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In this article, we report on the heat-transfer resistance at interfaces as a novel, denaturation-based method to detect single-nucleotide polymorphisms in DNA. We observed that a molecular brush of double-stranded DNA grafted onto synthetic diamond surfaces does not notably affect the heat-transfer resistance at the solid-to-liquid interface. In contrast to this, molecular brushes of single-stranded DNA cause, surprisingly, a substantially higher heat-transfer resistance and behave like a thermally insulating layer. This effect can be utilized to identify ds-DNA melting temperatures via the switching from low- to high heat-transfer resistance. The melting temperatures identified with this method for different DNA duplexes (29 base pairs without and with built-in mutations) correlate nicely with data calculated by modeling. The method is fast, label-free (without the need for fluorescent or radioactive markers), allows for repetitive measurements, and can also be extended toward array formats. Reference measurements by confocal fluorescence microscopy and impedance spectroscopy confirm that the switching of heat-transfer resistance upon denaturation is indeed related to the thermal on-chip denaturation of DNA.
Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.