The burden of neglected tropical diseases in Ethiopia, and opportunities for integrated control and elimination

Background: Neglected tropical diseases (NTDs) are a group of chronic parasitic diseases and related conditions that are the most common diseases among the 2·7 billion people globally living on less than US$2 per day. In response to the growing challenge of NTDs, Ethiopia is preparing to launch a NTD Master Plan. The purpose of this review is to underscore the burden of NTDs in Ethiopia, highlight the state of current interventions, and suggest ways forward. Results: This review indicates that NTDs are significant public health problems in Ethiopia. From the analysis reported here, Ethiopia stands out for having the largest number of NTD cases following Nigeria and the Democratic Republic of Congo. Ethiopia is estimated to have the highest burden of trachoma, podoconiosis and cutaneous leishmaniasis in sub-Saharan Africa (SSA), the second highest burden in terms of ascariasis, leprosy and visceral leishmaniasis, and the third highest burden of hookworm. Infections such as schistosomiasis, trichuriasis, lymphatic filariasis and rabies are also common. A third of Ethiopians are infected with ascariasis, one quarter is infected with trichuriasis and one in eight Ethiopians lives with hookworm or is infected with trachoma. However, despite these high burdens of infection, the control of most NTDs in Ethiopia is in its infancy. In terms of NTD control achievements, Ethiopia reached the leprosy elimination target of 1 case/10,000 population in 1999. No cases of human African trypanosomiasis have been reported since 1984. Guinea worm eradication is in its final phase. The Onchocerciasis Control Program has been making steady progress since 2001. A national blindness survey was conducted in 2006 and the trachoma program has kicked off in some regions. Lymphatic Filariasis, podoconiosis and rabies mapping are underway. Conclusion: Ethiopia bears a significant burden of NTDs compared to other SSA countries. To achieve success in integrated control of NTDs, integrated mapping, rapid scale up of interventions and operational research into co implementation of intervention packages will be crucial

Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph

As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort, and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that influence-based graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%.Comment: Mathematical Problems in Engineering, Vol 201

Geometry-based Detection of Flash Worms

While it takes traditional internet worms hours to infect all the vulnerable hosts on the Internet, a flash worm takes seconds. Because of the rapid rate with which flash worms spread, the existing worm defense mechanisms cannot respond fast enough to detect and stop the flash worm infections. In this project, we propose a geometric-based detection mechanism that can detect the spread of flash worms in a short period of time. We tested the mechanism on various simulated flash worm traffics consisting of more than 10,000 nodes. In addition to testing on flash worm traffics, we also tested the mechanism on non-flash worm traffics to see if our detection mechanism produces false alarms. In order to efficiently analyze bulks of various network traffics, we implemented an application that can be used to convert the network traffic data into graphical notations. Using the application, the analysis can be done graphically as it displays the large amount of network relationships as tree structures

Small RNAs and extracellular vesicles in filarial nematodes: from nematode development to diagnostics

Parasitic nematodes have evolved sophisticated mechanisms to communicate with their hosts in order to survive and successfully establish an infection. The transfer of RNA within extracellular vesicles (EVs) has recently been described as a mechanism that could contribute to this communication in filarial nematodes. It has been shown that these EVs are loaded with several types of RNAs, including microRNAs, leading to the hypothesis that parasites could actively use these molecules to manipulate host gene expression and to the exciting prospect that these pathways could result in new diagnostic and therapeutic strategies. Here we review the literature on the diverse RNAi pathways that operate in nematodes and more specifically our current knowledge of extracellular RNA (exRNA) and EVs derived from filarial nematodes in vitro and within their hosts. We further detail some of the issues and questions related to the capacity of RNA-mediated communication to function in parasite-host interactions and the ability of exRNA to enable us to distinguish and detect different nematode parasites in their hosts