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Spatial cues can facilitate segregation of target speech from maskers. However, in clinical practice, masked speech understanding is most often evaluated using co-located speech and maskers (i.e., without spatial cues). Many hearing aid centers in France are equipped with five-loudspeaker arrays, allowing masked speech understanding to be measured with spatial cues. It is unclear how hearing status may affect utilization of spatial cues to segregate speech and noise. In this study, speech reception thresholds (SRTs) for target speech in "diffuse noise" (target speech from 1 speaker, noise from the remaining 4 speakers) in 297 adult listeners across 9 Audilab hearing centers. Participants were categorized according to pure-tone-average (PTA) thresholds: typically-hearing (TH; ≤ 20 dB HL), mild hearing loss (Mild; >20 ≤ 40 dB HL), moderate hearing loss 1 (Mod-1; >40 ≤ 55 dB HL), and moderate hearing loss 2 (Mod-2; >55 ≤ 65 dB HL). All participants were tested without aided hearing. SRTs in diffuse noise were significantly correlated with PTA thresholds, age at testing, as well as word and phoneme recognition scores in quiet. Stepwise linear regression analysis showed that SRTs in diffuse noise were significantly predicted by a combination of PTA threshold and word recognition scores in quiet. SRTs were also measured in co-located and diffuse noise in 65 additional participants. SRTs were significantly lower in diffuse noise than in co-located noise only for the TH and Mild groups; masking release with diffuse noise (relative to co-located noise) was significant only for the TH group. The results are consistent with previous studies that found that hard of hearing listeners have greater difficulty using spatial cues to segregate competing speech. The data suggest that speech understanding in diffuse noise provides additional insight into difficulties that hard of hearing individuals experience in complex listening environments.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9473430 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0274435 | PLOS |
IEEE Trans Biomed Eng
September 2025
Objective: Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular resolution due to inherent limitations in imaging hardware and system noise, adversely affecting the accurate estimation of microstructural parameters with fine anatomical details. Deep learning-based super-resolution techniques have shown promise in enhancing dMRI resolution without increasing acquisition time. However, most existing methods are confined to either spatial or angular super-resolution, disrupting the information exchange between the two domains and limiting their effectiveness in capturing detailed microstructural features.
View Article and Find Full Text PDFJ Vis Exp
August 2025
School of Cyberspace Security, Zhengzhou University.
In the context of the rapid development of large language models (LLMs), contrastive learning has become widely adopted due to its ability to bypass costly data annotation by leveraging vast amounts of network data for model training. However, this widespread use raises significant concerns regarding data privacy protection. Unlearnable Examples (UEs), a technique that disrupts model learning by perturbing data, effectively prevents unauthorized models from misusing sensitive data.
View Article and Find Full Text PDFMagn Reson Med
September 2025
Department of Mechanical Science and Bioengineering, The University of Osaka Graduate School of Engineering Science, Osaka, Japan.
Purpose: Diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) imaging are well-established approaches for evaluating cerebrospinal fluid (CSF) flow in subarachnoid and perivascular spaces, and have recently been applied to study ventricular CSF flow. However, DWI does not directly measure flow velocity, and the physical implications of DWI measurements are unclear. This study aimed to provide a theoretical interpretation of the DWI and IVIM imaging of CSF flow velocity fields.
View Article and Find Full Text PDFJ Comput Aided Mol Des
September 2025
College of Information Engineering, Beijing Institute of Petrochemical Technology, No. 19 Qingyuan North Road, Daxing District, Beijing, 102617, China.
With the rapid advancement of biotechnology, protein generation and design based on generative models have demonstrated extensive applications in drug development, vaccine research, and biocatalysis. This research proposes a protein generation method based on the generalized diffusion model, which breaks through the traditional diffusion model's reliance on Gaussian noise, enables more flexible protein sequence generation, and preliminarily verifies its advantages. Specifically, protein sequences were first encoded using one-hot encoding and input into the diffusion model to generate novel sequences.
View Article and Find Full Text PDFRadiologie (Heidelb)
September 2025
Institut für Diagnostische und Interventionelle Radiologie, Universitätsspital Zürich, Rämistrasse 100, 8091, Zürich, Schweiz.
Background: Pulmonary manifestations of systemic diseases represent a complex and diagnostically challenging field. While pulmonary involvement in immunological and hematological disorders is well described, lung involvement in rare genetic and congenital systemic diseases-such as systemic storage diseases, neuromuscular disorders, and phacomatoses-is only gradually gaining increased attention in radiological diagnostics.
Results: The pulmonary component often manifests secondarily, frequently after years of progression and worsening of the underlying disease.