) are also compared with four well known feature selection methods. We found that the (WSNR) outperform the other competing methods on 6 out of 8 datasets. Box-plots and Bar-plots of the results of the proposed method and all the other methods are also constructed. The proposed method is further assessed on simulated data. Simulation analysis reveal that (WSNR) outperforms all the other methods included in the study. [ABSTRACT FROM AUTHOR]

Image style transfer aims to assign a specified artist's style to a real image. However, most existing methods cannot generate textures of various thicknesses due to the rich semantic information of the input image. The image loses some semantic information through style transfer with a uniform stroke size. To address the above problems, we propose an improved multi-stroke defocus adaptive style transfer framework based on a stroke pyramid, which mainly fuses various stroke sizes in the image spatial dimension to enhance the image content interpretability. We expand the receptive field of each branch and then fuse the features generated by the multiple branches based on defocus degree. Finally, we add an additional loss term to enhance the structural features of the generated image. The proposed model is trained using the Common Objects in Context (COCO) and Synthetic Depth of Field (SYNDOF) datasets, and the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used to evaluate the overall quality of the output image and its structural similarity with the content image, respectively. To validate the feasibility of the proposed algorithm, we compare the average PSNR and SSIM values of the output of the modified model and those of the original model. The experimental results show that the modified model improves the PSNR and SSIM values of the outputs by 1.43 and 0.12 on average, respectively. Compared with the single-stroke style transfer method, the framework proposed in this study improves the readability of the output images with more abundant visual expression. [ABSTRACT FROM AUTHOR]

In studies exploring the potential for nanosecond duration electric pulses to serve as a novel modality for neuromodulation, we found that a 5 ns pulse triggers an immediate rise in [Ca2+]i in isolated bovine adrenal chromaffin cells. To facilitate ongoing efforts to understand underlying mechanisms and to work toward carrying out investigations in cells in situ, we describe the suitability and advantages of using isolated murine adrenal chromaffin cells expressing, in a Cre-dependent manner, the genetically-encoded Ca2+indicator GCaMP6f. Initial experiments confirmed that Ca2+ responses evoked by a 5 ns pulse were similar between fluorescent Ca2+ indicator-loaded murine and bovine chromaffin cells, thereby establishing that 5 ns-elicited excitation of chromaffin cells occurs reproducibly across species. In GCaMP6f-expressing murine chromaffin cells, spontaneous Ca2+ activity as well as nicotinic receptor agonist- and 5 ns evoked-Ca2+ responses consistently displayed similar kinetic characteristics as those in dye-loaded cells but with two-twentyfold greater amplitudes and without photobleaching. The high signal-to-noise ratio of evoked Ca2+ responses as well as spontaneous Ca2+ activity was observed in cells derived from Sox10-Cre, conditional GCaMP6f mice or TH-Cre, conditional GCaMP6f mice, although the number of cells expressing GCaMP6f at sufficiently high levels for achieving high signal-to-noise ratios was greater in Sox10-Cre mice. As in bovine cells, Ca2+ responses elicited in murine GCaMP6f-expressing cells by a 5 ns pulse were mediated by the activation of voltage-gated Ca2+ channels but not tetrodotoxin-sensitive voltage-gated Na+ channels. We conclude that genetically targeting GCaMP6f expression to murine chromaffin cells represents a sensitive and valuable approach to investigate spontaneous, receptor agonist- and nanosecond electric pulse-induced Ca2+ responses in vitro. This approach will also facilitate future studies investigating the effects of ultrashort electric pulses on cells in ex vivo slices of adrenal gland, which will lay the foundation for using nanosecond electric pulses to stimulate neurosecretion in vivo. [ABSTRACT FROM AUTHOR]

Objective: Given the critical role of consonants in speech perception and the lack of knowledge on consonant perception in noise in Mandarin-speaking children, the current study aimed to investigate Mandarin consonant discrimination in normal-hearing children, in relation to the effects of age and signal-to-noise ratios (S/N). Design: A discrimination task consisting of 33 minimal pairs in monosyllabic words was designed to explore the development of consonant discrimination in five test conditions: 0, -5, -10, -15 dB S/Ns, and quiet. Study sample: Forty Mandarin-speaking, normal-hearing children aged from 4;0 to 8;9 in one-year-age increment were recruited and their performance was compared to 10 adult listeners. Results: A significant main effect of age, test conditions, and an interaction effect between these variables was noted. Consonant discrimination in quiet and in noise improved as children became older. Consonants that were difficult to discriminate in quiet and in noise were mainly velar contrasts. Noise seemed to have less effect on the discrimination of affricates and fricatives, and plosives appeared to be to be more difficult to discriminate in noise than in quiet. Place contrasts between alveolar and palato-alveolar consonants were difficult in quiet. Conclusions: The findings were the first to reveal typical perceptual development of Mandarin consonant discrimination in children and can serve as a reference for comparison with children with disordered perceptual development, such as those with hearing loss. [ABSTRACT FROM AUTHOR]

