Genome-wide association studies (GWAS) have become a popular method for analyzing sets of DNA sequences in order to discover the genetic basis of disease. Unfortunately, statistics published as the result of GWAS can be used to identify individuals participating in the study. To prevent privacy breaches, even previously published results have been removed from public databases, impeding researchers’ access to the data and hindering collaborative research. Existing techniques for privacy-preserving GWAS focus on answering specific questions, such as correlations between a given pair of SNPs (DNA sequence variations). This does not fit the typical GWAS process, where the analyst may not know in advance which SNPs to consider and which statistical tests to use, how many SNPs are significant for a given dataset, etc. We present a set of practical, privacy-preserving data mining algorithms for GWAS datasets. Our framework supports exploratory data analysis, where the analyst does not know a priori how many and which SNPs to consider. We develop privacy-preserving algorithms for computing the number and location of SNPs that are significantly associated with the disease, the significance of any statistical test between a given SNP and the disease, any measure of correlation between SNPs, and the block structure of correlations. We evaluate our algorithms on real-world datasets and demonstrate that they produce significantly more accurate results than prior techniques while guaranteeing differential privacy.

Aging affects voice production and is associated with reduced communicative ability and quality of life. Voice therapy is a critical component of treatment, but its effects on neuromuscular mechanisms are unknown. The ultrasonic vocalizations (USVs) of rats can be used to test the effects of aging and voice use on the laryngeal neuromuscular system. This study tested the hypothesis that age-related changes in the USVs of rats and laryngeal neuromuscular junctions can be reversed through vocal exercise. Young and old rats were trained for 8 weeks to increase their USVs and were compared with a no intervention group pre- and post-treatment. USV acoustics and aspects of neuromuscular junction (NMJ) morphology were measured in the thyroarytenoid muscle. Vocal training reduced or eliminated some age differences found in both USVs and NMJs. We conclude that vocal exercise may assist in mitigating age-related changes in voice characteristics and underlying neuromuscular adaptations.

Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USa, we have created BaGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. the BaGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods.

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Onion routing is a scheme for anonymous communication that is designed for practical use. It has not been modeled formally, however, and therefore its anonymity guarantees have not been rigorously analyzed. We give an IO-automata model of an onion-routing protocol and, under possibilistic definitions, characterize the situations in which anonymity and unlinkability are guaranteed.