On the affinity of Chuaria–Tawuia complex : a multidisciplinary study

In this study, biometric and structural engineering tool have been used to examine a possible relationship within Chuaria–Tawuia complex and micro-FTIR (Fourier Transform Infrared Spectroscopy) analyses to understand the biological affinity of Chuaria circularis Walcott, collected from the Mesoproterozoic Suket Shales of the Vindhyan Supergroup and the Neoproterozoic Halkal Shales of the Bhima Group of peninsular India. Biometric analyses of well preserved carbonized specimens show wide variation in morphology and uni-modal distribution. We believe and demonstrate to a reasonable extent that C. circularis most likely was a part of Tawuia-like cylindrical body of algal origin. Specimens with notch/cleft and overlapping preservation, mostly recorded in the size range of 3–5 mm, are of special interest. Five different models proposed earlier on the life cycle of C. circularis are discussed. A new model, termed as ‘Hybrid model’ based on present multidisciplinary study assessing cylindrical and spherical shapes suggesting variable cell wall strength and algal affinity is proposed. This model discusses and demonstrates varied geometrical morphologies assumed by Chuaria and Tawuia, and also shows the inter-relationship of Chuaria–Tawuia complex. Structural engineering tool (thin walled pressure vessel theory) was applied to investigate the implications of possible geometrical shapes (sphere and cylinder), membrane (cell wall) stresses and ambient pressure environment on morphologically similar C. circularis and Tawuia. The results suggest that membrane stresses developed on the structures similar to Chuaria–Tawuia complex were directly proportional to radius and inversely proportional to the thickness in both cases. In case of hollow cylindrical structure, the membrane stresses in circumferential direction (hoop stress) are twice of the longitudinal direction indicating that rupture or fragmentation in the body of Tawuia would have occurred due to hoop stress. It appears that notches and discontinuities seen in some of the specimens of Chuaria may be related to rupture suggesting their possible location in 3D Chuaria. The micro-FTIR spectra of C. circularis are characterized by both aliphatic and aromatic absorption bands. The aliphaticity is indicated by prominent alkyl group bands between 2800–3000 and 1300–1500 cm−1. The prominent absorption signals at 700–900 cm−1 (peaking at 875 and 860 cm−1) are due to aromatic CH out of plane deformation. A narrow, strong band is centred at 1540 cm−1 which could be COOH band. The presence of strong aliphatic bands in FTIR spectra suggests that the biogeopolymer of C. circularis is of aliphatic nature. The wall chemistry indicates the presence of ‘algaenan’—a biopolymer of algae.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

A new geological model of Cenozoic New Zealand suggests a complex phylogeographic history for New Zealand terrestrial taxa

The new model of North Island Cenozoic palaeogeography developed by Kamp et al. has a range of important implications for the evolution of New Zealand terrestrial taxa over the past 30 Ma. Key aspects include the prolonged isolation of the biota on the North Island landmass from the larger and more diverse greater South Island, and the founding of North Island taxa from the potentially unusual ecosystem of a small island around Northland. The prolonged period of isolation is expected to have generated deep phylogenetic splits within taxa present on both islands, and an important current aim should be to identify such signals in surviving endemics to start building a picture of the historical phylogeography, and inferred ecology of both islands through the Cenozoic. Given the potential differences in founding terrestrial species and climatic conditions, it seems likely that the ecology may have been very diferent between the North and South Islands. New genetic data from the 10 or so species of extinct moa suggest that the radiation of moa was much more recent than previously suggested, and reveals a complex pattern that is inferred to result from the interplay of the Cenozoic biogeography, marine barriers, and glacial cycles.

Fractional models for the migration of biological cells in complex spatial domains

Travelling wave phenomena are observed in many biological applications. Mathematical theory of standard reaction-diffusion problems shows that simple partial differential equations exhibit travelling wave solutions with constant wavespeed and such models are used to describe, for example, waves of chemical concentrations, electrical signals, cell migration, waves of epidemics and population dynamics. However, as in the study of cell motion in complex spatial geometries, experimental data are often not consistent with constant wavespeed. Non-local spatial models have successfully been used to model anomalous diffusion and spatial heterogeneity in different physical contexts. In this paper, we develop a fractional model based on the Fisher-Kolmogoroff equation and analyse it for its wavespeed properties, attempting to relate the numerical results obtained from our simulations to experimental data describing enteric neural crest-derived cells migrating along the intact gut of mouse embryos. The model proposed essentially combines fractional and standard diffusion in different regions of the spatial domain and qualitatively reproduces the behaviour of neural crest-derived cells observed in the caecum and the hindgut of mouse embryos during in vivo experiments.

Mating compatibility among four pest members of the Bactrocera dorsalis fruit fly species complex (Diptera : Tephritidae)

Bactrocera dorsalis (Hendel), Bactrocera papayae Drew & Hancock, Bactrocera philippinensis Drew & Hancock, and Bactrocera carambolae Drew & Hancock are pest members within the B. dorsalis species complex of tropical fruit flies. The species status of these taxa is unclear and this confounds quarantine, pest management, and general research. Mating studies carried out under uniform experimental conditions are required as part of resolving their species limits. These four taxa were collected from the wild and established as laboratory cultures for which we subsequently determined levels of prezygotic compatibility, assessed by field cage mating trials for all pair-wise combinations. We demonstrate random mating among all pair-wise combinations involving B. dorsalis, B. papayae, and B. philippinensis. B. carambolae was relatively incompatible with each of these species as evidenced by nonrandom mating for all crosses. Reasons for incompatibility involving B. carambolae remain unclear; however, we observed differences in the location of couples in the field cage for some comparisons. Alongside other factors such as pheromone composition or other courtship signals, this may lead to reduced interspecific mating compatibility with B. carambolae. These data add to evidence that B. dorsalis, B. papayae, and B. philippinensis represent the same biological species, while B. carambolae remains sufficiently different to maintain its current taxonomic identity. This poses significant implications for this group's systematics, impacting on pest management, and international trade.

Proteins, drug targets and the mechanisms they control : the simple truth about complex networks

Realizing the promise of molecularly targeted inhibitors for cancer therapy will require a new level of knowledge about how a drug target is wired into the control circuitry of a complex cellular network. Here we review general homeostatic principles of cellular networks that enable the cell to be resilient in the face of molecular perturbations, while at the same time being sensitive to subtle input signals. Insights into such mechanisms may facilitate the development of combination therapies that take advantage of the cellular control circuitry, with the aim of achieving higher efficacy at a lower drug dosage and with a reduced probability of drug-resistance development.

Teleoperation of a 7 DOF humanoid robot arm using human arm accelerations and EMG signals

We present our work on tele-operating a complex humanoid robot with the help of bio-signals collected from the operator. The frameworks (for robot vision, collision avoidance and machine learning), developed in our lab, allow for a safe interaction with the environment, when combined. This even works with noisy control signals, such as, the operator’s hand acceleration and their electromyography (EMG) signals. These bio-signals are used to execute equivalent actions (such as, reaching and grasping of objects) on the 7 DOF arm.

Raman spectroscopy of some complex arsenate minerals—implications for soil remediation

The application of spectroscopy to the study of contaminants in soils is important. Among the many contaminants is arsenic, which is highly labile and may leach to non-contaminated areas. Minerals of arsenate may form depending upon the availability of specific cations for example calcium and iron. Such minerals include carminite, pharmacosiderite and talmessite. Each of these arsenate minerals can be identified by its characteristic Raman spectrum enabling identification.