Neurofibroma presenting as clitoromegaly

Congenital clitoromegaly is common, but acquired clitoromegaly due to neurofibroma is rare. Here we present a case report of Neurofibroma presenting as clitoromegaly. A 7 year female had history of painless clitoromegaly since 3 years. Her hormonal assays and radiological examination of abdomen and pelvis did not reveal any abnormality. She was subjected to clitoroplasty for correction of clitoromegaly

Evaluation of Neuroprotective effect of medicinal plants in Drosophila melanogaster model

In this study, we investigated the neuroprotective effects of ethanolic extract of Bombax ceiba (EEBC) and ethanolic extract of Gymnemasylvestre (EEGS) against the toxicity induced by rotenone (ROT) in Drosophila melanogaster. Materials and Methods:Adult wild-type flies were concomitantly exposed to ROT (500 μM), EEBC (0.05% w/v and 0.1% w/v) and EEGS (0.05% w/v and 0.1% w/v) in the culture medium for 7 days. Results: ROT treated flies produced marked decreased in locomotor performance (i.e., climbing capability) in the negative geotaxis assay when compared to control group. EEBC and EEGS flies after treatment offered protection (24-42%) against the ROT-induced locomotor impairment in the negative geotaxis assay suggesting attenuation of ROT-induced locomotor deficits. Conclusion: The results of this study suggest that EEBC and EEGS were effective in reducing the toxicity induced by ROT in D. melanogaster as well as it confirms the significance of this model to explore possible therapeutic approaches in Parkinson’s disease (PD)

Implementation of Query Optimization for Reducing Run Time

Query optimization is the process of selecting the most efficient query-evaluation plan from many strategies so, In this paper  we have developed a technique that performs query optimization at compile-time to reduce the burden of optimization at run-time to improve the performance of the code execution. using histograms that are computed from the data and these histograms are used to get the estimate of selectivity for query joins and predicates in a query at compile-time. With these estimates, a query plan is constructed at compile-time and executed it at run-time Keywords: runtime, query optimization ,compile time histogra

Evaluation of anti-Parkinson’s activity of ethanolic extract of Tridax procumbens (Asteraceae)

9-17Parkinson's disease (PD) is the second most common neurodegenerative disease characterized by loss of dopaminergic neurons in the substantia nigra. In the present study was designed to evaluate anti-Parkinson’s activity of ethanolic extract of Tridax procumbens (EETP) leaves, family Asteraceae. The experimental paradigm included haloperidol-induced catalepsy in rat model and rotenone-induced locomotor impairment in the fruit fly. In the catalepsy model, the rats received treatment of EETP (100 and 200 mg/kg, p.o.) followed by haloperidol (1 mg/kg, i.p.) for 15 days. The significant (P p.o.). The catalase and reduced glutathione levels were found to be significantly (P P <0.05) improved the performances of locomotor activity in flies when compared with ROT treated flies. Thus, the study proved that EETP treatment significantly attenuated motor defects and also protected the brain from oxidative stress

A Review on Computing Semantic Similarity of Concepts in Knowledge Graphs

Semantic similarity is a metric defined over a set of documents or terms, where the idea of distance between them is based on the likeness of their meaning or semantic content as opposed to similarity which can be estimated regarding their syntactical representation (e.g. their string format). One of the drawbacks of conventional knowledge-based approaches (e.g. path or lch) in addressing such task is that the semantic similarity of any two concepts with the same path length is the same (uniform distance problem).To propose a weighted path length (wpath) method to combine both path length and IC in measuring the semantic similarity between concepts. The IC of two concepts� LCS is used to weight their shortest path length so that those concept pairs having same path length can have different semantic similarity score if they have different LCS

A Survey Paper on Online Shortest Path Computation for Users Based on Traffic Analysis

The online briefest way issue goes for figuring the briefest way in light of live activity circumstances. This is imperative in cutting edge auto route frameworks as it helps drivers to settle on sensible choices. To our best information, there is no effective framework/arrangement that can offer reasonable expenses at both customer and server sides for online most brief way calculation. Sadly, the traditional customer server construction modeling scales inadequately with the quantity of customers. A promising methodology is to let the server gather live activity data and afterward telecast them over radio or remote system. This methodology has astounding adaptability with the quantity of customers. Consequently, we build up another system called live movement file (LTI) which empowers drivers to rapidly and adequately gather the live activity data on the television station. An amazing result is that the driver can register/overhaul their most limited way come about by getting just a little part of the list. The trial study demonstrates that LTI is hearty to different parameters and it offers generally short tune-in expense (at customer side), quick question reaction time (at customer side), little show size (at server side), and light upkeep time (at server side) for online most limited way issue

