Signatures of Higher Functions of Brain Dynamics in Electroencephalogram: A Nonlinear Analysis

The brain with its highly complex structure made up of simple units,imterconnected information pathways and specialized functions has always been an object of mystery and sceintific fascination for physiologists,neuroscientists and lately to mathematicians and physicists. The stream of biophysicists are engaged in building the bridge between the biological and physical sciences guided by a conviction that natural scenarios that appear extraordinarily complex may be tackled by application of principles from the realm of physical sciences. In a similar vein, this report aims to describe how nerve cells execute transmission of signals ,how these are put together and how out of this integration higher functions emerge and get reflected in the electrical signals that are produced in the brain.Viewing the E E G Signal through the looking glass of nonlinear theory, the dynamics of the underlying complex system-the brain ,is inferred and significant implications of the findings are explored.

Development and Evaluation of Blind Identification Techniques for Nonlinear Systems

Identification and Control of Non‐linear dynamical systems are challenging problems to the control engineers.The topic is equally relevant in communication,weather prediction ,bio medical systems and even in social systems,where nonlinearity is an integral part of the system behavior.Most of the real world systems are nonlinear in nature and wide applications are there for nonlinear system identification/modeling.The basic approach in analyzing the nonlinear systems is to build a model from known behavior manifest in the form of system output.The problem of modeling boils down to computing a suitably parameterized model,representing the process.The parameters of the model are adjusted to optimize a performanace function,based on error between the given process output and identified process/model output.While the linear system identification is well established with many classical approaches,most of those methods cannot be directly applied for nonlinear system identification.The problem becomes more complex if the system is completely unknown but only the output time series is available.Blind recognition problem is the direct consequence of such a situation.The thesis concentrates on such problems.Capability of Artificial Neural Networks to approximate many nonlinear input-output maps makes it predominantly suitable for building a function for the identification of nonlinear systems,where only the time series is available.The literature is rich with a variety of algorithms to train the Neural Network model.A comprehensive study of the computation of the model parameters,using the different algorithms and the comparison among them to choose the best technique is still a demanding requirement from practical system designers,which is not available in a concise form in the literature.The thesis is thus an attempt to develop and evaluate some of the well known algorithms and propose some new techniques,in the context of Blind recognition of nonlinear systems.It also attempts to establish the relative merits and demerits of the different approaches.comprehensiveness is achieved in utilizing the benefits of well known evaluation techniques from statistics. The study concludes by providing the results of implementation of the currently available and modified versions and newly introduced techniques for nonlinear blind system modeling followed by a comparison of their performance.It is expected that,such comprehensive study and the comparison process can be of great relevance in many fields including chemical,electrical,biological,financial and weather data analysis.Further the results reported would be of immense help for practical system designers and analysts in selecting the most appropriate method based on the goodness of the model for the particular context.

Remote Sensing the Signatures of Upwelling in South Eastern Arabian Sea

In this thesis, a variety of available satellite data products have been made use of to bring out a synergistic analysis on the upwelling phenomenon in SEAS. Basic concepts of remote sensing, upwelling and linked oceanography topics have been dealt in this work .Auxiliary data products utilized in this study are described in chapter 2. The climatological monthly variability of the upwelling signatures are detailed under chapter 3. Chapter 4 presents the forcing factors that trigger the upwelling process in SEAS. Chapter 5 describes the oceanic response to the forcing factors with respect to the SST cooling and CHLA blooms. Chapter 6 presents the heat budget of the region and the variability of heat budget terms with respect to upwelling. Chapter 7 describes the inter-annual variability of upwelling intensity in SEAS and the influence of climatic events on upwelling.

A Prototype for a Multimodal Biometric Security system based on Face and Audio Signatures

Any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the claimed identity of an individual, referred to as biometrics, has gained significant interest in the wake of heightened concerns about security and rapid advancements in networking, communication and mobility. Multimodal biometrics is expected to be ultra-secure and reliable, due to the presence of multiple and independent—verification clues. In this study, a multimodal biometric system utilising audio and facial signatures has been implemented and error analysis has been carried out. A total of one thousand face images and 250 sound tracks of 50 users are used for training the proposed system. To account for the attempts of the unregistered signatures data of 25 new users are tested. The short term spectral features were extracted from the sound data and Vector Quantization was done using K-means algorithm. Face images are identified based on Eigen face approach using Principal Component Analysis. The success rate of multimodal system using speech and face is higher when compared to individual unimodal recognition systems

Biomarker pigment signatures in Cochin back water system e A tropical estuary south west coast of India

