Multivariate classification of animal communication signals: A simulation-based comparison of alternative signal processing procedures using electric fishes

Evolutionary studies of communication can benefit from classification procedures that allow individual animals to be assigned to groups (e.g. species) on the basis of high-dimension data representing their signals. Prior to classification, signals are usually transformed by a signal processing procedure into structural features. Applications of these signal processing procedures to animal communication have been largely restricted to the manual or semi-automated identification of landmark features from graphical representations of signals. Nonetheless, theory predicts that automated time-frequency-based digital signal processing (DSP) procedures can represent signals more efficiently (using fewer features) than can landmark procedures or frequency-based DSP – allowing more accurate classification. Moreover, DSP procedures are objective in that they require little previous knowledge of signal diversity, and are relatively free from potentially ungrounded assumptions of cross-taxon homology. Using a model data set of electric organ discharge waveforms from five sympatric species of the electric fish Gymnotus, we adopted an exhaustive simulation approach to investigate the classificatory performance of different signal processing procedures. We considered a landmark procedure, a frequency-based DSP procedure (the fast Fourier transform), and two kinds of time-frequency-based DSP procedures (a short-time Fourier transform, and several implementations of the discrete wavelet transform -DWT). The features derived from each of these signal processing procedures were then subjected to dimension reduction procedures to separate those features which permit the most effective discrimination among groups of signalers. We considered four alternative dimension reduction methods. Finally, each combination of reduced data was submitted to classification by linear discriminant analysis. Our results support theoretical predictions that time-frequency DSP procedures (especially DWT) permit more efficient discrimination of groups. The performance of signal processing was found to depend largely upon the dimension reduction procedure employed, and upon the number of resulting features. Because the best combinations of procedures are dataset-dependent and difficult to predict, we conclude that simulations of the kind described here, or at least simplified versions of them, should be routinely executed before classification of animal signals - especially unfamiliar ones.     

Recording of data and immediate interpretation of myoelectric activity of the stomach--electrogastrography]

Unlike other electrophysiological measurements such as electrocardiography (ECG) and electroencephalography (EEG), the cutaneous measurement of the electrical activity of the stomach (electrogastrography, EGG) is not an established clinical diagnostic procedure. To overcome common problems in acquiring very-low-frequency signals in the presence of large and unstable voltages we developed a real time data acquisition and analysis system based on linear-phase digital signal processing instead of analog filtering. FFT's computed on line from highly overlapping time sets are displayed as pseudo-3D or grey-scale graphic plots.     

基于SOPC技术的多路并行ECG实时检测分析系统的设计

针对目前心电监护设备微型化、实时性、高采样率、存储量大等实际需求,采用了一种基于最新的SOPC（System On a Programmable Chip）技术的心电检测系统的设计。将DSP和MCU的功能集成在一块FPGA上,在FPGA内部实现多路心电信号的并行处理,由SD卡记录较长时间的连续心电信号,并实现心电信号的实时分析和心律失常的预警等扩展功能。     

Within-species variation and measurement error in phylogenetic comparative methods

Most phylogenetically based statistical methods for the analysis of quantitative or continuously varying phenotypic traits assume that variation within species is absent or at least negligible, which is unrealistic for many traits. Within-species variation has several components. Differences among populations of the same species may represent either phylogenetic divergence or direct effects of environmental factors that differ among populations (phenotypic plasticity). Within-population variation also contributes to within-species variation and includes sampling variation, instrument-related error, low repeatability caused by fluctuations in behavioral or physiological state, variation related to age, sex, season, or time of day, and individual variation within such categories. Here we develop techniques for analyzing phylogenetically correlated data to include within-species variation, or "measurement error" as it is often termed in the statistical literature. We derive methods for (i) univariate analyses, including measurement of "phylogenetic signal," (ii) correlation and principal components analysis for multiple traits, (iii) multiple regression, and (iv) inference of "functional relations," such as reduced major axis (RMA) regression. The methods are capable of incorporating measurement error that differs for each data point (mean value for a species or population), but they can be modified for special cases in which less is known about measurement error (e.g., when one is willing to assume something about the ratio of measurement error in two traits). We show that failure to incorporate measurement error can lead to both biased and imprecise (unduly uncertain) parameter estimates. Even previous methods that are thought to account for measurement error, such as conventional RMA regression, can be improved by explicitly incorporating measurement error and phylogenetic correlation. We illustrate these methods with examples and simulations and provide Matlab programs.     

