Instrumentation and Automation Grounding

In any electrical or electronic systems, unwanted signals known as noise signals are encountered which interact with the true signal and thus affecting signal quality. Noise may enter into a device or system in many forms and have a different order of impacts. Prevention and elimination of noise had attained paramount importance to ensure signal fidelity. This chapter presents a comprehensive analysis on elimination of noise by electronic grounding of instrumentation and automation systems as well as various engineering considerations for the same.

Improved immunoassay sensitivity and specificity using single-molecule colocalization

Enzyme-linked immunosorbent assays (ELISAs) are a cornerstone of modern molecular detection, but the technique still suffers some notable challenges. One of the biggest problems is discriminating true signal generated by target molecules versus non-specific background arising from the interaction of detection antibodies with the assay substrate or interferents in the sample matrix. Single-Molecule Colocalization Assay (SiMCA) overcomes this problem by employing total internal reflection fluorescence (TIRF) microscopy to quantify target proteins based on the colocalization of fluorescent signal from orthogonally labeled capture and detection antibodies. By specifically counting colocalized fluorescent signals, we can essentially eliminate the confounding effects of background produced by non-specific binding of detection antibodies. We further employed a normalization strategy to account for the heterogeneous distribution of the capture antibodies, greatly improving the reproducibility of our measurements. In a series of experiments with TNF-α, we show that SiMCA can achieve a three-fold lower limit of detection compared to conventional single-color assays using the same antibodies and exhibits consistent performance for assays performed in complex specimens such as chicken serum and human blood. Our results help define the pernicious effects of non-specific background in immunoassays and demonstrate the diagnostic gains that can be achieved by eliminating those effects.

The Primary Physiological Roles of Autoinducer 2 in Escherichia coli Are Chemotaxis and Biofilm Formation

Autoinducer 2 (AI-2) is a ubiquitous metabolite but, instead of acting as a “universal signal,” relatively few phenotypes have been associated with it, and many scientists believe AI-2 is often a metabolic byproduct rather than a signal. Here, the aim is to present evidence that AI-2 influences both biofilm formation and motility (swarming and chemotaxis), using Escherichia coli as the model system, to establish AI-2 as a true signal with an important physiological role in this bacterium. In addition, AI-2 signaling is compared to the other primary signal of E. coli, indole, and it is shown that they have opposite effects on biofilm formation and virulence.

NGS-Integrator: An efficient tool for combining multiple NGS data tracks using minimum Bayes’ factors

Background Next-generation sequencing (NGS) is widely used for genome-wide identification and quantification of DNA elements involved in the regulation of gene transcription. Studies that generate multiple high-throughput NGS datasets require data integration methods for two general tasks: 1) generation of genome-wide data tracks representing an aggregate of multiple replicates of the same experiment; and 2) combination of tracks from different experimental types that provide complementary information regarding the location of genomic features such as enhancers. Results NGS-Integrator is a Java-based command line application, facilitating efficient integration of multiple genome-wide NGS datasets. NGS-Integrator first transforms all input data tracks using the complement of the minimum Bayes’ factor so that all values are expressed in the range [0,1] representing the probability of a true signal given the background noise. Then, NGS-Integrator calculates the joint probability for every genomic position to create an integrated track. We provide examples using real NGS data generated in our laboratory and from the mouse ENCODE database. Conclusions Our results show that NGS-Integrator is both time- and memory-efficient. Our examples show that NGS-Integrator can integrate information to facilitate downstream analyses that identify functional regulatory domains along the genome.

Gridless DOA Estimation for Minimum-Redundancy Linear Array in Nonuniform Noise

A gridless direction-of-arrival (DOA) estimation method to improve the estimation accuracy and resolution in nonuniform noise is proposed in this paper. This algorithm adopts the structure of minimum-redundancy linear array (MRA) and can be composed of two stages. In the first stage, by minimizing the rank of the covariance matrix of the true signal, the covariance matrix that filters out nonuniform noise is obtained, and then a gridless residual energy constraint scheme is designed to reconstruct the signal covariance matrix of the Hermitian Toeplitz structure. Finally, the unknown DOAs can be determined from the recovered covariance matrix, and the number of sources can be acquired as a byproduct. The proposed algorithm can be regarded as a gridless version method based on sparsity. Simulation results indicate that the proposed method has higher estimation accuracy and resolution compared with existing algorithms.

An Innovative Dual-Axis Precision Level Based on Light Transmission and Refraction for Angle Measurement

Currently, the widely used pendulum-type precision level cannot be miniaturized because reducing the size of the pendulum will reduce its displacement so as to decrease the measurement accuracy and resolution. Moreover, the commercial pendulum-type level can only sense one direction. In this paper, an innovative compact and high-accuracy dual-axis precision level is proposed. Based on the optical principle of light refraction and the reference of the invariant liquid level, the pendulum is no more needed. In addition, based on the light transmission design, there is no reflection signal to interfere with the true signal. Therefore, the level can achieve a high accuracy and small-sized design. The calibration result shows the error of the proposed precision level is better than ±0.6 arc-sec in the measurement range of ±100 arc-sec, and better than ±5 arc-sec in the full measurement range of ±800 arc-sec.

