FORMULATION AND EVALUATION THE NEMATICIDAL ACTIVITY OF CERTAIN PLANT OILS AGAINST CITRUS NEMATODE Tylenculus semi-penetrans

Four plant oils namely Barka, Sesam, Garlic and Almond were prepared as emulsifiable concentrate (EC). Polyethylene glycol 600 dioleate (PEG 600 DO), Toximol R and Toximol H were used as emulsifiers while xylene and toluene used as solvents. Four formulations only passed successfully (F1, D2, C3 and E5) in thePhysico-chemical properties according to the standards of WHO. The results indicated that the successful prepared formulations showed different degrees of effectiveness against second stage juveniles of Tylenculus semipenetrans under laboratory conditions. Second stage juveniles seem as paralyze at 24 hrs, whereas this effect disappears completely after 72 hrs in case of Almond and Barka. On the other hand, the effect of Sesam and Garlic showed a slight effect at 24 hrs and increased gradually to give highly effectiveness after 72 hours. According to EC50 values at 72 hrs, Garlic was more effective than Sesam. The respective EC50 values were 0.6 and 2 mg/ml. These results emphasized the promising effect of certain plant oil formulations including Garlic and Sesam oil against pathogenic nematode, and that such formulation might be used for nematode control in small areas, as gardens and plant nursery

Which method is best for the induction of labour?: A systematic review, network meta-analysis and cost-effectiveness analysis

Background: More than 150,000 pregnant women in England and Wales have their labour induced each year. Multiple pharmacological, mechanical and complementary methods are available to induce labour. Objective: To assess the relative effectiveness, safety and cost-effectiveness of labour induction methods and, data permitting, effects in different clinical subgroups. Methods: We carried out a systematic review using Cochrane methods. The Cochrane Pregnancy and Childbirth Group’s Trials Register was searched (March 2014). This contains over 22,000 reports of controlled trials (published from 1923 onwards) retrieved from weekly searches of OVID MEDLINE (1966 to current); Cochrane Central Register of Controlled Trials (The Cochrane Library); EMBASE (1982 to current); Cumulative Index to Nursing and Allied Health Literature (1984 to current); ClinicalTrials.gov; the World Health Organization International Clinical Trials Registry Portal; and hand-searching of relevant conference proceedings and journals. We included randomised controlled trials examining interventions to induce labour compared with placebo, no treatment or other interventions in women eligible for third-trimester induction. We included outcomes relating to efficacy, safety and acceptability to women. In addition, for the economic analysis we searched the Database of Abstracts of Reviews of Effects, and Economic Evaluations Databases, NHS Economic Evaluation Database and the Health Technology Assessment database. We carried out a network meta-analysis (NMA) using all of the available evidence, both direct and indirect, to produce estimates of the relative effects of each treatment compared with others in a network. We developed a de novo decision tree model to estimate the cost-effectiveness of various methods. The costs included were the intervention and other hospital costs incurred (price year 2012–13). We reviewed the literature to identify preference-based utilities for the health-related outcomes in the model. We calculated incremental cost-effectiveness ratios, expected costs, utilities and net benefit. We represent uncertainty in the optimal intervention using cost-effectiveness acceptability curves. Results: We identified 1190 studies; 611 were eligible for inclusion. The interventions most likely to achieve vaginal delivery (VD) within 24 hours were intravenous oxytocin with amniotomy [posterior rank 2; 95% credible intervals (CrIs) 1 to 9] and higher-dose (≥ 50 μg) vaginal misoprostol (rank 3; 95% CrI 1 to 6). Compared with placebo, several treatments reduced the odds of caesarean section, but we observed considerable uncertainty in treatment rankings. For uterine hyperstimulation, double-balloon catheter had the highest probability of being among the best three treatments, whereas vaginal misoprostol (≥ 50 μg) was most likely to increase the odds of excessive uterine activity. For other safety outcomes there were insufficient data or there was too much uncertainty to identify which treatments performed ‘best’. Few studies collected information on women’s views. Owing to incomplete reporting of the VD within 24 hours outcome, the cost-effectiveness analysis could compare only 20 interventions. The analysis suggested that most interventions have similar utility and differ mainly in cost. With a caveat of considerable uncertainty, titrated (low-dose) misoprostol solution and buccal/sublingual misoprostol had the highest likelihood of being cost-effective. Limitations: There was considerable uncertainty in findings and there were insufficient data for some planned subgroup analyses. Conclusions: Overall, misoprostol and oxytocin with amniotomy (for women with favourable cervix) is more successful than other agents in achieving VD within 24 hours. The ranking according to safety of different methods was less clear. The cost-effectiveness analysis suggested that titrated (low-dose) oral misoprostol solution resulted in the highest utility, whereas buccal/sublingual misoprostol had the lowest cost. There was a high degree of uncertainty as to the most cost-effective intervention

