Plasmodium ovale--a case report from Gujarat.
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Adult↗
Biomedical subjects
A Marathe
Publications and source records attributed to A Marathe.
Human babesiosis--a case report.
Babesiosis is an emerging, tick-transmitted, zoonotic disease caused by hematotropic parasites of the genus Babesia. Most cases of Babesial infections in humans have been acquired in temperate regions of the United States, Europe, France and England. A few cases of Babesiosis have been described in other parts of the world, including China, Taiwan, Egypt, South Africa, and Mexico.1,2 We report the first case of human Babesiosis, in a normosplenic, previously healthy individual from India.
Animals↗
On combinatorial DNA word design.
We consider the problem of designing DNA codes, namely sets of equi-length words over the alphabet [A, C, G, T] that satisfy certain combinatorial constraints. This problem is motivated by the task of reliably storing and retrieving information in synthetic DNA strands for use in DNA computing or as molecular bar codes in chemical libraries. The primary constraints that we consider, defined with respect to a parameter d, are as follows: for every pair of words w, x in a code, there are at least d mismatches between w and x if w not equal x and also between the reverse of w and the Watson-Crick complement of x. Extending classical results from coding theory, we present several upper and lower bounds on the maximum size of such DNA codes and give methods for constructing such codes. An additional constraint that is relevant to the design of DNA codes is that the free energies and enthalpies of the code words, and thus the melting temperatures, be similar. We describe dynamic programming algorithms that can (a) calculate the total number of words of length n whose free energy value, as approximated by a formula of Breslauer et al. (1986) falls in a given range, and (b) output a random such word. These algorithms are intended for use in heuristic algorithms for constructing DNA codes.
Algorithms↗
Discovery of association rules in medical data.
Data mining is a technique for discovering useful information from large databases. This technique is currently being profitably used by a number of industries. A common approach for information discovery is to identify association rules which reveal relationships among different items. In this paper, we use this approach to analyse a large database containing medical-record data. Our aim is to obtain association rules indicating relationships between procedures performed on a patient and the reported diagnoses. Random sampling was used to obtain these association rules. After reviewing the basic concepts associated with data mining, we discuss our approach for identifying association rules and report on the rules generated.
Algorithms↗