Fast, Continuous Audiogram Estimation Using Machine Learning.

OBJECTIVES: Pure-tone audiometry has been a staple of hearing assessments for decades. Many different procedures have been proposed for measuring thresholds with pure tones by systematically manipulating intensity one frequency at a time until a discrete threshold function is determined. The authors have developed a novel nonparametric approach for estimating a continuous threshold audiogram using Bayesian estimation and machine learning classification. The objective of this study was to assess the accuracy and reliability of this new method relative to a commonly used threshold measurement technique. DESIGN: The authors performed air conduction pure-tone audiometry on 21 participants between the ages of 18 and 90 years with varying degrees of hearing ability. Two repetitions of automated machine learning audiogram estimation and one repetition of conventional modified Hughson-Westlake ascending-descending audiogram estimation were acquired by an audiologist. The estimated hearing thresholds of these two techniques were compared at standard audiogram frequencies (i.e., 0.25, 0.5, 1, 2, 4, 8 kHz). RESULTS: The two threshold estimate methods delivered very similar estimates at standard audiogram frequencies. Specifically, the mean absolute difference between estimates was 4.16 ± 3.76 dB HL. The mean absolute difference between repeated measurements of the new machine learning procedure was 4.51 ± 4.45 dB HL. These values compare favorably with those of other threshold audiogram estimation procedures. Furthermore, the machine learning method generated threshold estimates from significantly fewer samples than the modified Hughson-Westlake procedure while returning a continuous threshold estimate as a function of frequency. CONCLUSIONS: The new machine learning audiogram estimation technique produces continuous threshold audiogram estimates accurately, reliably, and efficiently, making it a strong candidate for widespread application in clinical and research audiometry.

Continuing analysis of microRNA origins: Formation from transposable element insertions and noncoding RNA mutations.

MicroRNAs (miRs) are small noncoding RNAs that typically act as regulators of gene expression by base pairing with the 3' UTR of messenger RNAs (mRNAs) and either repressing their translation or initiating degradation. As of this writing over 24,500 distinct miRs have been identified, but the functions of the vast majority of these remain undescribed. This paper represents a summary of our in depth analysis of the genomic origins of miR loci, detailing the formation of 1,213 of the 7,321 recently identified miRs and thereby bringing the total number of miR loci with defined molecular origin to 3,605. Interestingly, our analyses also identify evidence for a second, novel mechanism of miR locus generation through describing the formation of 273 miR loci from mutations to other forms of noncoding RNAs. Importantly, several independent investigations of the genomic origins of miR loci have now supported the hypothesis that miR hairpins are formed by the adjacent genomic insertion of two complementary transposable elements (TEs) into opposing strands. While our results agree that subsequent transcription over such TE interfaces leads to the formation of the majority of functional miR loci, we now also find evidence suggesting that a subset of miR loci were actually formed by an alternative mechanism-point mutations in other structurally complex, noncoding RNAs (e.g., tRNAs and snoRNAs).