Distortion product otoacoustic emission (DPOAE) levels plotted as a function of stimulus frequency ratio demonstrate a bandpass shape. This bandpass shape is narrower at higher frequencies compared to lower frequencies and thus has been thought to be related to cochlear mechanical tuning. However, the frequency- and level-dependence of these functions above 8 kHz is largely unknown. Furthermore, how tuning estimates from these functions are related to behavioural tuning is not fully understood. From experiment 1, we report DPOAE level ratio functions (LRF) from seven normal-hearing, young-adults for f = 0.75-16 kHz and two stimulus levels of 62/52 and 52/37 dB FPL. We found that LRFs became narrower as a function of increasing frequency and decreasing level. Tuning estimates from these functions increased as expected from 1-8 kHz. In experiment 2, we compared tuning estimates from DPOAE LRF to behavioural tuning in 24 normal-hearing, young adults for 1 and 4 kHz and found that behavioural tuning generally predicted DPOAE LRF estimated tuning. Our findings suggest that DPOAE LRFs generally reflect the tuning profile consistent with basilar membrane, neural, and behavioural tuning. However, further investigations are warranted to fully determine the use of DPOAE LRF as a clinical measure of cochlear tuning.
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http://dx.doi.org/10.1080/14992027.2021.1886352 | DOI Listing |
Med Phys
January 2025
Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey.
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View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Mechanical Engineering, Thuyloi University, Hanoi, Vietnam.
Road surface roughness is the cause of vehicle vibration, which is considered a system disturbance. Previous studies on suspension system control often ignore the influence of disturbances while designing the controller, leading to system performance degradation under severe vibration conditions. In this work, we propose a control method to improve active suspension performance that reduces vehicle vibration by eliminating the influence of road disturbances.
View Article and Find Full Text PDFPLoS Biol
January 2025
Humanities and Social Sciences, California Institute of Technology, Pasadena, California, United States of America.
Pivotal to self-preservation is the ability to identify when we are safe and when we are in danger. Previous studies have focused on safety estimations based on the features of external threats and do not consider how the brain integrates other key factors, including estimates about our ability to protect ourselves. Here, we examine the neural systems underlying the online dynamic encoding of safety.
View Article and Find Full Text PDFDespite rapid advances in genomic sequencing, most rare genetic variants remain insufficiently characterized for clinical use, limiting the potential of personalized medicine. When classifying whether a variant is pathogenic, clinical labs adhere to diagnostic guidelines that comprehensively evaluate many forms of evidence including case data, computational predictions, and functional screening. While a substantial amount of clinical evidence has been developed for these variants, the majority cannot be definitively classified as 'pathogenic' or 'benign', and thus persist as 'Variants of Uncertain Significance' (VUS).
View Article and Find Full Text PDFJAMIA Open
February 2025
Department of Medicine, University of Wisconsin-Madison, Madison, WI 53792, United States.
Objective: To evaluate large language models (LLMs) for pre-test diagnostic probability estimation and compare their uncertainty estimation performance with a traditional machine learning classifier.
Materials And Methods: We assessed 2 instruction-tuned LLMs, Mistral-7B-Instruct and Llama3-70B-chat-hf, on predicting binary outcomes for Sepsis, Arrhythmia, and Congestive Heart Failure (CHF) using electronic health record (EHR) data from 660 patients. Three uncertainty estimation methods-Verbalized Confidence, Token Logits, and LLM Embedding+XGB-were compared against an eXtreme Gradient Boosting (XGB) classifier trained on raw EHR data.
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