Sedimentary biomarker pigments around Cochin estuary situated in the southwest coast of India were determined by HPLC. Fucoxanthin, an indicator of diatom was observed to be the most abundant carotenoid pigment in the estuary. Dinoflagellate derived carotenoid pigment peridinin was confined in the southern part of estuary and zeaxanthin pigment indicative of cyanobacteria were more found in sites influenced by anthropogenic activities. One compound having close similarity to fucoxanthin was also detected. Alloxanthin (cryptophyceae), chl b (green algae), canthaxanthin, neoxanthin, lutein and peridinin isomer were also detected by spectra and corresponding algal class were identified. The highest concentration of chl a (11.01 mg g 1) found near to the anthropogenic affected area while the lowest chl a (0.65 mg g 1) was recorded in industrial area. Degradation products of chl a, such as pheophorbide and pheophytin were observed and principal mode of mechanism of degradation were derived. Higher pheopigments content than chl a, reflects a density trapping of dead cells and early degradation of phytopigments from grazing activities

“Unmixing” Tissue Gene Expression Signatures from Tumor Biopsies

Emergent molecular measurement methods, such as DNA microarray, qRTPCR, and many others, offer tremendous promise for the personalized treatment of cancer. These technologies measure the amount of specific proteins, RNA, DNA or other molecular targets from tumor specimens with the goal of “fingerprinting” individual cancers. Tumor specimens are heterogeneous; an individual specimen typically contains unknown amounts of multiple tissues types. Thus, the measured molecular concentrations result from an unknown mixture of tissue types, and must be normalized to account for the composition of the mixture. For example, a breast tumor biopsy may contain normal, dysplastic and cancerous epithelial cells, as well as stromal components (fatty and connective tissue) and blood and lymphatic vessels. Our diagnostic interest focuses solely on the dysplastic and cancerous epithelial cells. The remaining tissue components serve to “contaminate” the signal of interest. The proportion of each of the tissue components changes as a function of patient characteristics (e.g., age), and varies spatially across the tumor region. Because each of the tissue components produces a different molecular signature, and the amount of each tissue type is specimen dependent, we must estimate the tissue composition of the specimen, and adjust the molecular signal for this composition. Using the idea of a chemical mass balance, we consider the total measured concentrations to be a weighted sum of the individual tissue signatures, where weights are determined by the relative amounts of the different tissue types. We develop a compositional source apportionment model to estimate the relative amounts of tissue components in a tumor specimen. We then use these estimates to infer the tissuespecific concentrations of key molecular targets for sub-typing individual tumors. We anticipate these specific measurements will greatly improve our ability to discriminate between different classes of tumors, and allow more precise matching of each patient to the appropriate treatment

Enfermedad tiroidea autoinmune en pacientes colombianos con lupus eritematoso sistémico

Objetivos: Determinar la prevalencia y los factores asociados con el desarrollo de hipotiroidismo autoinmune (HA) en una cohorte de pacientes con lupus eritematoso sistémico (LES), y analizar la información actual en cuanto a la prevalencia e impacto de la enfermedad tiroidea autoinmune y la autoinmunidad tiroidea en pacientes con LES. Métodos: Este fue un estudio realizado en dos pasos. Primero, un total de 376 pacientes con LES fueron evaluados sistemáticamente por la presencia de: 1) HA confirmado, 2) positividad para anticuerpos tiroperoxidasa/tiroglobulina (TPOAb/TgAb) sin hipotiroidismo, 3) hipotiroidismo no autoinmune, y 4) pacientes con LES sin hipotiroidismo ni positividad para TPOAb/TgAb. Se construyeron modelos multivariados y árboles de regresión y clasificación para analizar los datos. Segundo, la información actual fue evaluada a través de una revisión sistemática de la literatura (RLS). Se siguieron las guías PRISMA para la búsqueda en las bases de datos PubMed, Scopus, SciELO y Librería Virtual en Salud. Resultados: En nuestra cohorte, la prevalencia de HA confirmado fue de 12% (Grupo 1). Sin embargo, la frecuencia de positividad para TPOAb y TgAb fue de 21% y 10%, respectivamente (Grupo 2). Los pacientes con LES sin HA, hipotiroidismo no autoinmune ni positividad para TPOAb/TgAb constituyeron el 40% de la corhorte. Los pacientes con HA confirmada fueron estadísticamente significativo de mayor edad y tuvieron un inicio tardío de la enfermedad. El tabaquismo (ORA 6.93, IC 95% 1.98-28.54, p= 0.004), la presencia de Síndrome de Sjögren (SS) (ORA 23.2, IC 95% 1.89-359.53, p= 0.015) y la positividad para anticuerpos anti-péptido cíclico citrulinado (anti-CCP) (ORA 10.35, IC 95% 1.04-121.26, p= 0.047) se asociaron con la coexistencia de LES-HA, ajustado por género y duración de la enfermedad. El tabaquismo y el SS fueron confirmados como factores predictivos para LES-HA (AUC del modelo CART = 0.72). En la RSL, la prevalencia de ETA en LES varío entre 1% al 60%. Los factores asociados con esta poliautoinmunidad fueron el género femenino, edad avanzada, tabaquismo, positividad para algunos anticuerpos, SS y el compromiso articular y cutáneo. Conclusiones: La ETA es frecuente en pacientes con LES, y no afecta la severidad del LES. Los factores de riesgo identificados ayudarán a los clínicos en la búsqueda de ETA. Nuestros resultados deben estimular políticas para la suspensión del tabaquismo en pacientes con LES.

