IN VITRO ANTI OXIDANT ACTIVITY OF CHROMATOGRAPHICALLY SEPARATED FRACTIONS FROM THE LEAVES OF AGERATUM CONYZOIDES L

Synthetic anti oxidants are not safe for human health. It is often claimed that they may develop carcinoma in human body. Therefore, search for natural anti oxidants was going on and extended up to plant sources. Many medicinal plants are known having anti oxidant activity. Aageratum conyzoides Linn. is one such plant. In order to isolate anti oxidant compound (s) from the leaves of A. conyzoides L. the present study was undertaken. In isolation study silica gel G column chromatography of the powdered leaves of A.conyzoides L. was done when six fractions were separated. In vitro anti oxidant activity of these six fractions was measured by superoxide anion generation with help of xanthine-xanthine oxidase assay and with linoleic acid peroxidation assay as well as DPPH photometric assay. Results showed that fourth fraction had maximum anti oxidant activity. Inhibitory activities of xanthine oxidation, linoleic acid peroxidation and scavenging capacity of DPPH by the fourth fraction were respectively 96%, 97% and 96% whereas for other five fractions inhibitory activities were quite low. Anti oxidant activity is known to be associated with compounds like total phenol, flavonoids, ascorbic acid and carotenoids. These compounds were estimated in the separated six fractions after chromatography of powdered leaves of A. conyzoides L. Results showed that fourth fraction had total phenol, flavonoids, ascorbic acid and carotenoids in the concentrations of 58 mg/mg dry wt, 88 mg/mg dry wt, 22 mg/g dry wt and 25 mg/g dry wt respectively. The amounts were significantly higher in comparison to that of other fractions. In vitro anti oxidant activity of the fourth fraction was, therefore, related with high amounts of total phenol, flavonoids, ascorbic acid and carotenoids. Present study indicated that the separated fourth fraction after silica gel G column chromatography of powdered leaves of A.conyzoides L. may be used as natural anti oxidant

Protein sequence classification using feature hashing

Recent advances in next-generation sequencing technologies have resulted in an exponential increase in the rate at which protein sequence data are being acquired. The k-gram feature representation, commonly used for protein sequence classification, usually results in prohibitively high dimensional input spaces, for large values of k. Applying data mining algorithms to these input spaces may be intractable due to the large number of dimensions. Hence, using dimensionality reduction techniques can be crucial for the performance and the complexity of the learning algorithms. In this paper, we study the applicability of feature hashing to protein sequence classification, where the original high-dimensional space is "reduced" by hashing the features into a low-dimensional space, using a hash function, i.e., by mapping features into hash keys, where multiple features can be mapped (at random) to the same hash key, and "aggregating" their counts. We compare feature hashing with the "bag of k-grams" approach. Our results show that feature hashing is an effective approach to reducing dimensionality on protein sequence classification tasks

WildGEN: Long-horizon Trajectory Generation for Wildlife

Trajectory generation is an important concern in pedestrian, vehicle, and wildlife movement studies. Generated trajectories help enrich the training corpus in relation to deep learning applications, and may be used to facilitate simulation tasks. This is especially significant in the wildlife domain, where the cost of obtaining additional real data can be prohibitively expensive, time-consuming, and bear ethical considerations. In this paper, we introduce WildGEN: a conceptual framework that addresses this challenge by employing a Variational Auto-encoders (VAEs) based method for the acquisition of movement characteristics exhibited by wild geese over a long horizon using a sparse set of truth samples. A subsequent post-processing step of the generated trajectories is performed based on smoothing filters to reduce excessive wandering. Our evaluation is conducted through visual inspection and the computation of the Hausdorff distance between the generated and real trajectories. In addition, we utilize the Pearson Correlation Coefficient as a way to measure how realistic the trajectories are based on the similarity of clusters evaluated on the generated and real trajectories.Comment: 1st CIKM International Workshop on Knowledge Extraction and Management for Wildlife Conservation (InfoWild 2023