Noise and thermal stability of vibrating micro-gyrometers preamplifiers

The preamplifier is a critical component of gyrometer's electronics. Indeed the resolution of the sensor is limited by its signal to noise ratio, and the gyrometer's thermal stability is limited by its gain drift. In this paper, five different kinds of preamplifiers are presented and compared. Finally, the design of an integrated preamplifier is shown in order to increase the gain stability while reducing its noise and size.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/EDA-Publishing

A Low-Cost, Built-In Self-Test Method for Resistive MEMS sensors

This paper illustrates the experimental application of the LIMBO method, an identification method based on binary observations dedicated to the (self-) test of integrated electronic and electromechanical systems, such as MEMS. The tested MEMS device is a micro-wire used as a heating resistor, inserted in a Wheatstone bridge. We show how the impulse response and the offset of the micro device are estimated only using binary inputs and outputs and straightforward calculations, which can easily be implemented on an FPGA. This approach only requires a 1-bit ADC and a 1-bit DAC, which makes it very amenable to integration and highlights its suitability for the test of systems based on resistive sensor and/or actuator

Architecture for Integrated Mems Resonators Quality Factor Measurement

In this paper, an architecture designed for electrical measurement of the quality factor of MEMS resonators is proposed. An estimation of the measurement performance is made using PSPICE simulations taking into account the component's non-idealities. An error on the measured Q value of only several percent is achievable, at a small integration cost, for sufficiently high quality factor values (Q > 100).Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/EDA-Publishing

A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data

Batch effects are the systematic non-biological differences between batches (groups) of samples in microarray experiments due to various causes such as differences in sample preparation and hybridization protocols. Previous work focused mainly on the development of methods for effective batch effects removal. However, their impact on cross-batch prediction performance, which is one of the most important goals in microarray-based applications, has not been addressed. This paper uses a broad selection of data sets from the Microarray Quality Control Phase II (MAQC-II) effort, generated on three microarray platforms with different causes of batch effects to assess the efficacy of their removal. Two data sets from cross-tissue and cross-platform experiments are also included. Of the 120 cases studied using Support vector machines (SVM) and K nearest neighbors (KNN) as classifiers and Matthews correlation coefficient (MCC) as performance metric, we find that Ratio-G, Ratio-A, EJLR, mean-centering and standardization methods perform better or equivalent to no batch effect removal in 89, 85, 83, 79 and 75% of the cases, respectively, suggesting that the application of these methods is generally advisable and ratio-based methods are preferred

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions

BackgroundTargeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing.ResultsAll panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden.ConclusionThis comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.Peer reviewe

The SEQC2 epigenomics quality control (EpiQC) study

BACKGROUND: Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA's Epigenomics Quality Control Group. RESULTS: Each sample is processed in multiple replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), enzymatic deamination method (EMSeq), targeted methylation sequencing (Illumina Methyl Capture EPIC), single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies, and 850k Illumina methylation arrays. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, bwa-meth, and BitMapperBS), we find overall high concordance between assays, but also differences in efficiency of read mapping, CpG capture, coverage, and platform performance, and variable performance across 26 microarray normalization algorithms. CONCLUSIONS: The data provided herein can guide the use of these DNA reference materials in epigenomics research, as well as provide best practices for experimental design in future studies. By leveraging seven human cell lines that are designated as publicly available reference materials, these data can be used as a baseline to advance epigenomics research

Evolutionary Optimization of Robust and Chattering-Free Mamdani Type Fuzzy Controller

In fuzzy control area, the evolutionary algorithm is one of the most common design tools for fuzzy knowledge base generation. In this paper, we present the application of an integer evolutionary algorithm (IEA) for simultaneous optimization of fuzzy rule base and fuzzy data base of Mamdani-type fuzzy controller. The motivation behind this work is to design a robust and accurate controller without chattering phenomenon in the control input. More specifically, we consider the minimization of the variance of the control input in the same time as root mean square tracking error during the optimization. This fact leads the IEA to search for accurate fuzzy controller that provides just enough control input for smooth behavior. To assess the design technique, simulations were conducted with direct-drive DC motor. The simulation results show the effectiveness of the proposed IEA in designing a robust and chattering-free Mamdani fuzzy controller with high accuracy as compared to a conventional PD controller