It is recommended that oscillometric devices be calibrated by auscultation when first used, but this is difficult in practice. Here, we introduce a smartphone‐based technique to verify the accuracy of blood pressure monitors (BPMs). We enrolled 99 consecutive subjects and tested 6 brands of BPMs in this study. During measurements of electronic oscillometric BPMs, Korotkoff sounds were simultaneously collected using a stethoscope head beneath a cuff connected to a smartphone, and an app named Accutension Stetho could then yield an auscultatory BP reading as a reference. Next, differences in BP between the different BPMs and Accutension Stetho were determined. The percentage of BP differences falling within 5, 10, and 15 mm Hg; the mean (MD) value; and the standard deviation (SD) of BP differences and deflation errors were analyzed among all the BPMs. We found that the percentages of SBP differences falling within 5 mm Hg of the 6 BPMs were 80%, 79%, 77%, 72%, 68%, and 63%, in turn. The deflation rates among the 6 BPMs were 2.23, 3.48, 6.10, 2.44, 3.66, and 4.85 mm Hg/beat, respectively. Deflation errors, which were defined as deflation prior to the end of the Korotkoff sounds, existed in 4 BPMs. In conclusion, Accutension Stetho could detect BP differences between oscillometric BPM readings and simultaneous auscultatory readings. Diastolic BP was overestimated when the device deflated prior to the end of the Korotkoff sounds. Using the app, it is possible to evaluate the accuracy of BPMs among the same subjects.

Although poorly positioned nucleosomes are ubiquitous in the eukaryotic genome, they are difficult to identify with existing nucleosome identification methods. Recently available enhanced high-throughput chromatin conformation capture techniques such as Micro-C, DNase Hi-C, and Hi-CO characterize nucleosome-level chromatin proximity, probing the positions of mono-nucleosomes and the spacing between nucleosome pairs at the same time, enabling nucleosome profiling in poorly positioned regions. Here we develop a novel computational approach, NucleoMap, to identify nucleosome positioning from ultra-high resolution chromatin contact maps. By integrating nucleosome read density, contact distances, and binding preferences, NucleoMap precisely locates nucleosomes in both prokaryotic and eukaryotic genomes and outperforms existing nucleosome identification methods in both precision and recall. We rigorously characterize genome-wide association in eukaryotes between the spatial organization of mono-nucleosomes and their corresponding histone modifications, protein binding activities, and higher-order chromatin functions. We also find evidence of two tetra-nucleosome folding structures in human embryonic stem cells and analyze their association with multiple structural and functional regions. Based on the identified nucleosomes, nucleosome contact maps are constructed, reflecting the inter-nucleosome distances and preserving the contact distance profiles in original contact maps.

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