Male remating in Drosophila ananassae: evidence for interstrain variation for remating time and shorter duration of copulation in second mating

ABSTRACT—In Drosophila ananassae, male remating was studied using ten mass culture stocks which were initiated from flies collected from different geographic localities. Male remating occurs at a high fre-quency and varies within narrow limits (84–96 percent) in different strains. Interestingly, male remating time (in min) varies from 7.41 (Bhutan) to 21.59 (PAT) in different strains and the variation is highly significant. Further, the results also show that males copulate for shorter duration during second mating. This is the first report in the genus Drosophila which provides evidence for interstrain variations for male remating time as well as for shorter duration of copulation during second mating as compared to first mating in D. ananassae

Female remating in Drosophila ananassae: effect of density on female remating frequency

Drosophila ananassae, a cosmopolitan and domestic species, is largely cir-cumtropicalin distribution and belongs to the ananassae species complex of the ananassae subgroup of the melanogaster species group. In the present study, experiments were conducted to investigate the effect of density on fe-male remating frequency by employing different wild-type and mutant strains of D. ananassae. Two experimental designs, i.e., 2-h daily observation and continuous confinement, were used. The results show that there is significant dependence of remating frequency on density in all strains tested under both experimental designs except in a wild-type strain (Bhutan), which shows no dependence of remating frequency on density under 2-h daily observation de-sign. This finding provides evidence that density may increase the frequency of female remating in D. ananassae

Female remating in Drosophila ananassae: bidirectional selection for remating speed

In Drosophila ananassae, artificial selection was carried out for fast and slow remating speed for 10 generations. Response to selection resulted in rapid divergence in remating time in each of two replicates of both fast and slow lines. There were significant differences in mean remat-ing time in females among fast, slow, and control lines. Regression coefficients for both fast and slow lines are significantly different from zero. The realized heritability over 10 genera-tions of selection is from 0.26 to 0.33 for two replicates of fast line and from 0.23 to 0.27 for two replicates of slow line. These findings suggest that female remating time in D. ananassae is under polygenic control. Remating frequency of females showed a correlated response in both fast and slow lines. At generation 10, correlated response to selection was also investigated. Mating propensity of D. ananassae of fast and slow lines was observed in an Elens-Wattiaux mating chamber. Fifteen pairs per test showed that on the average, the fast lines (11.20, 11.60) were more successful in mating than those of slow (6.40, 5.60) and control (8.00) lines. Pro-ductivity of once-mated females was measured in terms of number of progeny produced per fe-male and the results of productivity analysis indicate that females of fast lines (157.83, 130.83) produced more progeny compared with slow (72.70, 85.83) and control (109.23) lines

A Novel Approach to Distributed Multi-Class SVM

With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research proposes a novel algorithm that implements the Support Vector Machine over a multi-class dataset and is efficient in a distributed environment (here, Hadoop). The idea is to divide the dataset into half recursively and thus compute the optimal Support Vector Machine for this half during the training phase, much like a divide and conquer approach. While testing, this structure has been effectively exploited to significantly reduce the prediction time. Our algorithm has shown better computation time during the prediction phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the dataset grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms.Comment: 8 Page