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Food, mood and health: a neurobiologic outlook
Prasad, C.;
Brazilian Journal of Medical and Biological Research , 1998, DOI: 10.1590/S0100-879X1998001200002
Abstract: hippocrates was the first to suggest the healing power of food; however, it was not until the medieval ages that food was considered a tool to modify temperament and mood, although scientific methods as we know them today were not in use at the time. modern scientific methods in neuroscience began to emerge much later, leading investigators to examine the role of diet in health, including mental well-being, with greater precision. this review shows how short- and long-term forced dietary interventions bring about changes in brain structure, chemistry, and physiology, leading to altered animal behavior. examples will be presented to show how diets alter brain chemistry, behavior, and the action of neuroactive drugs. most humans and most animal species examined in a controlled setting exhibit a fairly reproducible pattern of what and how they eat. recent data suggest that these patterns may be under the neurochemical and hormonal control of the organisms themselves. other data show that in many instances food may be used unconsciously to regulate mood by seemingly normal subjects as well as those undergoing drug withdrawal or experiencing seasonal affective disorders and obesity-related social withdrawal. we will discuss specific examples that illustrate that manipulation of dietary preference is actually an attempt to correct neurochemical make-up.
Food, mood and health: a neurobiologic outlook
Prasad C.
Brazilian Journal of Medical and Biological Research , 1998,
Abstract: Hippocrates was the first to suggest the healing power of food; however, it was not until the medieval ages that food was considered a tool to modify temperament and mood, although scientific methods as we know them today were not in use at the time. Modern scientific methods in neuroscience began to emerge much later, leading investigators to examine the role of diet in health, including mental well-being, with greater precision. This review shows how short- and long-term forced dietary interventions bring about changes in brain structure, chemistry, and physiology, leading to altered animal behavior. Examples will be presented to show how diets alter brain chemistry, behavior, and the action of neuroactive drugs. Most humans and most animal species examined in a controlled setting exhibit a fairly reproducible pattern of what and how they eat. Recent data suggest that these patterns may be under the neurochemical and hormonal control of the organisms themselves. Other data show that in many instances food may be used unconsciously to regulate mood by seemingly normal subjects as well as those undergoing drug withdrawal or experiencing seasonal affective disorders and obesity-related social withdrawal. We will discuss specific examples that illustrate that manipulation of dietary preference is actually an attempt to correct neurochemical make-up.
Deep Recurrent Neural Networks for Time Series Prediction
Sharat C. Prasad,Piyush Prasad
Computer Science , 2014,
Abstract: Ability of deep networks to extract high level features and of recurrent networks to perform time-series inference have been studied. In view of universality of one hidden layer network at approximating functions under weak constraints, the benefit of multiple layers is to enlarge the space of dynamical systems approximated or, given the space, reduce the number of units required for a certain error. Traditionally shallow networks with manually engineered features are used, back-propagation extent is limited to one and attempt to choose a large number of hidden units to satisfy the Markov condition is made. In case of Markov models, it has been shown that many systems need to be modeled as higher order. In the present work, we present deep recurrent networks with longer backpropagation through time extent as a solution to modeling systems that are high order and to predicting ahead. We study epileptic seizure suppression electro-stimulator. Extraction of manually engineered complex features and prediction employing them has not allowed small low-power implementations as, to avoid possibility of surgery, extraction of any features that may be required has to be included. In this solution, a recurrent neural network performs both feature extraction and prediction. We prove analytically that adding hidden layers or increasing backpropagation extent increases the rate of decrease of approximation error. A Dynamic Programming (DP) training procedure employing matrix operations is derived. DP and use of matrix operations makes the procedure efficient particularly when using data-parallel computing. The simulation studies show the geometry of the parameter space, that the network learns the temporal structure, that parameters converge while model output displays same dynamic behavior as the system and greater than .99 Average Detection Rate on all real seizure data tried.
A note on the flow induced by a constantly accelerating edge in an Oldroyd-B fluid
C. Fetecau,Sharat C. Prasad
International Journal of Mathematics and Mathematical Sciences , 2005, DOI: 10.1155/ijmms.2005.2677
Abstract: Analytical expressions for the velocity field and the tangential stresses that are induced due to a constantly accelerating edge in an Oldroyd-B fluid have been established for all values of material constants. The solutions that have been obtained satisfy the governing differential equations and all the imposed initial and boundary conditions. These solutions reduce to those for the Maxwell, second-grade, and Navier-Stokes fluid as limiting cases. Exact solutions such as those determined here for an unsteady problem serve a dual purpose. They have relevance to an interesting physical problem and the solutions can also be used to check the efficacy of the flows of such fluids in more complicated flow domains.