Problem: Low-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis. Aim: This study aims to create an automatic fundus macular image enhancement framework to improve low-quality fundus images and remove complex image degradation. Method: We propose a new deep learning-based model that automatically enhances low-quality retinal fundus images that suffer from complex degradation. We collected a dataset, comprising 1068 pairs of high-quality (HQ) and low-quality (LQ) fundus images from the Kangbuk Samsung Hospital's health screening program and ophthalmology department from 2017 to 2019. Then, we used these dataset to develop data augmentation methods to simulate major aspects of retinal image degradation and to propose a customized convolutional neural network (CNN) architecture to enhance LQ images, depending on the nature of the degradation. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), r-value (linear index of fuzziness), and proportion of ungradable fundus photographs before and after the enhancement process are calculated to assess the performance of proposed model. A comparative evaluation is conducted on an external database and four different open-source databases. Results: The results of the evaluation on the external test dataset showed an significant increase in PSNR and SSIM compared with the original LQ images. Moreover, PSNR and SSIM increased by over 4 dB and 0.04, respectively compared with the previous state-of-the-art methods (P < 0.05). The proportion of ungradable fundus photographs decreased from 42.6% to 26.4% (P = 0.012). Conclusion: Our enhancement process improves LQ fundus images that suffer from complex degradation significantly. Moreover our customized CNN achieved improved performance over the existing state-of-the-art methods. Overall, our framework can have a clinical impact on reducing re-examinations and improving the accuracy of diagnosis. [ABSTRACT FROM AUTHOR]

Resolution in a confocal laser scanning microscopes (CLSM) can be improved if the pinhole is closed. But closing the pinhole will deteriorate the signal to noise ratio (SNR). A simple technique to improve the SNR while keeping the resolution same by upgrading the system to an image scanning microscope. In this paper, we explain in detail, based on an Olympus Fluoview 300 system, how a scanning microscope can be upgraded into an image scanning microscope (ISM) using a simple camera-based detector and an Arduino Due providing a galvo driving and camera synchronization signals. We could confirm a resolution improvement as well as superconcentration and made the interesting observation of a reduced influence of laser fluctuations. [ABSTRACT FROM AUTHOR]

There is an increasing interest in the field of audiology and speech communication to measure the effort that it takes to listen in noisy environments, with obvious implications for populations suffering from hearing loss. Pupillometry offers one avenue to make progress in this enterprise but important methodological questions remain to be addressed before such tools can serve practical applications. Typically, cocktail-party situations may occur in less-than-ideal lighting conditions, e.g. a pub or a restaurant, and it is unclear how robust pupil dynamics are to luminance changes. In this study, we first used a well-known paradigm where sentences were presented at different signal-to-noise ratios (SNR), all conducive of good intelligibility. This enabled us to replicate findings, e.g. a larger and later peak pupil dilation (PPD) at adverse SNR, or when the sentences were misunderstood, and to investigate the dependency of the PPD on sentence duration. A second experiment reiterated two of the SNR levels, 0 and +14 dB, but measured at 0, 75, and 220 lux. The results showed that the impact of luminance on the SNR effect was non-monotonic (sub-optimal in darkness or in bright light), and as such, there is no trivial way to derive pupillary metrics that are robust to differences in background light, posing considerable constraints for applications of pupillometry in daily life. Our findings raise an under-examined but crucial issue when designing and understanding listening effort studies using pupillometry, and offer important insights to future clinical application of pupillometry across sites. [ABSTRACT FROM AUTHOR]

Human embryonic stem cells (hESC), derived from the blastocysts, provide unique cellular models for numerous potential applications. They have great promise in the treatment of diseases such as Parkinson's, Huntington's, diabetes mellitus, etc. hESC are a reliable developmental model for early embryonic growth because of their ability to divide indefinitely (pluripotency), and differentiate, or functionally change, into any adult cell type. Their adaptation to toxicological studies is particularly attractive as pluripotent stem cells can be used to model various stages of prenatal development. Automated detection and classification of human embryonic stem cell in videos is of great interest among biologists for quantified analysis of various states of hESC in experimental work. Currently video annotation is done by hand, a process which is very time consuming and exhaustive. To solve this problem, this paper introduces DeephESC 2.0 an automated machine learning approach consisting of two parts: (a) Generative Multi Adversarial Networks (GMAN) for generating synthetic images of hESC, (b) a hierarchical classification system consisting of Convolution Neural Networks (CNN) and Triplet CNNs to classify phase contrast hESC images into six different classes namely: Cell clusters, Debris, Unattached cells, Attached cells, Dynamically Blebbing cells and Apoptically Blebbing cells. The approach is totally non-invasive and does not require any chemical or staining of hESC. DeephESC 2.0 is able to classify hESC images with an accuracy of 93.23% out performing state-of-the-art approaches by at least 20%. Furthermore, DeephESC 2.0 is able to generate large number of synthetic images which can be used for augmenting the dataset. Experimental results show that training DeephESC 2.0 exclusively on a large amount of synthetic images helps to improve the performance of the classifier on original images from 93.23% to 94.46%. This paper also evaluates the quality of the generated synthetic images using the Structural SIMilarity (SSIM) index, Peak Signal to Noise ratio (PSNR) and statistical p-value metrics and compares them with state-of-the-art approaches for generating synthetic images. DeephESC 2.0 saves hundreds of hours of manual labor which would otherwise be spent on manually/semi-manually annotating more and more videos.