Single cell analysis using surface enhanced Raman scattering (SERS) tags

Fluorescence is a mainstay of bioanalytical methods, offering sensitive and quantitative reporting, often in multiplexed or multiparameter assays. Perhaps the best example of the latter is flow cytometry, where instruments equipped with multiple lasers and detectors allow measurement of 15 or more different fluorophores simultaneously, but increases beyond this number are limited by the relatively broad emission spectra. Surface enhanced Raman scattering (SERS) from metal nanoparticles can produce signal intensities that rival fluorescence, but with narrower spectral features that allow a greater degree of multiplexing. We are developing nanoparticle SERS tags as well as Raman flow cytometers for multiparameter single cell analysis of suspension or adherent cells. SERS tags are based on plasmonically active nanoparticles (gold nanorods) whose plasmon resonance can be tuned to give optimal SERS signals at a desired excitation wavelength. Raman resonant compounds are adsorbed on the nanoparticles to confer a unique spectral fingerprint on each SERS tag, which are then encapsulated in a polymer coating for conjugation to antibodies or other targeting molecules. Raman flow cytometry employs a high resolution spectral flow cytometer capable of measuring the complete SERS spectra, as well as conventional flow cytometry measurements, from thousands of individual cells per minute. Automated spectral unmixing algorithms extract the contributions of each SERS tag from each cell to generate high content, multiparameter single cell population data. SERS-based cytometry is a powerful complement to conventional fluorescence-based cytometry. The narrow spectral features of the SERS signal enables more distinct probes to be measured in a smaller region of the optical spectrum with a single laser and detector, allowing for higher levels of multiplexing and multiparameter analysis.     

Nanoparticles for bioanalysis

This review covers the emerging field of nanobiotechnology, in which nanoparticles are applied to the analysis of biomolecules. Nanoparticles can be used in a variety of bioanalytical formats, and this review discusses four classes of use. First, nanoparticles as quantitation tags, such as the optical detection of quantum dots and the electrochemical detection of metallic nanoparticles. Second, encoded nanoparticles as substrates for multiplexed bioassays, such as striped metallic nanoparticles. Third, nanoparticles that leverage signal transduction, for example in colloidal gold-based aggregation assays. Fourth, functional nanoparticles that exploit specific physical or chemical properties of nanoparticles to carry out novel functions, such as the catalysis of a biological reaction. In addition, the review discusses the next generation of nanoparticles that will be utilized in the life sciences, such as nanodots and carbon nanotubes.     

Analytical and numerical solutions of the potential and electric field generated by different electrode arrays in a tumor tissue under electrotherapy

Background

During the cardiac cycle, the heart normally produces repeatable physiological sounds. However, under pathologic conditions, such as with heart valve stenosis or a ventricular septal defect, blood flow turbulence leads to the production of additional sounds, called murmurs. Murmurs are random in nature, while the underlying heart sounds are not (being deterministic).

Innovation

We show that a new analytical technique, which we call Digital Subtraction Phonocardiography (DSP), can be used to separate the random murmur component of the phonocardiogram from the underlying deterministic heart sounds.

Methods

We digitally recorded the phonocardiogram from the anterior chest wall in 60 infants and adults using a high-speed USB interface and the program Gold Wave http://www.goldwave.com. The recordings included individuals with cardiac structural disease as well as recordings from normal individuals and from individuals with innocent heart murmurs. Digital Subtraction Analysis of the signal was performed using a custom computer program called Murmurgram. In essence, this program subtracts the recorded sound from two adjacent cardiac cycles to produce a difference signal, herein called a "murmurgram". Other software used included Spectrogram (Version 16), GoldWave (Version 5.55) as well as custom MATLAB code.

Results

Our preliminary data is presented as a series of eight cases. These cases show how advanced signal processing techniques can be used to separate heart sounds from murmurs. Note that these results are preliminary in that normal ranges for obtained test results have not yet been established.

Conclusions

Cardiac murmurs can be separated from underlying deterministic heart sounds using DSP. DSP has the potential to become a reliable and economical new diagnostic approach to screening for structural heart disease. However, DSP must be further evaluated in a large series of patients with well-characterized pathology to determine its clinical potential.     

