RNA2HLA: HLA-based quality control of RNA-seq datasets

RNA-sequencing (RNA-seq) is a widely used approach for accessing the transcriptome in biomedical research. Studies frequently include multiple samples taken from the same individual at various time points or under different conditions, correct assignment of those samples to each particular participant is evidently of great importance. Here, we propose taking advantage of typing the highly polymorphic genes from the human leukocyte antigen (HLA) complex in order to verify the correct allocation of RNA-seq samples to individuals. We introduce RNA2HLA, a novel quality control (QC) tool for performing study-wide HLA-typing for RNA-seq data and thereby identifying the samples from the common source. RNA2HLA allows precise allocation and grouping of RNA samples based on their HLA types. Strikingly, RNA2HLA revealed wrongly assigned samples from publicly available datasets and thereby demonstrated the importance of this tool for the quality control of RNA-seq studies. In addition, our tool successfully extracts HLA alleles in four-digital resolution and can be used to perform massive HLA-typing from RNA-seq based studies, which will serve multiple research purposes beyond sample QC.

Consequences of the Nyquist-Shannon sampling criterion in Mesoscopic Multiphoton Microscopy to avail full-field sub-micron resolution resolvability

With a limited effective voxel rate, to date, each laser-scanning mesoscopic multiphoton microscope (MPM), despite securing an ultra-large field of view (FOV) and an ultra-high optical resolution simultaneously, experiences a fundamental issue with digitization; i.e., inability to satisfy the Nyquist-Shannon sampling criterion to resolve the optics-limited sub-micron resolution over the whole FOV. Such a system either neglects the criterion degrading the digital resolution to twice the pixel size, or significantly reduces the imaging area and/or the imaging speed to respect the digitization. Here we introduce a Nyquist figure of merit parameter to assess this issue, further to comprehend a maximum aliasing-free FOV and a cross-over excitation wavelength for a laser scanning MPM system. Based on our findings we demonstrate an ultra-high voxel rate acquisition in a custom-built mesoscopic MPM system to exceed the Nyquist-rate for a >3800 FOV-resolution ratio while not compromising the imaging speed as well as the photon-budget.

On the feasibility of IoT-based smart meters for earthquake early warning

The microelectromechanical systems (MEMS) accelerometer built into a smart meter (SM) has a nominal digital resolution of 16 bits. However, this resolution collapses to 7 bits of information per sample when used in an urban environment. This collapse in resolution limits the sensitivity required to effectively operate the earthquake early warning platform (EEWP). In this study, we evaluate the performance of the MEMS sensor in present SMs with respect to a reference sensor, with a special focus on its poor noise power spectral density (PSD, [Formula: see text]). We also explore the general capacity of the SM in an IoT-based EEWP and provide explicit information regarding the 16-bit digital MEMS accelerometer. Then, we investigate the functionality of the sensor in the context of event detection in the presence of background vibration. When the value of acceleration root mean square (RMS) exceeds 20 mg, the meter’s error decreases to <20%, whereas the peak ground acceleration error decreases to <20% for the peak value greater than ~70 mg. The MEMS sensor is unreliable for motions with a peak acceleration of less than 148 mg or those with an RMS value less than 46 mg. However, we note that SMs exhibit reasonable amplitude and phase coherence for frequencies above 1 Hz with respect to the reference accelerometer. To enhance the sensitivity, averaging 1000 coherent accelerometer observations enhances the digital resolution to 14 bits, which allows the efficient usage of the network bandwidth. Since the accelerometer is used as an anti-tampering mechanism, the SM is similar to a tiltmeter. Therefore, it is necessary to reconfigure SMs for early warning systems. Despite the challenges, the use of SM for an IoT-based EEWP is technically feasible.