Machine Learning Approaches for Identifying microRNA Targets and Conserved Protein Complexes

Much research has been directed toward understanding the roles of essential components in the cell, such as proteins, microRNAs, and genes. This dissertation focuses on two interesting problems in bioinformatics research: microRNA-target prediction and the identification of conserved protein complexes across species. We define the two problems and develop novel approaches for solving them. MicroRNAs are short non-coding RNAs that mediate gene expression. The goal is to predict microRNA targets. Existing methods rely on sequence features to predict targets. These features are neither sufficient nor necessary to identify functional target sites and ignore the cellular conditions in which microRNA and mRNA interact. We developed MicroTarget to predict microRNA-mRNA interactions using heterogeneous data sources. MicroTarget uses expression data to learn candidate target set for each microRNA. Then, sequence data is used to provide evidence of direct interactions and ranking the predicted targets. The predicted targets overlap with many of the experimentally validated ones. The results indicate that using expression data helps in predicting microRNA targets accurately. Protein complexes conserved across species specify processes that are core to cell machinery. Methods that have been devised to identify conserved complexes are severely limited by noise in PPI data. Behind PPIs, there are domains interacting physically to perform the necessary functions. Therefore, employing domains and domain interactions gives a better view of the protein interactions and functions. We developed novel strategy for local network alignment, DONA. DONA maps proteins into their domains and uses DDIs to improve the network alignment. We developed novel strategy for constructing an alignment graph and then uses this graph to discover the conserved sub-networks. DONA shows better performance in terms of the overlap with known protein complexes with higher precision and recall rates than existing methods. The result shows better semantic similarity computed with respect to both the biological process and the molecular function of the aligned sub-networks.Ph. D.Much research has been directed toward understanding the roles of essential components in the cell, such as proteins, microRNAs, and genes. The processes within the cell include a mixture of small molecules. It is of great interest to utilize different information sources to discover the interactions among these molecules. This dissertation focuses on two interesting problems: microRNA-target prediction and the identification of conserved protein complexes across species. We define the two problems and develop novel approaches for solving them. MicroRNAs are a recently discovered class of non-coding RNAs. They play key roles in the regulation of gene expression of as much as 30% of all mammalian protein encoding genes. MicroRNAs regulation activity has been implicated in a number of diseases including cancer, heart disease and neurological diseases. We developed MicroTarget to predict microRNAgene interactions using heterogeneous data sources. The predicted target genes overlap with many of the experimentally validated ones. Proteins carry out their tasks in the cell by interacting with each other. Protein complexes conserved among species specify the cell core processes. We identify conserved complexes by constructing an alignment graph leveraging on the conservation of PPIs between species through domain conservation and domain-domain interactions (DDI) in addition to PPI networks. Better integration of domain conservation and interactions in our developed conserved protein complexes identification system helps biologists benefit from verified data to predict more reliable similarity relationships among species. All the test data sets and source code for this dissertation are available at: https://bioinformatics.cs.vt.edu/∼htorkey/Software

Secured Audio Framework Based on Chaotic-Steganography Algorithm for Internet of Things Systems

The exponential growth of interconnected devices in the Internet of Things (IoT) has raised significant concerns about data security, especially when transmitting sensitive information over wireless channels. Traditional encryption techniques often fail to meet the energy and processing constraints of resource-limited IoT devices. This paper proposes a novel hybrid security framework that integrates chaotic encryption and steganography to enhance confidentiality, integrity, and resilience in audio communication. Chaotic systems generate unpredictable keys for strong encryption, while steganography conceals the existence of sensitive data within audio signals, adding a covert layer of protection. The proposed approach is evaluated within an Orthogonal Frequency Division Multiplexing (OFDM)-based wireless communication system, widely recognized for its robustness against interference and channel impairments. By combining secure encryption with a practical transmission scheme, this work demonstrates the effectiveness of the proposed hybrid method in realistic IoT environments, achieving high performance in terms of signal integrity, security, and resistance to noise. Simulation results indicate that the OFDM system incorporating chaotic algorithm modes alongside steganography outperforms the chaotic algorithm alone, particularly at higher Eb/No values. Notably, with DCT-OFDM, the chaotic-CFB based on steganography algorithm achieves a performance gain of approximately 30 dB compared to FFT-OFDM and DWT-based systems at Eb/No = 8 dB. These findings suggest that steganography plays a crucial role in enhancing secure transmission, offering greater signal deviation, reduced correlation, a more uniform histogram, and increased resistance to noise, especially in high BER scenarios. This highlights the potential of hybrid cryptographic-steganographic methods in safeguarding sensitive audio information within IoT networks and provides a foundation for future advancements in secure IoT communication systems