We demonstrate that golden hamsters infected with Leishmania donovani amastigotes develop the capacity to eliminate intracellular pathogens on treatment with low-dose standard antileishmanial sodium stibogluconate (Stibanate) in combination with polyinosinic-polycytidilic acid stabilized with polylysine and carboxymethycellulose (poly ICLC), a potent inducer of interferon (IFN) and immune enhancer, plus L-arginine. Data suggest that low doses of both Stibanate and poly ICLC plus L-arginine provide marginal inhibition against L. donovani infection in golden hamsters. When given in combination, however, a significant inhibition was achieved without toxicity, as all the animals survived up to 45 or 60 days. These results suggest that combination therapy using Stibanate and poly ICLC plus L-arginine may be very effective in reducing the dose of Stibanate and, hence, its dose-dependent toxicity in clinical situations.
There can be noise and uncertainty in the bug reports data as the bugs are reported by a heterogeneous group of users working across different countries. Bug description is an essential attribute that helps to predict other bug attributes, such as severity, priority, and time fixes. We need to consider the noise and confusion present in the text of the bug report, as it can impact the output of different machine learning techniques. Shannon entropy has been used in this paper to calculate summary uncertainty about the bug. Bug severity attribute tells about the type of impact the bug has on the functionality of the software. Correct bug severity estimation allows scheduling and repair bugs and hence help in resource and effort utilization. To predict the severity of the bug we need software project historical data to train the classifier. These training data are not always available in particular for new software projects. The solution which is called cross project prediction is to use the training data from other projects. Using bug priority, summary weight and summary entropy, we have proposed cross project bug severity assessment models. Results for proposed summary entropy based approach for bug severity prediction in cross project context show improved performance of the Accuracy and F-measure up to 70.23% and 93.72% respectively across all the machine learning techniques over existing work.
In the present study, series of laboratory experiments were conducted to investigate the effectiveness of perforated screens as energy dissipators in mixed triple wall mode in the case of small hydraulic structures. The shapes of openings for each layer of screens were either circular, square or triangular. Every layer of the screen had a porosity of 45% per unit depth of the screen. The experiments were conducted to dissipate the energy for supercritical flows of Froude number F1 ranging from 3.2 to 19.3. The screens were placed vertically with the first screen 1.5 m from the sluice gate and consecutive screen at a gap of 25 mm. The experiments showed that the energy of the supercritical flows can be dissipated effectively by using perforated screens. The difference in energy dissipation between the upstream and downstream of the screen was more significant than the energy dissipation caused by classical hydraulic jumps. Comparing the results of the present study with the previous researchers it is found that the energy loss in case of present study more than the previous researchers. The relative energy loss in the present study was found to be varying from 74 to 94%. The value of the Froude number downstream of the screen, F2, was varying from 1.1 to 1.81, with an average value of 1.35. Tailwater deficit parameter, D, if found to be varying from 0.66 to 0.90.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.