Acute thrombocytopenia due to meropenem: a case report

Meropenem is a broad spectrum antibacterial drug from the carbapenem family. It is commonly used as an empirical antimicrobial in severe infections like pneumonia, intra-abdominal infections, septicaemia, meningitis etc. Severe adverse effects with meropenem are rare (<1 %) which include hypersensitivity and hematologic adverse effects. In the literature, few cases have been reported of meropenem induced thrombocytopenia that has shown the immune mechanism by which thrombocytopenia occurs. Drug induced thrombocytopenia (DITP) can be a life threatening condition if not diagnosed and managed properly. As meropenem is being widely used in hospitals nowadays, it is important to be aware of the rare but serious adverse effects it causes

Query Optimization to Improve Performance of the Code Execution

Object-Oriented Programming (OOP) is one of the most successful techniques for abstraction. Bundling together objects into collections of objects, and then operating on these collections, is a fundamental part of main stream object-oriented programming languages. Object querying is an abstraction of operations over collections, whereas manual implementations are performed at low level which forces the developers to specify how a task must be done. Some object-oriented languages allow the programmers to express queries explicitly in the code, which are optimized using the query optimization techniques from the database domain. In this regard, we have developed a technique that performs query optimization at compile-time to reduce the burden of optimization at run-time to improve the performance of the code execution. Keywords- Querying; joins; compile time; run-time; histograms; query optimizatio

HMCMA: Design of an Efficient Model with Hybrid Machine Learning in Cyber security for Enhanced Detection of Malicious Activities

In the rapidly evolving landscape of cyber security, the incessant advancement of malicious activities presents a formidable challenge, necessitating a paradigm shift in detection methodologies. Traditional methods, primarily reliant on static rule-based systems, exhibit palpable limitations in grappling with the dynamic and sophisticated nature of modern cyber threats. This inadequacy underscores the urgent need for innovative approaches that can adeptly adapt and respond to the ever-changing threat environment. Addressing this exigency, the present research introduces a novel hybrid machine learning model, ingeniously crafted to transcend the constraints of existing malicious activity detection frameworks. The proposed model synergizes the strengths of diverse machine learning strategies, including anomaly detection techniques including Isolation Forest and One-Class SVM, and validates the results of these classifiers using Random Forest and Gradient Boosting operations. The validated malware instances are classified into malware types using fusion of Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) based Recurrent Neural Networks (RNNs) under real-time network configuration sets. This eclectic amalgamation not only leverages the unique capabilities of each algorithm but also harmonizes them to forge a more robust and precise detection mechanisms. The strategic integration of these algorithms facilitates a comprehensive analysis of network traffic and system logs, thereby significantly enhancing the detection accuracy. Furthermore, the model's adaptive learning component ensures its relevance and efficacy in the face of evolving cyber threats, a quintessential feature for contemporary cyber security solutions. Empirical evaluations, conducted using multiple malware datasets and samples, substantiate the model's superiority over existing methods. It exhibited a remarkable 10.4% improvement in precision, an 8.5% increase in accuracy, a 4.9% enhancement in recall, an 8.3% rise in AUC, a 4.5% boost in specificity, and a notable 2.5% reduction in detection delay. These compelling results underscore the model's potential in revolutionizing malicious activity detection, providing organizations with a more effective and resilient defense mechanism against a spectrum of cyber threats. The research culminates in a significant stride forward in cyber security, offering a robust, adaptive, and comprehensive solution that addresses the pressing need for advanced malicious activity detection, thereby bolstering the overall cyber security posture of organizations in the digital age sets

A Review on Personalized Tag based Image based Search Engines

The development of social media based on Web 2.0, amounts of images and videos spring up everywhere on the Internet. This phenomenon has brought great challenges to multimedia storage, indexing and retrieval. Generally speaking, tag-based image search is more commonly used in social media than content based image retrieval and content understanding. Thanks to the low relevance and diversity performance of initial retrieval results, the ranking problem in the tag-based image retrieval has gained researchers� wide attention. We will review some of techniques proposed by different authors for image retrieval in this paper