In recent years, time-frequency analysis (TFA) methods have received widespread attention and undergone rapid development. However, traditional TFA methods cannot achieve the desired effect when dealing with nonstationary signals. Therefore, this study proposes a new TFA method called the local maximum synchrosqueezing scaling-basis chirplet transform (LMSBCT), which is a further improvement of the scaling-basis chirplet transform (SBCT) with energy rearrangement in frequency and can be viewed as a good combination of SBCT and local maximum synchrosqueezing transform. A better concentration in terms of the time-frequency energy and a more accurate instantaneous frequency trajectory can be achieved using LMSBCT. The time-frequency distribution of strong frequency-modulated signals and multicomponent signals can be handled well, even for signals with close signal frequencies and low signal-to-noise ratios. Numerical simulations and real experiments were conducted to prove the superiority of the proposed method over traditional methods. [ABSTRACT FROM AUTHOR]

Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so that destructive interference occurs. As a proof of concept, we demonstrate the algorithm's performance by reducing electromyogram noise in electroencephalograms with the usage of a custom, flexible, 3D-printed, compound electrode. With this setup, an average of 4dB and a maximum of 10dB improvement of the signal-to-noise ratio of the EEG was achieved by removing wide band muscle noise. This concept has the potential to not only adaptively improve the signal-to-noise ratio of EEG but can be applied to a wide range of biological, industrial and consumer applications such as industrial sensing or noise cancelling headphones. [ABSTRACT FROM AUTHOR]

In the modern era, researchers have focused a great deal of effort on multimedia security and fast processing to address computational processing time difficulties. Due to limited battery capacity and storage, Unmanned Aerial Vehicles (UAVs) must use energy-efficient processing. In order to overcome the vulnerability of time inefficiency and provide an appropriate degree of security for digital images, this paper proposes a new encryption system based on the bit-plane extraction method, chaos theory, and Discrete Wavelet Transform (DWT). Using confusion and diffusion processes, chaos theory is used to modify image pixels. In contrast, bit-plane extraction and DWT are employed to reduce the processing time required for encryption. Multiple cyberattack analysis, including noise and cropping attacks, are performed by adding random noise to the ciphertext image in order to determine the proposed encryption scheme's resistance to such attacks. In addition, a variety of statistical security analyses, including entropy, contrast, energy, correlation, peak signal-to-noise ratio (PSNR), and mean square error (MSE), are performed to evaluate the security of the proposed encryption system. Moreover, a comparison is made between the statistical security analysis of the proposed encryption scheme and the existing work to demonstrate that the suggested encryption scheme is better to the existing ones. [ABSTRACT FROM AUTHOR]

Purpose: It is well known that speech uses both the auditory and visual modalities to convey information. In cases of congenital sensory deprivation, the feedback language learners have access to for mapping visible and invisible orofacial articulation is impoverished. Although the effects of blindness on the movements of the lips, jaw, and tongue have been documented in francophone adults, not much is known about their consequences for speech intelligibility. The objective of this study is to investigate the effects of congenital visual deprivation on vowel intelligibility in adult speakers of Canadian French. Method: Twenty adult listeners performed two perceptual identification tasks in which vowels produced by congenitally blind adults and sighted adults were used as stimuli. The vowels were presented in the auditory, visual, and audiovisual modalities (experiment 1) and at different signal-to-noise ratios in the audiovisual modality (experiment 2). Correct identification scores were calculated. Sequential information analyses were also conducted to assess the amount of information transmitted to the listeners along the three vowel features of height, place of articulation, and rounding. Results: The results showed that, although blind speakers did not differ from their sighted peers in the auditory modality, they had lower scores in the audiovisual and visual modalities. Some vowels produced by blind speakers are also less robust in noise than those produced by sighted speakers. Conclusion: Together, the results suggest that adult blind speakers have learned to adapt to their sensory loss so that they can successfully achieve intelligible vowel targets in non-noisy conditions but that they produce less intelligible speech in noisy conditions. Thus, the trade-off between visible (lips) and invisible (tongue) articulatory cues observed between vowels produced by blind and sighted speakers is not equivalent in terms of perceptual efficiency. [ABSTRACT FROM AUTHOR]

Introduction: One major challenge in PET radiomics is its sensitivity to noise. Low signal-to-noise ratio (SNR) affects not only the precision but also the accuracy of quantitative metrics extracted from the images resulting in noise-induced bias. This phantom study aims to identify the radiomic features that are robust to noise in terms of precision and accuracy and to explore some methods that might help to correct noise-induced bias. Methods: A phantom containing three 18F-FDG filled 3D printed inserts, reflecting heterogeneous tracer uptake and realistic tumor shapes, was used in the study. The three different phantom inserts were filled and scanned with three different tumor-to-background ratios, simulating a total of nine different tumors. From the 40-minute list-mode data, ten frames each for 5 s, 10 s, 30 s, and 120 s frame duration were reconstructed to generate images with different noise levels. Under these noise conditions, the precision and accuracy of the radiomic features were analyzed using intraclass correlation coefficient (ICC) and similarity distance metric (SDM) respectively. Based on the ICC and SDM values, the radiomic features were categorized into four groups: poor, moderate, good, and excellent precision and accuracy. A "difference image" created by subtracting two statistically equivalent replicate images was used to develop a model to correct the noise-induced bias. Several regression methods (e.g., linear, exponential, sigmoid, and power-law) were tested. The best fitting model was chosen based on Akaike information criteria. Results: Several radiomic features derived from low SNR images have high repeatability, with 68% of radiomic features having ICC ≥ 0.9 for images with a frame duration of 5 s. However, most features show a systematic bias that correlates with the increase in noise level. Out of 143 features with noise-induced bias, the SDM values were improved based on a regression model (53 features to excellent and 67 to good) indicating that the noise-induced bias of these features can be, at least partially, corrected. Conclusion: To have a predictive value, radiomic features should reflect tumor characteristics and be minimally affected by noise. The present study has shown that it is possible to correct for noise-induced bias, at least in a subset of the features, using a regression model based on the local image noise estimates. [ABSTRACT FROM AUTHOR]

Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms. [ABSTRACT FROM AUTHOR]

Compressed fluorescence lifetime imaging (Compressed-FLIM) is a novel Snapshot compressive imaging (SCI) method for single-shot widefield FLIM. This approach has the advantages of high temporal resolution and deep frame sequences, allowing for the analysis of FLIM signals that follow complex decay models. However, the precision of Compressed-FLIM is limited by reconstruction algorithms. To improve the reconstruction accuracy of Compressed-FLIM in dealing with large-scale FLIM problem, we developed a more effective combined prior model 3DTGp V_net, based on the Plug and Play (PnP) framework. Extensive numerical simulations indicate the proposed method eliminates reconstruction artifacts caused by the Deep denoiser networks. Moreover, it improves the reconstructed accuracy by around 4dB (peak signal-to-noise ratio; PSNR) over the state-of-the-art TV+FFDNet in test data sets. We conducted the single-shot FLIM experiment with different Rhodamine reagents and the results show that in practice, the proposed algorithm has promising reconstruction performance and more negligible lifetime bias. [ABSTRACT FROM AUTHOR]

Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images. [ABSTRACT FROM AUTHOR]

In urban and other complex environments, satellite navigation signals are vulnerable to multipath interference, especially medium- and short-delay multipath interference, which seriously affects the positioning accuracy. Aiming at the problem that the effect of traditional methods on medium and short multipath suppression is not obvious and it is difficult to accurately estimate the direction of arrival (DOA) when beamforming is used, a multipath suppression scheme based on a postcorrelation and independent component analysis (ICA) algorithm is proposed in this paper. First, the satellite navigation signal on each array element is despread and demodulated by postcorrelation technology to improve the signal-to-noise ratio (SNR), and then the direct signal component is directly extracted by the ICA algorithm to filter the noise while suppressing the multipath interference. Simulation results show that this method can effectively suppress medium and short multipath interference when the SNR of satellite navigation signals is as low as -30 dB, significantly improve the SNR of navigation messages and reduce the bit error rate, which provides a new solution for global navigation satellite system (GNSS) multipath suppression. [ABSTRACT FROM AUTHOR]

Objectives: The aim of the study was to find the lowest possible tube current and the optimal iterative reconstruction (IR) strength in abdominal imaging. Material and methods: Reconstruction software was used to insert noise, simulating the use of a lower tube current. A semi-anthropomorphic abdominal phantom (Quality Assurance in Radiology and Medicine, QSA-543, Moehrendorf, Germany) was used to validate the performance of the ReconCT software (S1 Appendix). Thirty abdominal CT scans performed with a standard protocol (120 kVref, 150 mAsref) scanned at 90 kV, with dedicated contrast media (CM) injection software were selected. There were no other in- or exclusion criteria. The software was used to insert noise as if the scans were performed with 90, 80, 70 and 60% of the full dose. Consequently, the different scans were reconstructed with filtered back projection (FBP) and IR strength 2, 3 and 4. Both objective (e.g. Hounsfield units [HU], signal to noise ratio [SNR] and contrast to noise ratio [CNR]) and subjective image quality were evaluated. In addition, lesion detection was graded by two radiologists in consensus in another 30 scans (identical scan protocol) with various liver lesions, reconstructed with IR 3, 4 and 5. Results: A tube current of 60% still led to diagnostic objective image quality (e.g. SNR and CNR) when IR strength 3 or 4 were used. IR strength 4 was preferred for lesion detection. The subjective image quality was rated highest for the scans performed at 90% with IR 4. Conclusion: A tube current reduction of 10–40% is possible in case IR 4 is used, leading to the highest image quality (10%) or still diagnostic image quality (40%), shown by a pairwise comparison in the same patients. [ABSTRACT FROM AUTHOR]

Fluorescent imaging with voltage- or calcium-sensitive dyes, known as optical mapping, is one of the indispensable modern techniques to study cardiac or neural electrophysiology, unsurpassed by temporal and spatial resolution. High-speed CMOS cameras capable of optical registration of action potential propagation are in general very costly. We present a complete solution priced below US$1,000 (including camera and lens) at the moment of publication with an open-source image acquisition and processing software. We demonstrate that the iDS UI-3130CP rev.2 camera we used in this study is capable of 200x200 977 frames per second (FPS) action potential recordings from rodent hearts, with the signal-to-noise-ratio of a conditioned signal of 16 ± 10. A comparison with a specialized MiCAM Ultimate-L camera has shown that signal-to-noise ratio (SNR) while lower is sufficient for accurate measurements of AP waveform, conduction velocity (± 0.04 m/s) and action potential duration (± 7ms) in mouse and rat hearts. We used 4-aminopyridine to prolong the action potential duration in mouse heart, thus demonstrating that the proposed system is adequate for pharmacological studies. [ABSTRACT FROM AUTHOR]

Inter-satellite optical wireless communication (Is-OWC) links can become promising solutions to realize the next-generation high-speed communication services. The operation of Global Navigation Satellite Systems can be improved with the use of Is-OWC links through ranging and communication services. However, the key challenge in Inter-satellite link (ISL) is its effective range which is limited due to pointing errors. In this work, we propose to develop a high-capacity and long-reach Is-OWC link by incorporating hybrid mode division multiplexing (MDM) and wavelength division multiplexing (WDM) schemes to transmit ten independent channels over 40000kms Is-OWC link. Each channel is capable of carrying 400Gbps data which is encoded by the dual polarization quadrature phase shift key technique with required signal to noise ratio (SNR) and received power. The proposed Is-OWC link satisfies the enhanced communication within Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) satellites. The proposed Is-OWC is further evaluated under the impact of space turbulences, particularly transmitter and receiver pointing errors. The result reported that the proposed Is-OWC link can transmit 4Tbps data over 16000kms with the transmitter pointing error of 2μrad and receiver pointing error of 1μrad. [ABSTRACT FROM AUTHOR]

Having a large receptive vocabulary benefits speech-in-noise recognition for young children, though this is not always the case for older children or adults. These observations could indicate that effects of receptive vocabulary size on speech-in-noise recognition differ depending on familiarity of the target words, with effects observed only for more recently acquired and less frequent words. Two experiments were conducted to evaluate effects of vocabulary size on open-set speech-in-noise recognition for adults with normal hearing. Targets were words acquired at 4, 9, 12 and 15 years of age, and they were presented at signal-to-noise ratios (SNRs) of -5 and -7 dB. Percent correct scores tended to fall with increasing age of acquisition (AoA), with the caveat that performance at -7 dB SNR was better for words acquired at 9 years of age than earlier- or later-acquired words. Similar results were obtained whether the AoA of the target words was blocked or mixed across trials. Differences in word duration appear to account for nonmonotonic effects of AoA. For all conditions, a positive correlation was observed between recognition and vocabulary size irrespective of target word AoA, indicating that effects of vocabulary size are not limited to recently acquired words. This dataset does not support differential assessment of AoA, lexical frequency, and other stimulus features known to affect lexical access. [ABSTRACT FROM AUTHOR]

The Perona-Malik (P-M) model exhibits deficiencies such as noise amplification, new noise introduction, and significant gradient effects when processing noisy images. To address these issues, this paper proposes an image-denoising algorithm, ACE-GPM, which integrates an Automatic Color Equalization (ACE) algorithm with a gradient-adjusted P-M model. Initially, the ACE algorithm is employed to enhance the contrast of low-light images obscured by fog and noise. Subsequently, the Otsu method, a technique to find the optimal threshold based on between-class variance, is applied for precise segmentation, enabling more accurate identification of different regions within the image. After that, distinct gradients enhance the image's foreground and background via an enhancement function that accentuates edge and detailed information. The denoising process is finalized by applying the gradient P-M model, employing a gradient descent approach to further emphasize image edges and details. Experimental evidence indicates that the proposed ACE-GPM algorithm not only elevates image contrast and eliminates noise more effectively than other denoising methods but also preserves image details and texture information, evidenced by an average increase of 0.42 in the information entropy value. Moreover, the proposed solution achieves these outcomes with reduced computational resource expenditures while maintaining high image quality., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: The relationship between electrode-nerve interface (ENI) estimates and inter-subject differences in speech performance with sequential and simultaneous channel stimulation in adult cochlear implant listeners were explored. We investigated the hypothesis that individuals with good ENIs would perform better with simultaneous compared to sequential channel stimulation speech processing strategies than those estimated to have poor ENIs. Methods: Fourteen postlingually deaf implanted cochlear implant users participated in the study. Speech understanding was assessed with a sentence test at signal-to-noise ratios that resulted in 50% performance for each user with the baseline strategy F120 Sequential. Two simultaneous stimulation strategies with either two (Paired) or three sets of virtual channels (Triplet) were tested at the same signal-to-noise ratio. ENI measures were estimated through: (I) voltage spread with electrical field imaging, (II) behavioral detection thresholds with focused stimulation, and (III) slope (IPG slope effect) and 50%-point differences (dB offset effect) of amplitude growth functions from electrically evoked compound action potentials with two interphase gaps. Results: A significant effect of strategy on speech understanding performance was found, with Triplets showing a trend towards worse speech understanding performance than sequential stimulation. Focused thresholds correlated positively with the difference required to reach most comfortable level (MCL) between Sequential and Triplet strategies, an indirect measure of channel interaction. A significant offset effect (difference in dB between 50%-point for higher eCAP growth function slopes with two IPGs) was observed. No significant correlation was observed between the slopes for the two IPGs tested. None of the measures used in this study correlated with the differences in speech understanding scores between strategies. Conclusions: The ENI measure based on behavioral focused thresholds could explain some of the difference in MCLs, but none of the ENI measures could explain the decrease in speech understanding with increasing pairs of simultaneously stimulated electrodes in processing strategies. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are regarded as future transport mechanisms that drive the vehicles without the need of drivers. The photonic-based radar technology is a promising candidate for delivering attractive applications to autonomous vehicles such as self-parking assistance, navigation, recognition of traffic environment, etc. Alternatively, microwave radars are not able to meet the demand of next-generation autonomous vehicles due to its limited bandwidth availability. Moreover, the performance of microwave radars is limited by atmospheric fluctuation which causes severe attenuation at higher frequencies. In this work, we have developed coherent-based frequency-modulated photonic radar to detect target locations with longer distance. Furthermore, the performance of the proposed photonic radar is investigated under the impact of various atmospheric weather conditions, particularly fog and rain. The reported results show the achievement of significant signal to noise ratio (SNR) and received power of reflected echoes from the target for the proposed photonic radar under the influence of bad weather conditions. Moreover, a conventional radar is designed to establish the effectiveness of the proposed photonic radar by considering similar parameters such as frequency and sweep time. [ABSTRACT FROM AUTHOR]

In cochlear implant (CI) users, measurements of electrically evoked compound action potentials (ECAPs) prove the functionality of the neuron-electrode interface. Objective measures, e.g., the ECAP threshold, may serve as a basis for the clinical adjustment of the device for the optimal benefit of the CI user. As for many neural responses, the threshold determination often is based on the subjective assessment of the clinical specialist, whose decision-making process could be aided by autonomous computational algorithms. To that end, we extended the signal-to-noise ratio (SNR) approach for ECAP threshold determination to be applicable for FineGrain (FG) ECAP responses. The new approach takes advantage of two features: the FG stimulation paradigm with its enhanced resolution of recordings, and SNR-based ECAP threshold determination, which allows defining thresholds independently of morphology and with comparably low computational power. Pearson's correlation coefficient r between the ECAP threshold determined by five experienced evaluators and the threshold determined with the FG-SNR algorithm was in the range of r = 0.78–0.93. Between evaluators, r was in a comparable range of 0.84–0.93. A subset of the parameters of the algorithm was varied to identify the parameters with the highest potential to improve the FG-SNR formalism in the future. The two steps with the strongest influence on the agreement between the threshold estimate of the evaluators and the algorithm were the removal of undesired frequency components (denoising of the response traces) and the exact determination of the two time windows (signal and noise and noise only)."The parameters were linked to the properties of an ECAP response, indicating how to adjust the algorithm for the automatic detection of other neurophysiological responses. [ABSTRACT FROM AUTHOR]

Objective: To test the hypothesis that caffeine can influence tinnitus, we recruited 80 patients with chronic tinnitus and randomly allocated them into two groups (caffeine and placebo) to analyze the self-perception of tinnitus symptoms after caffeine consumption, assuming that this is an adequate sample for generalization. Methods: The participants were randomized into two groups: one group was administered a 300-mg capsule of caffeine, and the other group was given a placebo capsule (cornstarch). A diet that restricted caffeine consumption for 24 hours was implemented. The participants answered questionnaires (the Tinnitus Handicap Inventory—THI, the Visual Analog Scale—VAS, the profile of mood state—POMS) and underwent examinations (tonal and high frequency audiometry, acufenometry (frequency measure; intensity measure and the minimum level of tinnitus masking), transient otoacoustic emissions—TEOAE and distortion product otoacoustic emissions—DPOAE assessments) at two timepoints: at baseline and after capsule ingestion. Results: There was a significant change in mood (measured by the POMS) after caffeine consumption. The THI and VAS scores were improved at the second timepoint in both groups. The audiometry assessment showed a significant difference in some frequencies between baseline and follow-up measurements in both groups, but these differences were not clinically relevant. Similar findings were observed for the amplitude and signal-to-noise ratio in the TEOAE and DPOAE measurements. Conclusions: Caffeine (300 mg) did not significantly alter the psychoacoustic measures, electroacoustic measures or the tinnitus-related degree of discomfort. [ABSTRACT FROM AUTHOR]