Functionalization of Cobalt Ferrite Nanoparticles with Alginate Coating for Biocompatible Applications  [PDF]
Prasad M. Tamhankar, Aparna M. Kulkarni, Shrikant C. Watawe
Materials Sciences and Applications (MSA) , 2011, DOI: 10.4236/msa.2011.29179
Abstract: The soft magnetic materials have potential applications in the field of bioengineering as carriers for targeted drug delivery. The magnetic properties, particle size after coating, Curie temperature and its biocompatibility are important parameters for the synthesis of materials. In the present communication cobalt ferrite nanoparticles have been synthesized using co-precipitation method and coated with sodium alginate. The X-ray diffraction and infrared spectroscopic measurements have been used to confirm the ferrite structure formation and coating of the samples with alginate. The SEM micrographs have been used to confirm the particle size which is found to be 45 nm before coating and 78 nm after coating. The saturation magnetization obtained using the hysteresis data for the uncoated cobalt ferrite sample is 19.8 emu/gm while for the coated sample it reduces to 10.2 emu/gm. The AC susceptibility measurements indicate SP structure for the uncoated samples with Curie temperature less than 100℃. The thermo gravimetric measurements have been used to estimate the amount of alginate coating on the sample and it has been correlated with retention of magnetic properties after coating. The value of saturation magnetization reduces after coating due to mass reduction of magnetic material in the sample in accordance with the TGA measurements.
MIMO System Reduction Using Modified Pole Clustering and Genetic Algorithm
C. B. Vishwakarma,R. Prasad
Modelling and Simulation in Engineering , 2009, DOI: 10.1155/2009/540895
Abstract: A new mixed method for reducing the order of the large-scale linear dynamic multi-input-multi-output (MIMO) systems has been presented. In this method, the common denominator polynomial of the reduced-order transfer function matrix is synthesized by using modified pole clustering while the coefficients of the numerator elements are computed by minimizing the integral square error between the time responses of the original and reduced system element using Genetic Algorithm. The modified pole clustering generates more dominant cluster centres than cluster centres obtained by pole clustering technique already available in literature. The proposed algorithm is computer-oriented and comparable in quality. This method guarantees stability of the reduced model if the original high-order system is stable. The algorithm of the proposed method is illustrated with the help of an example and the results are compared with the other well-known reduction techniques.
On the non-existence of some interpolatory polynomials
C. H. Anderson,J. Prasad
International Journal of Mathematics and Mathematical Sciences , 1986, DOI: 10.1155/s016117128600090x
Abstract: Here we prove that if xk, k=1,2, ¢ € |,n+2 are the zeros of (1 ¢ ’x2)Tn(x) where Tn(x) is the Tchebycheff polynomial of first kind of degree n, ±j, 2j, j=1,2, ¢ € |,n+2 and 3j, j=1,2, ¢ € |,n+1 are any real numbers there does not exist a unique polynomial Q3n+3(x) of degree ¢ ‰ ¤3n+3 satisfying the conditions: Q3n+3(xj)= ±j, Q3n+3(xj)= 2j, j=1,2, ¢ € |,n+2 and Q ¢ € ′3n+3(xj)= 3j, j=2,3, ¢ € |,n+1. Similar result is also obtained by choosing the roots of (1 ¢ ’x2)Pn(x) as the nodes of interpolation where Pn(x) is the Legendre polynomial of degree n.
Engineering Students Perception of Professor Role as a Leader: An Empirical Study
Shiva Prasad H.C
Lecture Notes in Engineering and Computer Science , 2011,
Abstract:
Fisher Information With Respect to Cumulants
S. Prasad,N. C. Menicucci
Physics , 2002, DOI: 10.1109/TIT.2004.825034
Abstract: Fisher information is a measure of the best precision with which a parameter can be estimated from statistical data. It can also be defined for a continuous random variable without reference to any parameters, in which case it has a physically compelling interpretation of representing the highest precision with which the first cumulant of the random variable, i.e., its mean, can be estimated from its statistical realizations. We construct a complete hierarchy of information measures that determine the best precision with which all of the cumulants of a random variable -- and thus its complete probability distribution -- can be estimated from its statistical realizations. Several properties of these information measures and their generating functions are discussed.
Dynamic Range Improvement of GMRT Low Frequency Images
Peeyush Prasad,C. R. Subrahmanya
Physics , 2011,
Abstract: This paper outlines some new observational and data processing techniques for enhancing the dynamic range of low frequency images obtained with the Giant Metrewave Radio Telescope. We illustrate new software tools developed to facilitate visibility editing and calibration as well as other preprocessing required to enhance the dynamic range of images from a planned survey.
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