Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.

Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without a priori assigned parameters. The B-Net is evaluated on simulated data sets and further applied to experimental data of DNA and protein translocation. The B-Net results are characterized by small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to 1, an impossibility for threshold-based algorithms. The B-Net presents a generic architecture applicable to pulse-like signals beyond nanopore currents.

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Congenital cytomegalovirus (CMV) infection is the most common infectious cause of sensorineural hearing loss in children. While the importance of CMV-induced SNHL has been described, the mechanisms underlying its pathogenesis and the role of inflammatory responses remain elusive. The present study established an experimental model of hearing loss after systemic infection with murine CMV (MCMV) in newborn mice. Auditory brainstem responses were tested to evaluate hearing at 3 weeks, expression of inflammasome-associated factors was assessed by immunofluorescence, western blot analysis, reverse transcription-quantitative polymerase chain reaction and ELISA. MCMV sequentially induced inflammasome-associated factors. Furthermore, the inflammasome-associated factors were also increased in cultured spiral ganglion neurons infected with MCMV for 24 h. In addition, MCMV increased the content of reactive oxygen species (ROS). These results suggest that hearing loss caused by MCMV infection may be associated with ROS-induced inflammation.

A mathematical model of oxidation of SixGe1−x alloys is presented. The growth of SiO2 is simulated in conjunction with the determination of silicon distribution in SixGe1−x using numerical methods. The main feature of the model is the assumption of simultaneous oxidation of germanium and silicon when exposing the SixGe1−x to an oxidizing atmosphere. In accordance with thermodynamics, the GeO2 formed is subsequently reduced by the (free) silicon available at the interface between the growing SiO2 and the remaining SixGe1−x through a reduction reaction. Thus, the enhanced oxidation of silicon in the presence of germanium is modeled as a result of the rapid oxidation of germanium followed by the quick reduction of GeO2 by silicon. The growth of a mixed oxide in the form of either (Si,Ge)O2 or SiO2–GeO2 only occurs when the supply of silicon to the SiO2/SixGe1−x interface is insufficient. A comparison is made between simulation and experiment for wet oxidation (in pyrogenic steam) of polycrystalline SixGe1−x films. It is found that the model gives a good account of the oxidation process. Kinetic parameters, i.e., interfacial reaction rate constant for oxidation of germanium and diffusion coefficient of silicon (germanium) in SixGe1−x, are extracted by fitting the simulation to the experiment.

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Cervical cancer is the fourth most common malignancy among females worldwide. MicroRNA-379 (miR-379) is aberrantly expressed in multiple human cancer types. However, the expression pattern, roles, and detailed regulatory mechanisms of miR-379 in cervical cancer remain unknown. In this study, we found that miR-379 expression was downregulated in cervical cancer tissues and cell lines. Low miR-379 expression was correlated with International Federation of Gynecology and Obstetrics (FIGO) stage, lymph node metastasis, and distant metastasis. Additionally, miR-379 overexpression suppressed the proliferation and invasion of cervical cancer cells. Furthermore, V-crk avian sarcoma virus CT10 oncogene homolog-like (CRKL) was identified as a direct target of miR-379 in cervical cancer. CRKL was upregulated in cancer tissues and negatively correlated with miR-379 expression. Moreover, restored CRKL expression rescued the inhibitory effects of miR-379 overexpression on cell proliferation and invasion. In conclusion, miR-379 may serve as a tumor suppressor in cervical cancer by directly targeting CRKL. Restoring miR-379 expression may be an effective strategy for the treatment of cervical cancer.