Hearing aids (HAs) only partially restore the ability of older hearing impaired (OHI) listeners to understand speech in noise, due in large part to persistent deficits in consonant identification. Here, we investigated whether adaptive perceptual training would improve consonant-identification in noise in sixteen aided OHI listeners who underwent 40 hours of computer-based training in their homes. Listeners identified 20 onset and 20 coda consonants in 9,600 consonant-vowel-consonant (CVC) syllables containing different vowels (/ɑ/, /i/, or /u/) and spoken by four different talkers. Consonants were presented at three consonant-specific signal-to-noise ratios (SNRs) spanning a 12 dB range. Noise levels were adjusted over training sessions based on d' measures. Listeners were tested before and after training to measure (1) changes in consonant-identification thresholds using syllables spoken by familiar and unfamiliar talkers, and (2) sentence reception thresholds (SeRTs) using two different sentence tests. Consonant-identification thresholds improved gradually during training. Laboratory tests of d' thresholds showed an average improvement of 9.1 dB, with 94% of listeners showing statistically significant training benefit. Training normalized consonant confusions and improved the thresholds of some consonants into the normal range. Benefits were equivalent for onset and coda consonants, syllables containing different vowels, and syllables presented at different SNRs. Greater training benefits were found for hard-to-identify consonants and for consonants spoken by familiar than unfamiliar talkers. SeRTs, tested with simple sentences, showed less elevation than consonant-identification thresholds prior to training and failed to show significant training benefit, although SeRT improvements did correlate with improvements in consonant thresholds. We argue that the lack of SeRT improvement reflects the dominant role of top-down semantic processing in processing simple sentences and that greater transfer of benefit would be evident in the comprehension of more unpredictable speech material.

The most common complaint of older hearing impaired (OHI) listeners is difficulty understanding speech in the presence of noise. However, tests of consonant-identification and sentence reception threshold (SeRT) provide different perspectives on the magnitude of impairment. Here we quantified speech perception difficulties in 24 OHI listeners in unaided and aided conditions by analyzing (1) consonant-identification thresholds and consonant confusions for 20 onset and 20 coda consonants in consonant-vowel-consonant (CVC) syllables presented at consonant-specific signal-to-noise (SNR) levels, and (2) SeRTs obtained with the Quick Speech in Noise Test (QSIN) and the Hearing in Noise Test (HINT). Compared to older normal hearing (ONH) listeners, nearly all unaided OHI listeners showed abnormal consonant-identification thresholds, abnormal consonant confusions, and reduced psychometric function slopes. Average elevations in consonant-identification thresholds exceeded 35 dB, correlated strongly with impairments in mid-frequency hearing, and were greater for hard-to-identify consonants. Advanced digital hearing aids (HAs) improved average consonant-identification thresholds by more than 17 dB, with significant HA benefit seen in 83% of OHI listeners. HAs partially normalized consonant-identification thresholds, reduced abnormal consonant confusions, and increased the slope of psychometric functions. Unaided OHI listeners showed much smaller elevations in SeRTs (mean 6.9 dB) than in consonant-identification thresholds and SeRTs in unaided listening conditions correlated strongly (r = 0.91) with identification thresholds of easily identified consonants. HAs produced minimal SeRT benefit (2.0 dB), with only 38% of OHI listeners showing significant improvement. HA benefit on SeRTs was accurately predicted (r = 0.86) by HA benefit on easily identified consonants. Consonant-identification tests can accurately predict sentence processing deficits and HA benefit in OHI listeners.

Magnetic resonance imaging radio frequency arrays are composed of multiple receive coils that have their signals combined to form an image. Combination requires an estimate of the radio frequency coil sensitivities to align signal phases and prevent destructive interference. At lower fields this can be accomplished using a uniform physical reference coil. However, at higher fields, uniform volume coils are lacking and, when available, suffer from regions of low receive sensitivity that result in poor sensitivity estimation and combination. Several approaches exist that do not require a physical reference coil but require manual intervention, specific prescans, or must be completed post-acquisition. This makes these methods impractical for large multi-volume datasets such as those collected for novel types of functional MRI or quantitative susceptibility mapping, where magnitude and phase are important. This pilot study proposes a fitted SVD method which utilizes existing combination methods to create a phase sensitive combination method targeted at large multi-volume datasets. This method uses any multi-image prescan to calculate the relative receive sensitivities using voxel-wise singular value decomposition. These relative sensitivities are fitted to the solid harmonics using an iterative least squares fitting algorithm. Fits of the relative sensitivities are used to align the phases of the receive coils and improve combination in subsequent acquisitions during the imaging session. This method is compared against existing approaches in the human brain at 7 Tesla by examining the combined data for the presence of singularities and changes in phase signal-to-noise ratio. Two additional applications of the method are also explored, using the fitted SVD method in an asymmetrical coil and in a case with subject motion. The fitted SVD method produces singularity-free images and recovers between 95–100% of the phase signal-to-noise ratio depending on the prescan data resolution. Using solid harmonic fitting to interpolate singular value decomposition derived receive sensitivities from existing prescans allows the fitted SVD method to be used on all acquisitions within a session without increasing exam duration. Our fitted SVD method is able to combine imaging datasets accurately without supervision during online reconstruction. [ABSTRACT FROM AUTHOR]

