Showing papers in "Current Science in 1996"

Viral cell recognition and entry

Abstract: Rhinovirus infection is initiated by the recognition of a specific cell-surface receptor. The major group of rhinovirus serotypes attach to intercellular adhesion molecule-1 (ICAM-1). The attachment process initiates a series of conformational changes resulting in the loss of genomic RNA from the virion. X-ray crystallography and sequence comparisons suggested that a deep crevice or canyon is the site on the virus recognized by the cellular receptor molecule. This has now been verified by electron microscopy of human rhinovirus 14 (HRV14) and HRV16 complexed with a soluble component of ICAM-1. A hydrophobic pocket underneath the canyon is the site of binding of various hydrophobic drug compounds that can inhibit attachment and uncoating. This pocket is also associated with an unidentified, possibly cellular in origin, "pocket factor." The pocket factor binding site overlaps the binding site of the receptor. It is suggested that competition between the pocket factor and receptor regulates the conformational changes required for the initiation of the entry of the genomic RNA into the cell.

Seasonal variability in oxygen and nutrients in the central and eastern arabian sea

Abstract: Extensive observations made in the central and eastern Arabian Sea under JGOFS (India) programme suggest strong seasonal variations in concentrations of oxygen and nutrients in the water column. In the intermediate waters (depth range 150-800 m) oxygen concentrations were lowest during winter (with values near zero at ∼400 m) in relation to those in the other two seasons (intermonsoon and southwest monsoon). This together with nitrate distributions revealed the occurrence of intense reducing conditions in intermediate waters during winter because of the sluggish water movement and high surface productivity. Secondary nitrite, an indicator of occurrence of denitrification was present at oxygen levels < 10 μM. Nitrate deficits reached a maximum of 10 μM in winter, whereas, it was half this value in monsoon. The average deficits suggest increasing reducing conditions in the order monsoon (1.6 μM), intermonsoon (3.7 μM) and winter (4.0 μM).

Forcing mechanisms of the Bay of Bengal circulation

Abstract: A state-of-the-art ocean general circulation model, set up for the North Indian Ocean and driven by climatological wind stress simulates most of the observed features of the near-surface circulation of the Bay of Bengal. The prominent features of the annual cycle are an anticyclonic gyre with a poleward East India Coastal Current (EICC) during February-May, and an equatorward EICC during October-December, During the summer monsoon, the coastal current flows poleward in the south and equatorward in the north, To identify the principal mechanisms governing this cycle, we carried out experiments with modified winds, When spatially uniform wind stress was applied only over the Bay, the circulation is similar to, but weaker than the observed, and can be linked to two coastal Kelvin wave pulses which originate along the eastern boundary of the Bay during the summer and winter monsoons, When the Bay is forced with observed winds, the wind stress curl strengthens the poleward EICC during February-May and the equatorward EICC during October-December. The principal contribution of equatorial winds is to generate the equatorward coastal current during the summer monsoon off the east coast of India.

Documenting diversity: an experiment

Abstract: Complex natural systems such as soils, groundwater, and populations of disease vectors like mosquitoes exhibit a great deal of variation in space and time. Documenting such variation is essential for good management of environmental resources; it can also provide for students and teachers excellent opportunities for first hand scientific observations. This article describes such an experiment, focusing on inventorying and monitoring of biodiversity of the Western Ghats. Initiated in early 1994, this experiment involves a network of over 20 undergraduate colleges collaborating with ecologists at the Indian Institute of Science in Bangalore. Experience gained so far in this programme suggests that such field research could serve as a valuable teaching device, as well as generate useful inputs for decentralized management of natural resources at the panchayat and district levels.

Climate-change and agriculture - an indian perspective

Abstract: In this paper, we examine the major predictions made so far regarding the nature of climate change and its impacts on our region in the light of the known errors of the set of models and the observations over this century. The major predictions of the climate models about the impact of increased concentration of greenhouse gases ave at variance with the observations over the Indian region during the last century characterized by such increases and global warming. It is important to note that as far as the Indian region is concerned, the impact of year-to-year variation of the monsoon will continue to be dominant over longer period changes even in the presence of global warming. Recent studies have also brought out the uncertainties in the yields simulated by crop models. It is suggested that a deeper understanding of the links between climate and agricultural productivity is essential for generating reliable predictions of impact of climate change. Such an insight is also required for identifying cropping patterns and management practices which are tailored for sustained maximum yield in the face of the vagaries of the monsoon.

A novel neural network design for long range prediction of rainfall pattern

Abstract: The importance of long-range prediction of rainfall pattern for devising and planning agricultural strategies cannot be overemphasized. However, the prediction of rainfall pattern remains a difficult problem and the desired level of accuracy has not been reached. The conventional methods for prediction of rainfall use either dynamical or statistical modelling. In this article we report the results of a new modelling technique using artificial neural networks. Artificial neural networks are especially useful where the dynamical processes and their interrelations for a given phenomenon are not known with sufficient accuracy. Since conventional neural networks were found to be unsuitable for simulating and predicting rainfall patterns, a generalized structure of a neural network was then explored and found to provide consistent prediction (hindcast) of all-India annual mean rainfall with good accuracy. Performance and consistency of this network are evaluated and compared with those of other (conventional) neural networks. It is shown that the generalized network can make consistently good prediction of annual mean rainfall. Immediate application and potential of such a prediction system are discussed.

Heavy rainfall during monsoon season : point and spatial distribution

Abstract: The daily rainfall records of about 5000 stations in India, during the monsoon periods of several decades, have been examined for occurrence of heavy rainfall. The point values of (i) the highest annual rainfalls, (ii) the highest rainfalls of one, two, and three day durations, and (iii) the highest hourly values, are presented in the form of tables and charts. The highest depth-area-duration values for twelve storm-centres are also given.