Antipyretic and antibacterial activity of Chloranthus erectus (Buch.-Ham.) Verdcourt leaf extract: A popular folk medicine of Arunachal Pradesh

Objective : The main objective of this work was to study the antipyretic and antibacterial activity of C. erectus (Buch.-Ham.) Verdcourt leaf extract in an experimental albino rat model. Materials and Methods : The methanol extract of C. erectus leaf (MECEL) was evaluated for its antipyretic potential on normal body temperature and Brewers yeast-induced pyrexia in albino rats model. While the antibacterial activity of MECEL against five Gram (-) and three Gram () bacterial strains and antimycotic activity was investigated against four fungi using agar disk diffusion and microdilution methods. Result : Yeast suspension (10 mL/kg b.w.) elevated rectal temperature after 19 h of subcutaneous injection. Oral administration of MECEL at 100 and 200 mg/kg b.w. showed significant reduction of normal rectal body temperature and yeast-provoked elevated temperature (38.8 0.2 and 37.6 0.4, respectively, at 2-3 h) in a dose-dependent manner, and the effect was comparable to that of the standard antipyretic drug-paracetamol (150 mg/kg b.w.). MECEL at 2 mg/disk showed broad spectrum of growth inhibition activity against both groups of bacteria. However, MECEL was not effective against the yeast strains tested in this study. Conclusion : This study revealed that the methanol extract of C. erectus exhibited significant antipyretic activity in the tested models and antibacterial activity as well, and may provide the scientific rationale for its popular use as antipyretic agent in Khamptiss folk medicines.

Mining sentiments from songs using latent dirichlet allocation

Song-selection and mood are interdependent. If we capture a song’s sentiment, we can determine the mood of the listener, which can serve as a basis for recommendation systems. Songs are generally classified according to genres, which don’t entirely reflect sentiments. Thus, we require an unsupervised scheme to mine them. Sentiments are classified into either two (positive/negative) or multiple (happy/angry/sad/...) classes, depending on the application. We are interested in analyzing the feelings invoked by a song, involving multi-class sentiments. To mine the hidden sentimental structure behind a song, in terms of “topics”, we consider its lyrics and use Latent Dirichlet Allocation (LDA). Each song is a mixture of moods. Topics mined by LDA can represent moods. Thus we get a scheme of collecting similar-mood songs. For validation, we use a dataset of songs containing 6 moods annotated by users of a particular website.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.