Those experiencing hearing loss face severe challenges in perceiving speech in noisy situations such as a busy restaurant or cafe. There are many factors contributing to this deficit including decreased audibility, reduced frequency resolution, and decline in temporal synchrony across the auditory system. Some hearing assistive devices implement beamforming in which multiple microphones are used in combination to attenuate surrounding noise while the target speaker is left unattenuated. In increasingly challenging auditory environments, more complex beamforming algorithms are required, which increases the processing time needed to provide a useful signal-to-noise ratio of the target speech. This study investigated whether the benefits from signal enhancement from beamforming are outweighed by the negative impacts on perception from an increase in latency between the direct acoustic signal and the digitally enhanced signal. The hypothesis for this study is that an increase in latency between the two identical speech signals would decrease intelligibility of the speech signal. Using 3 gain / latency pairs from a beamforming simulation previously completed in lab, perceptual thresholds of SNR from a simulated use case were obtained from normal hearing participants. No significant differences were detected between the 3 conditions. When presented with 2 copies of the same speech signal presented at varying gain / latency pairs in a noisy environment, any negative intelligibility effects from latency are masked by the noise. These results allow for more lenient restrictions for limiting processing delays in hearing assistive devices. [ABSTRACT FROM AUTHOR]

In the realm of digital image applications, image processing technology occupies a pivotal position, with image segmentation serving as a foundational component. As the digital image application domain expands across industries, the conventional segmentation techniques increasingly challenge to cater to modern demands. To address this gap, this paper introduces an MCMC-based image segmentation algorithm based on the Markov Random Field (MRF) model, marking a significant stride in the field. The novelty of this research lies in its method that capitalizes on domain information in pixel space, amplifying the local segmentation precision of image segmentation algorithms. Further innovation is manifested in the development of an adaptive segmentation image denoising algorithm based on MCMC sampling. This algorithm not only elevates image segmentation outcomes, but also proficiently denoises the image. In the experimental results, MRF-MCMC achieves better segmentation performance, with an average segmentation accuracy of 94.26% in Lena images, significantly superior to other common image segmentation algorithms. In addition, the study proposes that the denoising model outperforms other algorithms in peak signal-to-noise ratio and structural similarity in environments with noise standard deviations of 15, 25, and 50. In essence, these experimental findings affirm the efficacy of this study, opening avenues for refining digital image segmentation methodologies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang, Ma. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

ObjectivesTo validate the feasibility of labeling Clostridium novyi-NT (C.novyi-NT) anaerobes with iron-oxide nanoparticles for magnetic resonance imaging (MRI) and demonstrate the potential to use MRI to visualize intra-tumoral delivery of these iron-oxide labeled C.novyi-NT during percutaneous injection procedures.Materials and methodsAll studies were approved by IACUC. C.novyi-NT were labeled with hybrid iron-oxide Texas red nanoparticles. Growth of labeled and control samples were evaluated with optical density. Labeling was confirmed with confocal fluorescence and transmission electron microscopy (TEM). MRI were performed using a 7 Tesla scanner with T2*-weighted (T2*W) sequence. Contrast-to-noise ratio (CNR) measurements were performed for phantoms and signal-to-noise ratio (SNR) measurements performed in C57BL/6 mice (n = 12) with Panc02 xenografts before and after percutaneous injection of iron-oxide labeled C.novyi-NT. MRI was repeated 3 and 7 days post-injection. Hematoxylin-eosin (HE), Prussian blue and Gram staining of tumor specimens were performed for confirmation of intra-tumoral delivery.ResultsIron-oxide labeling had no influence upon C.novyi-NT growth. The signal intensity (SI) within T2*W images was significantly decreased for iron-oxide labeled C.novyi-NT phantoms compared to unlabeled controls. Under confocal fluorescence microscopy, the iron-oxide labeled C.novyi-NT exhibited a uniform red fluorescence consistent with observed regions of DAPI staining and overall labeling efficiency was 100% (all DAPI stained C.novyi-NT exhibited red fluorescence). Within TEM images, a large number iron granules were observed within the iron-oxide labeled C.novyi-NT; these were not observed within unlabeled controls. Intra-procedural MRI measurements permitted in vivo visualization of the intra-tumoral distribution of iron-oxide labeled C.novyi-NT following percutaneous injection (depicted as punctate regions of SI reductions within T2*-weighted images); tumor SNR decreased significantly following intra-tumoral injection of C.novyi-NT (p