Phytochemical investigations on the therapeutic properties of Ensete glaucum (Roxb.) Cheesman

68-73The traditional Khasi tribal community of North-East India cite the use of pseudostem sap from Ensete glaucum (Roxb.) Cheesman for therapeutic purpose, especially for diarrhoea. This preliminary study has been conducted to evaluate the curative properties of Ensete glaucum pseudostem sap by screening for the presence of amino acids, cardiac glycosides, flavonoids, polyphenols, alkaloids, reducing sugars, starch, saponins, tannins, terpenoids and oils and fats. Standard tests confirmed the presence of flavonoids, reducing sugars, terpenoids, saponins, cardiac glycosides and alkaloids, which together contribute to the curative property of the sap as discussed. Polyphenol content was found to be 10.59 mg GAE mL-1 and total antioxidant capacity estimated is 54.538 mg AAE mL-1, whereas, total flavonoids were measured at 2.52 mg QE mL-1 of fresh sap

Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions

Seasonal effect on physiological, reproductive and fertility profiles in breeding mithun bulls

Objective: To analyse the seasonal effect on physiological parameters, reproductive profiles and in vitro fertility in breeding mithun bulls.Methods: A total of ten adult mithun bulls age of 5 to 6 years old with good body condition (score 5-6) were selected from ICAR-NRC on Mithun, Jharnapani, Nagaland, India. The seasons were categorised into winter, spring, summer and autumn seasons based on the meteorological data and sunshine hours. The physiological parameters, reproductive profiles and in vitro fertility parameters were assessed during different seasons in mithun under the semi-intensive system of management.Results: The statistical analysis revealed that these experimental parameters were differed significantly (P<0.05) among the seasons and in overall spring and winter seasons were more beneficial in mithun breeding programme, although, the breeding in mithun occurred throughout the year with variation.Conclusions: It is concluded that collection & preservation of mithun semen and artificial insemination in mithun species during the season of spring and winter has significant beneficial effect in terms of semen production, freezability and fertility for artificial breeding programme in mithun under the semi-intensive system

Multidimensional signals and analytic flexibility : estimating degrees of freedom in human-speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions