Image Morphing

Morphing is also used in the gaming industry to add engaging animation to video games and computer games. However, morphing techniques are not limited only to entertainment purposes. Morphing is a powerful tool that can enhance many multimedia projects such as presentations, education, electronic book illustrations, and computer-based training

Evaluation of anti-depressant effect of lemon grass (Cymbopogon citratus) in albino mice

Background: Depression is a common serious psychiatric disorder and the available anti-depressant treatments are associated with many unwanted side-effects. Thus, various herbal products have been tried. The advantages of herbal treatments would include its complementary nature to the conventional treatment, thus making the latter a safer and cheaper option for depressive disorders. The objective of the present study was to evaluate the anti-depressant activity of lemon grass (Cymbopogon citratus) in albino mice and compare it with Imipramine.Methods: A total of 60 Swiss albino mice weighing around 20-40 g of either sex were divided into 10 groups (n=6). They were orally administered with tween 80, as a control, 20 mg/kg imipramine (standard), 5 mg/kg and 10 mg/kg C. citratus (test drugs), and combination of imipramine (10 mg/kg) and C. citratus (10 mg/kg).  Duration of immobility was observed for last 4 mins of total 6 mins period in groups 1-5 for forced swimming test (porsolt test) and groups 6-10 for tail suspension test each on 1st, 8th and 15th day and recorded as mean±standard error of the mean. Results were analyzed by one-way analysis of variance, followed by Tukey’s post-hoc test.Results: Lemon grass at the above doses significantly reduced the immobility time in both the tests compared with the control (<0.05). The reduction in the duration of immobility at the dose of 10 mg/kg was comparable to imipramine.Conclusions: The essential oil of lemon grass (C. citratus) has significant anti-depressant activity comparable to imipramine

Reducing Attack Surface of a Web Application by Open Web Application Security Project Compliance

The attack surface of a system is the amount of application area that is exposed to the adversaries. The overall vulnerability can be reduced by reducing the attack surface of a web application. In this paper, we have considered the web components of two versions of an in-house developed project management web application and the attack surface has been calculated prior and post open web application security project (OWASP) compliance based on a security audit to determine and then compare the security of this Project Management Application. OWASP is an open community to provide free tools and guidelines for application security. It was observed that the attack surface of the software reduced by 45 per cent once it was made OWASP compliant. The vulnerable surface exposed by the code even after OWASP compliance was due to the mandatory access points left in the software to ensure accessibility over a network.Defence Science Journal, 2012, 62(5), pp.324-330, DOI:http://dx.doi.org/10.14429/dsj.62.129

PRELIMINARY PHYTOCHEMICAL AND DIURETIC SCREENING OF ETHANOLIC AND AQUEOUS EXTRACT OF ZINGIBER OFFICINALE

The present study was to evaluate Diuretic activity of ethanolic and aqueous extracts of Zingiber officinale Rhizome in wistar rats. Ethanolic and aqueous extracts were administered to experimental rats orally at the doses of 500 mg/kg p.o.  Furosemide (5 mg/kg) was used as positive control in the study. The diuretic effect of the extract was evaluated by measuring urine volume &amp; sodium content. Urine volume was significantly increased by ethanolic extract in comparison to the aqueous and control group, while the excretion of sodium was also increased by extract. The ethanolic extract had the additional advantage over aqueous extract. We can conclude that ethanolic extract of Zingiber officinale produced notable diuretic effect which appeared to be comparable to that produced by the reference diuretic furosemide

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing