A self-contained MEMS vector hydrophone with a scalar-vector integrated design is proposed in this paper. Compared with traditional MEMS vector hydrophones, this design solves the problem of ambiguity in the port and starboard during orientation, and also realizes the self-contained storage of acoustic signals. First, the sensor principle and structural design of the self-contained MEMS hydrophone are introduced, and then the principle of the combined beamforming algorithm is given. In addition to this, the amplitude and phase calibration method based on the self-contained MEMS vector hydrophone is proposed. Then, the sensitivity and phase calibrations of the sensor are carried out in the standing wave tube. The sensitivity of the vector channel is -182.7 dB (0 dB@1 V/μPa) and the sensitivity of the scalar channel is -181.8 dB (0 dB@1 V/μPa). Finally, an outdoor water experiment was carried out. The experimental results show that the self-contained MEMS vector hydrophone can accurately pick up and record underwater acoustics information. It realizes the precise orientation of the target by combining beamforming algorithms. The direction of arrival (DOA) error is within 5° under the outdoor experimental conditions with an SNR of 13.67 dB.

Over the last decade, gene set analysis has become the first choice for gaining insights into underlying complex biology of diseases through gene expression and gene association studies. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results. Although gene set analysis approaches are extensively used in gene expression and genome wide association data analysis, the statistical structure and steps common to these approaches have not yet been comprehensively discussed, which limits their utility. In this article, we provide a comprehensive overview, statistical structure and steps of gene set analysis approaches used for microarrays, RNA-sequencing and genome wide association data analysis. Further, we also classify the gene set analysis approaches and tools by the type of genomic study, null hypothesis, sampling model and nature of the test statistic, etc. Rather than reviewing the gene set analysis approaches individually, we provide the generation-wise evolution of such approaches for microarrays, RNA-sequencing and genome wide association studies and discuss their relative merits and limitations. Here, we identify the key biological and statistical challenges in current gene set analysis, which will be addressed by statisticians and biologists collectively in order to develop the next generation of gene set analysis approaches. Further, this study will serve as a catalog and provide guidelines to genome researchers and experimental biologists for choosing the proper gene set analysis approach based on several factors.

This paper presents the use of eye tracking data in Magnetic AngularRate Gravity (MARG)-sensor based head orientation estimation. The approach presented here can be deployed in any motion measurement that includes MARG and eye tracking sensors (e.g., rehabilitation robotics or medical diagnostics). The challenge in these mostly indoor applications is the presence of magnetic field disturbances at the location of the MARG-sensor. In this work, eye tracking data (visual fixations) are used to enable zero orientation change updates in the MARG-sensor data fusion chain. The approach is based on a MARG-sensor data fusion filter, an online visual fixation detection algorithm as well as a dynamic angular rate threshold estimation for low latency and adaptive head motion noise parameterization. In this work we use an adaptation of Madgwicks gradient descent filter for MARG-sensor data fusion, but the approach could be used with any other data fusion process. The presented approach does not rely on additional stationary or local environmental references and is therefore self-contained. The proposed system is benchmarked against a Qualisys motion capture system, a gold standard in human motion analysis, showing improved heading accuracy for the MARG-sensor data fusion up to a factor of 0.5 while magnetic disturbance is present.

A self-contained polymerase chain reaction (PCR) platform with miniaturized power-system is introduced. It is powered by portable lithium batteries and integrated continuous-flow PCR amplification platform. Generally speaking, traditional commercial thermal cyclers rely on external electric supply and thus they are too big in instrument size. This prevents real-timely and field testing during PCR diagnosis. The authors are introducing a continuous-flow 3D spiral microreactor for DNA amplifications and high-resolution multiplexed targets\' detection by utilizing the polyvinyl chloride (PVC) tubing-polymer to fabricate the microreactor for the first time. The whole setup (that can all be placed in one hand) includes (a) the thermo-cycled control (5.5 cm width, 10 cm length and 11 cm height), (b) the passive continuous-flow control, and (c) the trapezoidal PCR microreactor. The PCR platform can work for 4.5 h continuously. With minimal accessories and operations, the total cost of the self-contained PCR machinery is <20 $, much lower than the mainstream of commercial PCR machinery. By waiving external electric supply, this miniaturized PCR platform is applied to amplify the typical DNA fragments of plasma isolated hepatitis B virus (HBV), influenza virus (H7N9avian influenza) bacterium (Escherichia coli) plasmid and multiplexed targets. The efficiency of the method is 70% of that of commercial thermal cycler (CFX Connect, Bio Rad). The DNA of H7N9avian influenza can be detected in concentrations as low as 103 copies per μL. Graphical abstract By utilizing the polyvinyl chloride (PVC) tubing-polymer to fabricate the microchip for the first time, this paper introduces a 3D spiral microreactor with a miniaturized power-system supplied by portable AA-batteries, applied in DNA amplifications and high-resolution multiplexed targets\' detection. In this microdevice, we made the machinery portable by waiving the external plugs, which solved the problem of traditional commercial thermal cyclers about large volume and expensive price.

The analysis of gene sets (in a form of functionally related genes or pathways) has become the method of choice for extracting the strongest signals from omics data. The motivation behind using gene sets instead of individual genes is two-fold. First, this approach incorporates pre-existing biological knowledge into the analysis and facilitates the interpretation of experimental results. Second, it employs a statistical hypotheses testing framework. Here, we briefly review main Gene Set Analysis (GSA) approaches for testing differential expression of gene sets and several GSA approaches for testing statistical hypotheses beyond differential expression that allow extracting additional biological information from the data. We distinguish three major types of GSA approaches testing: (1) differential expression (DE), (2) differential variability (DV), and (3) differential co-expression (DC) of gene sets between two phenotypes. We also present comparative power analysis and Type I error rates for different approaches in each major type of GSA on simulated data. Our evaluation presents a concise guideline for selecting GSA approaches best performing under particular experimental settings. The value of the three major types of GSA approaches is illustrated with real data example. While being applied to the same data set, major types of GSA approaches result in complementary biological information.

Transcriptome sequencing (RNA-seq) is gradually replacing microarrays for high-throughput studies of gene expression. The main challenge of analyzing microarray data is not in finding differentially expressed genes, but in gaining insights into the biological processes underlying phenotypic differences. To interpret experimental results from microarrays, gene set analysis (GSA) has become the method of choice, in particular because it incorporates pre-existing biological knowledge (in a form of functionally related gene sets) into the analysis. Here we provide a brief review of several statistically different GSA approaches (competitive and self-contained) that can be adapted from microarrays practice as well as those specifically designed for RNA-seq. We evaluate their performance (in terms of Type I error rate, power, robustness to the sample size and heterogeneity, as well as the sensitivity to different types of selection biases) on simulated and real RNA-seq data. Not surprisingly, the performance of various GSA approaches depends only on the statistical hypothesis they test and does not depend on whether the test was developed for microarrays or RNA-seq data. Interestingly, we found that competitive methods have lower power as well as robustness to the samples heterogeneity than self-contained methods, leading to poor results reproducibility. We also found that the power of unsupervised competitive methods depends on the balance between up- and down-regulated genes in tested gene sets. These properties of competitive methods have been overlooked before. Our evaluation provides a concise guideline for selecting GSA approaches, best performing under particular experimental settings in the context of RNA-seq.