Canonical correlation analysis as a feature extraction method to classify active sonar targets with shallow neural networks.

Sonar target recognition remains an active area of research due to the complex entanglement of features from various acoustic scatterers, background clutter, and distortion by waveguide propagation effects. An equally challenging issue is due to different acoustic echoes returned from the target (including different target elements) itself. This work investigates the sonar target classification problem from a statistical perspective and aims to extract salient target feature vectors. Specifically, a multivariate statistical method is employed, canonical correlation analysis (CCA), as a feature extraction technique prior to multi-class classification of active sonar field data. The intuition behind using CCA is that persistent features slowly morph over time due to the changing aspect angles and platform positions and can be represented by maximally correlated projections of consecutive pings. CCA is applied using a sliding window, and the projections are used as feature vectors to train a neural network classifier. The smallest increase in classification accuracy when comparing the projection feature vectors to unprocessed feature vectors was 10%. The largest increase was 34%. The results are further examined through the use of confusion matrices and layer-wise relevance propagation, which distributes the trained networks output score to the input layer.

Employing and Interpreting a Machine Learning Target-Cognizant Technique for Analysis of Unknown Signals in Multiple Reaction Monitoring.

The aim of this interdisciplinary work is a robust signal processing and autonomous machine learning framework to associate well-known (target) as well as any potentially unknown (non-target) peaks present within gas chromatography-mass spectrometry (GC/MS/MS) raw instrument signal. Particularly, this work evaluates three machine learning algorithms abilities to autonomously associate raw signal peaks based on accuracy in training and testing. A target is a known congener that is expected to be present within the raw instrument signal and a non-target is an unknown or unexpected compound. Autonomously identifying target peaks within the GC/MS/MS and associating them with non-target peaks can help improve the analysis of collected samples. Association of peaks refers to classifying peaks as known congeners regardless if the peak is a target or non-target. Uncertainty of peaks fitted and discovered through raw instrument signals from GC/MS/MS data is assessed to create topographical illustrations of target annotated peaks among sample raw instrument signals collected across diverse locations in the Chicago area. The term "annotated peak" is used to assign peaks found at specific retention times as a known congener. Adaptive signal processing techniques are utilized to smooth data and correct baseline drifts as well as detect and separate coeluted (overlapped) peaks in the raw instrument signal to provide key feature extraction. 150 air samples are analyzed for individual polychlorinated biphenyls (PCB) with GC/MS/MS across Chicago, IL. 80% of the data is used for training classification of target PCBs and 20% of the data is evaluated to identify and associate consistently occurring non-target peaks with target PCBs. A random forest classifier is used to associate identified peaks to target PCB peaks. Geographical topographical representations of target PCBs in the raw instrument signal demonstrates how PCBs accumulate and degrade in certain locations.

Sonar target representation using two-dimensional Gabor wavelet features.

This paper introduces a feature extraction technique that identifies highly informative features from sonar magnitude spectra for automated target classification. The approach involves creating feature representations through convolution of a two-dimensional Gabor wavelet and acoustic color magnitudes to capture elastic waves. This feature representation contains extracted localized features in the form of Gabor stripes, which are representative of unique targets and are invariant of target aspect angle. Further processing removes non-informative features through a threshold-based culling. This paper presents an approach that begins connecting model-based domain knowledge with machine learning techniques to allow interpretation of the extracted features while simultaneously enabling robust target classification. The relative performance of three supervised machine learning classifiers, specifically a support vector machine, random forest, and feed-forward neural network are used to quantitatively demonstrate the representations' informationally rich extracted features. Classifiers are trained and tested with acoustic color spectrograms and features extracted using the algorithm, interpreted as stripes, from two public domain field datasets. An increase in classification performance is generally seen, with the largest being a 47% increase from the random forest tree trained on the 1-31 kHz PondEx10 data, suggesting relatively small datasets can achieve high classification accuracy if model-cognizant feature extraction is utilized.

Emerging Priorities for Microbiome Research.

Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which has yielded tremendous insight into the nature of the microbial communities, including their interactions and effects, both within a host and in an external environment as part of an ecological community. Understanding the role of microbiota, including their dynamic interactions with their hosts and other microbes, can enable the engineering of new diagnostic techniques and interventional strategies that can be used in a diverse spectrum of fields, spanning from ecology and agriculture to medicine and from forensics to exobiology. From June 19-23 in 2017, the NIH and NSF jointly held an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in our Understanding of the Microbiome. This review is inspired by some of the topics that arose as priority areas from this unique, interactive workshop. The goal of this review is to summarize the Innovation Lab's findings by introducing the reader to emerging challenges, exciting potential, and current directions in microbiome research. The review is broken into five key topic areas: (1) interactions between microbes and the human body, (2) evolution and ecology of microbes, including the role played by the environment and microbe-microbe interactions, (3) analytical and mathematical methods currently used in microbiome research, (4) leveraging knowledge of microbial composition and interactions to develop engineering solutions, and (5) interventional approaches and engineered microbiota that may be enabled by selectively altering microbial composition. As such, this review seeks to arm the reader with a broad understanding of the priorities and challenges in microbiome research today and provide inspiration for future investigation and multi-disciplinary collaboration.

