Proteins containing polyglutamine (polyQ) regions are found in almost all eukaryotes, albeit with various frequencies. In humans, proteins such as huntingtin (Htt) with abnormally expanded polyQ regions cause neurodegenerative diseases such as Huntington’s disease (HD). To study how the presence of endogenous polyQ aggregation modulates polyQ aggregation and toxicity, we expressed polyQ expanded Htt fragments (polyQ Htt) in Schizosaccharomyces pombe. In stark contrast to other unicellular fungi, such as Saccharomyces cerevisiae, S. pombe is uniquely devoid of proteins with more than 10 Q repeats. We found that polyQ Htt forms aggregates within S. pombe cells only with exceedingly long polyQ expansions. Surprisingly, despite the presence of polyQ Htt aggregates in both the cytoplasm and nucleus, no significant growth defect was observed in S. pombe cells. Further, PCR analysis showed that the repetitive polyQ-encoding DNA region remained constant following transformation and after multiple divisions in S. pombe, in contrast to the genetic instability of polyQ DNA sequences in other organisms. These results demonstrate that cells with a low content of polyQ or other aggregation-prone proteins can show a striking resilience with respect to polyQ toxicity and that genetic instability of repetitive DNA sequences may have played an important role in the evolutionary emergence and exclusion of polyQ expansion proteins in different organisms.

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We developed and tested a novel template matching approach for signal quality assessment on electrocardiogram (ECG) data. A computational method was developed that uses a sinusoidal approximation to the QRS complex to generate a correlation value at every point of an ECG. The strength of this correlation can be numerically adapted into a ‘score’ for each segment of an ECG, which can be used to stratify signal quality. The algorithm was tested on lead II ECGs of intensive care unit (ICU) patients admitted to the Mount Sinai Hospital (MSH) from January to July 2020 and on records from the MIT BIH arrhythmia database. The algorithm was found to be 98.9% specific and 99% sensitive on test data from the MSH ICU patients. The routine performs in linear O(n) time and occupies O(1) heap space in runtime. This approach can be used to lower the burden of pre-processing in ECG signal analysis. Given its runtime (O(n)) and memory (O(1)) complexity, there are potential applications for signal quality stratification and arrhythmia detection in wearable devices or smartphones.

Continuous electrocardiogram (ECG) recordings, which measure the electrical activity of the heart, are increasingly used to predict clinical outcomes via machine learning and artificial intelligence. [1-3] Raw ECG data can have a variety of interference artifacts which, if unattended to, can obscure or invalidate the results of machine learning algorithms. Current ECG de-noising routines focus primarily on how to purify ECG signals, but not on how to classify and excise regions of unusable voltage data. [4] Here we demonstrate a new method to quantitatively assess the quality of an ECG. We found that approximating a Fourier series to the QRS complex and cross-correlating this kernel sequentially with two-second intervals of the lead II ECG yields a metric that can be used to distinguish sections of data as low-or high-quality signal. The algorithm was developed on independently annotated ECG data, subdivided into two-second intervals (“epochs”), from ten patients admitted to the Mount Sinai Hospital. It was found to be 99% sensitive and 98.9% specific in classifying epochs into either noise or signal in 1,000 test epochs. This algorithm can be used to accelerate machine learning modeling that uses ECG data and lower the burden of pre-processing in research. Given its memory and run-time efficiency (demonstrably O(n)), this routine can be an effective starting point for real-time and continuous evaluation of ECG data using machine learning.