Frequency characteristics of signals and instrumentation: implication for EMG biofeedback studies

Signals can be analyzed in either the time or frequency domain. In the time domain, the analysis consists of manipulating and measuring one or more characteristics of the signal that may vary with time. One can, for instance, rectify a signal, filter it, calculate its mean value, display the histogram of its amplitude, and so forth. Frequency analysis is less well understood because it requires a lengthy mathematical treatment most easily done by computer. However, it gives exclusive information on a signal. For instance, when the frequency content of a signal is known, it is easy to specify which characteristics an amplifier must have in order to amplify the signal without distortion, or to set the cutoff frequencies of filters to eliminate noise. Also, in many circumstances, frequency spectra are more easily interpreted than the original raw data. Such is the case with the EMG where the random aspect of the signal makes some form of processing (i.e., rectification, filtering, etc.) necessary, but not always as meaningful as we would like. Thus we present here the principal characteristics of frequency analysis, and discuss its usefulness in analyzing EMG signals and its application to biofeedback, clinical practice, and research.     

Open, reconfigurable cytometric acquisition system: ORCAS.

A digital signal processing (DSP)-based digital data acquisition system has been developed to support novel flow cytometry efforts. The system flexibility includes how it detects, captures, and processes event data. Custom data capture boards utilizing analog to digital converters (ADCs) and field programmable gate arrays (FPGA) detect events and capture correlated event data. A commercial DSP board processes the captured data and sends the results over the IEEE 1394 bus to the host computer that provides a user interface for acquisition, display, analysis, and storage. The system collects list mode data, correlated pulse shapes, or streaming data from a variety of detector types using Linux, Mac OS X, and Windows host computers. It extracts pulse features not found on commercial systems with excellent sensitivity and linearity over a wide dynamic range. List mode data are saved in FCS 3.0 formatted files while streaming or correlated waveform data are saved in custom format files for postprocessing. Open, reconfigurable cytometric acquisition system is compact, scaleable, flexible, and modular. Programmable feature extraction algorithms have exciting possibilities for both new and existing applications. The recent availability of a commercial data capture board will enable general availability of similar systems.     

Developmental characters in phylogenetic inference and their absolute timing information

Despite the recent surge of interest in studying the evolution of development, surprisingly little work has been done to investigate the phylogenetic signal in developmental characters. Yet, both the potential usefulness of developmental characters in phylogenetic reconstruction and the validity of inferences on the evolution of developmental characters depend on the presence of such a phylogenetic signal and on the ability of our coding scheme to capture it. In a recent study, we showed, using simulations, that a new method (called the continuous analysis) using standardized time or ontogenetic sequence data and squared-change parsimony outperformed event pairing and event cracking in analyzing developmental data on a reference phylogeny. Using the same simulated data, we demonstrate that all these coding methods (event pairing and standardized time or ontogenetic sequence data) can be used to produce phylogenetically informative data. Despite some dependence between characters (the position of an event in an ontogenetic sequence is not independent of the position of other events in the same sequence), parsimony analysis of such characters converges on the correct phylogeny as the amount of data increases. In this context, the new coding method (developed for the continuous analysis) outperforms event pairing; it recovers a lower proportion of incorrect clades. This study thus validates the use of ontogenetic data in phylogenetic inference and presents a simple coding scheme that can extract a reliable phylogenetic signal from these data.     

基于虚拟仪器技术的血管壁动态信息无创检测与辅助诊断系统

为了在体外无损地实现对血管壁动态信息、心电和心音信号等医学信息多参数的综合同步检测以及分析和处理,利用虚拟仪器技术设计了血管壁动态信息多参数的无创检测辅助诊断系统．该系统硬件平台由信号输入模块、信号调理模块以及采集卡等三大模块组成。软件系统由开发虚拟仪器的流行软件LabVIEW编写,实现了数据的采集、实时显示、分析处理和存储等。初步的临床检测结果证实了本无创检测系统的可行性和临床应用的前景,为功能性无创辅助诊断与血管壁弹性（硬化）程度有关的血管疾病提供了一种新的方法。     

Selecting EEG components using time series analysis in brain death diagnosis

In diagnosis of brain death for human organ transplant, EEG (electroencephalogram) must be flat to conclude the patient’s brain death but it has been reported that the flat EEG test is sometimes difficult due to artifacts such as the contamination from the power supply and ECG (electrocardiogram, the signal from the heartbeat). ICA (independent component analysis) is an effective signal processing method that can separate such artifacts from the EEG signals. Applying ICA to EEG channels, we obtain several separated components among which some correspond to the brain activities while others contain artifacts. This paper aims at automatic selection of the separated components based on time series analysis. In the flat EEG test in brain death diagnosis, such automatic component selection is helpful.     