Robust Gradient-Based Markov Subsampling

Subsampling is a widely used and effective method to deal with the challenges brought by big data. Most subsampling procedures are designed based on the importance sampling framework, where samples with high importance measures are given corresponding sampling probabilities. However, in the highly noisy case, these samples may cause an unstable estimator which could lead to a misleading result. To tackle this issue, we propose a gradient-based Markov subsampling (GMS) algorithm to achieve robust estimation. The core idea is to construct a subset which allows us to conservatively correct a crude initial estimate towards the true signal. Specifically, GMS selects samples with small gradients via a probabilistic procedure, constructing a subset that is likely to exclude noisy samples and provide a safe improvement over the initial estimate. We show that the GMS estimator is statistically consistent at a rate which matches the optimal in the minimax sense. The promising performance of GMS is supported by simulation studies and real data examples.

Instrumentation and Automation Grounding

In any electrical or electronic systems, unwanted signals known as noise signals are encountered which interact with the true signal and thus affecting signal quality. Noise may enter into a device or system in many forms and have a different order of impacts. Prevention and elimination of noise had attained paramount importance to ensure signal fidelity. This chapter presents a comprehensive analysis on elimination of noise by electronic grounding of instrumentation and automation systems as well as various engineering considerations for the same.

A comparison of random-field-theory and false-discovery-rate inference results in the analysis of registered one-dimensional biomechanical datasets

Background The inflation of falsely rejected hypotheses associated with multiple hypothesis testing is seen as a threat to the knowledge base in the scientific literature. One of the most recently developed statistical constructs to deal with this problem is the false discovery rate (FDR), which aims to control the proportion of the falsely rejected null hypotheses among those that are rejected. FDR has been applied to a variety of problems, especially for the analysis of 3-D brain images in the field of Neuroimaging, where the predominant form of statistical inference involves the more conventional control of false positives, through Gaussian random field theory (RFT). In this study we considered FDR and RFT as alternative methods for handling multiple testing in the analysis of 1-D continuum data. The field of biomechanics has recently adopted RFT, but to our knowledge FDR has not previously been used to analyze 1-D biomechanical data, nor has there been a consideration of how FDR vs. RFT can affect biomechanical interpretations. Methods We reanalyzed a variety of publicly available experimental datasets to understand the characteristics which contribute to the convergence and divergence of RFT and FDR results. We also ran a variety of numerical simulations involving smooth, random Gaussian 1-D data, with and without true signal, to provide complementary explanations for the experimental results. Results Our results suggest that RFT and FDR thresholds (the critical test statistic value used to judge statistical significance) were qualitatively identical for many experimental datasets, but were highly dissimilar for others, involving non-trivial changes in data interpretation. Simulation results clarified that RFT and FDR thresholds converge as the true signal weakens and diverge when the signal is broad in terms of the proportion of the continuum size it occupies. Results also showed that, while sample size affected the relation between RFT and FDR results for small sample sizes (<15), this relation was stable for larger sample sizes, wherein only the nature of the true signal was important. Discussion RFT and FDR thresholds are both computationally efficient because both are parametric, but only FDR has the ability to adapt to the signal features of particular datasets, wherein the threshold lowers with signal strength for a gain in sensitivity. Additional advantages and limitations of these two techniques as discussed further. This article is accompanied by freely available software for implementing FDR analyses involving 1-D data and scripts to replicate our results.

High-resolution signal-in-space measurements of VHF omnidirectional ranges using UAS

In this paper, we describe measurement results of the signal-in-space of very high frequency (VHF) omnidirectional range (VOR) facilities. In aviation VOR are used to display the current course of the aircraft in the cockpit. To understand the influence of wind turbines (WT) on the signal integrity of terrestrial navigation and radar signals, the signal content and its changes, respectively, must be investigated. So far, only numerical simulations have been carried out on the frequency-modulation (FM) part of the Doppler-VOR (DVOR) signal to estimate the influence of WT on DVOR. Up to now, the amplitude-modulated (AM) part of the DVOR was not assessed at all. In 2016, we presented an unmanned aerial system (UAS) as a carrier for state-of-the-art radio-frequency (RF) measurement instrumentation (Schrader et al., 2016a, c; Bredemeyer et al., 2016), to measure and to record the true signal-in-space (both FM and AM signal) during the flight. The signal-in-space (which refers to time-resolved signal content and field strength, respectively) is measured and sampled without loss of information and, furthermore, synchronously stored with time stamp and with precise position in space, where the measurements were taken.