Tactile perception of rhythmic patterns in normal, blind, deaf and aphasic children

This paper is a review of a study to determine if perception through rhythm is contingent upon auditory experience.

Chemical composition observed over the mid-Atlantic and the detection of pollution signatures far from source regions

The atmospheric composition of the central North Atlantic region has been sampled using the FAAM BAe146 instrumented aircraft during the Intercontinental Transport of Ozone and Precursors (ITOP) campaign, part of the wider International Consortium for Atmospheric Research on Transport and Transformation (ICARTT). This paper presents an overview of the ITOP campaign. Between late July and early August 2004, twelve flights comprising 72 hours of measurement were made in a region from approximately 20 to 40°W and 33 to 47°N centered on Faial Island, Azores, ranging in altitude from 50 to 9000 m. The vertical profiles of O3 and CO are consistent with previous observations made in this region during 1997 and our knowledge of the seasonal cycles within the region. A cluster analysis technique is used to partition the data set into air mass types with distinct chemical signatures. Six clusters provide a suitable balance between cluster generality and specificity. The clusters are labeled as biomass burning, low level outflow, upper level outflow, moist lower troposphere, marine and upper troposphere. During this summer, boreal forest fire emissions from Alaska and northern Canada were found to provide a major perturbation of tropospheric composition in CO, PAN, organic compounds and aerosol. Anthropogenic influenced air from the continental boundary layer of the USA was clearly observed running above the marine boundary layer right across the mid-Atlantic, retaining high pollution levels in VOCs and sulfate aerosol. Upper level outflow events were found to have far lower sulfate aerosol, resulting from washout on ascent, but much higher PAN associated with the colder temperatures. Lagrangian links with flights of other aircraft over the USA and Europe show that such signatures are maintained many days downwind of emission regions. Some other features of the data set are highlighted, including the strong perturbations to many VOCs and OVOCs in this remote region.

Understanding electron heat flux signatures in the solar wind

Suprathermal electrons (E > 80 eV) carry heat flux away from the Sun. Processes controlling the heat flux are not well understood. To gain insight into these processes, we model heat flux as a linear dependence on two independent parameters: electron number flux and electron pitch angle anisotropy. Pitch angle anisotropy is further modeled as a linear dependence on two solar wind components: magnetic field strength and plasma density. These components show no correlation with number flux, reinforcing its independence from pitch angle anisotropy. Multiple linear regression applied to 2 years of Wind data shows good correspondence between modeled and observed heat flux and anisotropy. The results suggest that the interplay of solar wind parameters and electron number flux results in distinctive heat flux dropouts at heliospheric features like plasma sheets but that these parameters continuously modify heat flux. This is inconsistent with magnetic disconnection as the primary cause of heat flux dropouts. Analysis of fast and slow solar wind regimes separately shows that electron number flux and pitch angle anisotropy are equally correlated with heat flux in slow wind but that number flux is the dominant correlative in fast wind. Also, magnetic field strength correlates better with pitch angle anisotropy in slow wind than in fast wind. The energy dependence of the model fits suggests different scattering processes in fast and slow wind.

Forward modelling to determine the observational signatures of white-light imaging and interplanetary scintillation for the propagation of an interplanetary shock in the ecliptic plane

Recent coordinated observations of interplanetary scintillation (IPS) from the EISCAT, MERLIN, and STELab, and stereoscopic white-light imaging from the two heliospheric imagers (HIs) onboard the twin STEREO spacecraft are significant to continuously track the propagation and evolution of solar eruptions throughout interplanetary space. In order to obtain a better understanding of the observational signatures in these two remote-sensing techniques, the magnetohydrodynamics of the macro-scale interplanetary disturbance and the radio-wave scattering of the micro-scale electron-density fluctuation are coupled and investigated using a newly constructed multi-scale numerical model. This model is then applied to a case of an interplanetary shock propagation within the ecliptic plane. The shock could be nearly invisible to an HI, once entering the Thomson-scattering sphere of the HI. The asymmetry in the optical images between the western and eastern HIs suggests the shock propagation off the Sun–Earth line. Meanwhile, an IPS signal, strongly dependent on the local electron density, is insensitive to the density cavity far downstream of the shock front. When this cavity (or the shock nose) is cut through by an IPS ray-path, a single speed component at the flank (or the nose) of the shock can be recorded; when an IPS ray-path penetrates the sheath between the shock nose and this cavity, two speed components at the sheath and flank can be detected. Moreover, once a shock front touches an IPS ray-path, the derived position and speed at the irregularity source of this IPS signal, together with an assumption of a radial and constant propagation of the shock, can be used to estimate the later appearance of the shock front in the elongation of the HI field of view. The results of synthetic measurements from forward modelling are helpful in inferring the in-situ properties of coronal mass ejection from real observational data via an inverse approach.