Shallow water channel estimation with energy efficient transmitted signal design.

This paper proposes channel estimation using energy efficient transmission of signal dictionaries for shallow water acoustic communications. Specifically, the multi-columned structure of the channel delay spread is exploited to design partially sampled dictionary in a two-dimensional (2-D) frequency representation of the channel. The key contribution of this work is to achieve considerable energy saving in the transmission of complex exponential signals, designed specifically for real-time shallow water channel estimation at the receiver. This is accomplished by harnessing 2-D frequency localization with compressive transmission and modified-compressive sensing with prior information to exploit the sparse structure of the rapidly fluctuating shallow water acoustic channel in real time. The proposed technique reduces demands on transmitted signal energy by harnessing the reconstruction ability of sparse sensing while retaining key non-sparse channel elements that represent important multipath phenomena. Numerical evidence based on experimental channel estimates demonstrates the efficacy of the proposed work.

Fecal Fingerprints of Enteric Pathogen Contamination in Public Environments of Kisumu, Kenya, Associated with Human Sanitation Conditions and Domestic Animals.

Young children are infected by a diverse range of enteric pathogens in high disease burden settings, suggesting pathogen contamination of the environment is equally diverse. This study aimed to characterize across- and within-neighborhood diversity in enteric pathogen contamination of public domains in urban informal settlements of Kisumu, Kenya, and to assess the relationship between pathogen detection patterns and human and domestic animal sanitation conditions. Microbial contamination of soil and surface water from 166 public sites in three Kisumu neighborhoods was measured by enterococcal assays and quantitative reverse transcription polymerase chain reaction (qRT-PCR) for 19 enteric pathogens. Regression was used to assess the association between observed sanitary indicators of contamination with enterococci and pathogen presence and concentration, and pathogen diversity. Seventeen types of pathogens were detected in Kisumu public domains. Enteric pathogens were codetected in 33% of soil and 65% of surface water samples. Greater pathogen diversity was associated with the presence of domestic animal feces but not with human open defecation, deteriorating latrines, flies, or disposal of human feces. Sanitary conditions were not associated with enterococcal bacteria, specific pathogen concentrations, or "any pathogen". Young children played at 40% of observed sites. Managing domestic animal feces may be required to reduce enteric pathogen environmental contamination in high-burden settings.

Interpreting comprehensive two-dimensional gas chromatography using peak topography maps with application to petroleum forensics.

BACKGROUND: Comprehensive two-dimensional gas chromatography [Formula: see text] provides high-resolution separations across hundreds of compounds in a complex mixture, thus unlocking unprecedented information for intricate quantitative interpretation. We exploit this compound diversity across the [Formula: see text] topography to provide quantitative compound-cognizant interpretation beyond target compound analysis with petroleum forensics as a practical application. We focus on the [Formula: see text] topography of biomarker hydrocarbons, hopanes and steranes, as they are generally recalcitrant to weathering. We introduce peak topography maps (PTM) and topography partitioning techniques that consider a notably broader and more diverse range of target and non-target biomarker compounds compared to traditional approaches that consider approximately 20 biomarker ratios. Specifically, we consider a range of 33-154 target and non-target biomarkers with highest-to-lowest peak ratio within an injection ranging from 4.86 to 19.6 (precise numbers depend on biomarker diversity of individual injections). We also provide a robust quantitative measure for directly determining "match" between samples, without necessitating training data sets. RESULTS: We validate our methods across 34 [Formula: see text] injections from a diverse portfolio of petroleum sources, and provide quantitative comparison of performance against established statistical methods such as principal components analysis (PCA). Our data set includes a wide range of samples collected following the 2010 DeepwaterHorizon disaster that released approximately 160 million gallons of crude oil from the Macondo well (MW). Samples that were clearly collected following this disaster exhibit statistically significant match [Formula: see text] using PTM-based interpretation against other closely related sources. PTM-based interpretation also provides higher differentiation between closely correlated but distinct sources than obtained using PCA-based statistical comparisons. In addition to results based on this experimental field data, we also provide extentive perturbation analysis of the PTM method over numerical simulations that introduce random variability of peak locations over the [Formula: see text] biomarker ROI image of the MW pre-spill sample (sample [Formula: see text] in Additional file 4: Table S1). We compare the robustness of the cross-PTM score against peak location variability in both dimensions and compare the results against PCA analysis over the same set of simulated images. Detailed description of the simulation experiment and discussion of results are provided in Additional file 1: Section S8. CONCLUSIONS: We provide a peak-cognizant informational framework for quantitative interpretation of [Formula: see text] topography. Proposed topographic analysis enables [Formula: see text] forensic interpretation across target petroleum biomarkers, while including the nuances of lesser-known non-target biomarkers clustered around the target peaks. This allows potential discovery of hitherto unknown connections between target and non-target biomarkers.