PEG–salt aqueous two-phase systems: an attractive and versatile liquid–liquid extraction technology for the downstream processing of proteins and enzymes

Nowadays, there is an increasing demand to establish new feasible, efficient downstream processing (DSP) techniques in biotechnology and related fields. Although several conventional DSP technologies have been widely employed, they are usually expensive and time-consuming and often provide only low recovery yields. Hence, the DSP is one major bottleneck for the commercialization of biological products. In this context, polyethylene glycol (PEG)–salt aqueous two-phase systems (ATPS) represent a promising, efficient liquid–liquid extraction technology for the DSP of various biomolecules, such as proteins and enzymes. Furthermore, ATPS can overcome the limitations of traditional DSP techniques and have gained importance for applications in several fields of biotechnology due to versatile advantages over conventional DSP methods, such as biocompatibility, technical simplicity, and easy scale-up potential. In the present review, various practical applications of PEG–salt ATPS are presented to highlight their feasibility to operate as an attractive and versatile liquid–liquid extraction technology for the DSP of proteins and enzymes, thus facilitating the approach of new researchers to this technique. Thereby, single- and multi-stage extraction, several process integration methods, as well as large-scale extraction and purification of proteins regarding technical aspects, scale-up, recycling of process chemicals, and economic aspects are discussed.     

Optical Doppler tomography based on a field programmable gate array

We report the design of and results obtained by using a field programmable gate array (FPGA) to digitally process optical Doppler tomography signals. The processor fits into the analog signal path in an existing optical coherence tomography setup. We demonstrate both Doppler frequency and envelope extraction using the Hilbert transform, all in a single FPGA. An FPGA implementation has certain advantages over general purpose digital signal processor (DSP) due to the fact that the processing elements operate in parallel as opposed to the DSP, which is primarily a sequential processor.     

Frequency characteristics of signals and instrumentation: Implication for EMG biofeedback studies

Signals can be analyzed in either the time or frequency domain. In the time domain, the analysis consists of manipulating and measuring one or more characteristics of the signal that may vary with time. One can, for instance, rectify a signal, filter it, calculate its mean value, display the histogram of its amplitude, and so forth. Frequency analysis is less well understood because it requires a lengthy mathematical treatment most easily done by computer. However, it gives exclusive information on a signal. For instance, when the frequency content of a signal is known, it is easy to specify which characteristics an amplifier must have in order to amplify the signal without distortion, or to set the cutoff frequencies of filters to eliminate noise. Also, in many circumstances, frequency spectra are more easily interpreted than the original raw data. Such is the case with the EMG where the random aspect of the signal makes some form of processing (i.e., rectification, filtering, etc.) necessary, but not always as meaningful as we would like. Thus we present here the principal characteristics of frequency analysis, and discuss its usefulness in analyzing EMG signals and its application to biofeedback, clinical practice, and research.The authors acknowledge the technical assistance of F. Kemp, M. Goyette, and C. Goulet. T. Milner kindly reviewed the final version of the text. The preparation of this paper was supported through funds from Health and Welfare Canada (NHRDP) and the Centre de recherche, Institut de réadaptation de Montréal.     

Biotechnological applications of bioluminescence and chemiluminescence

Recent progress in molecular biology has made available several biotechnological tools that take advantage of the high detectability and rapidity of bioluminescence and chemiluminescence spectroscopy. These developments provide inroads to in vitro and in vivo continuous monitoring of biological processes (e.g. gene expression, protein-protein interaction and disease progression), with clinical, diagnostic and drug discovery applications. Furthermore, combining luminescent enzymes or photoproteins with biospecific recognition elements at the genetic level has led to the development of ultrasensitive and selective bioanalytical tools, such as recombinant whole-cell biosensors, immunoassays and nucleic acid hybridization assays. The high detectability of the luminescence analytical signal makes it appropriate for miniaturized bioanalytical devices (e.g. microarrays, microfluidic devices and high-density-well microtiter plates) for the high-throughput screening of genes and proteins in small sample volumes.