University of Adelaide

About: University of Adelaide is a education organization based out in Adelaide, South Australia, Australia. It is known for research contribution in the topics: Population & Pregnancy. The organization has 27251 authors who have published 79167 publications receiving 2671128 citations. The organization is also known as: The University of Adelaide & Adelaide University.

Experimental strategies for microRNA target identification

Abstract: MicroRNAs (miRNAs) are important regulators of eukaryotic gene expression in most biological processes. They act by guiding the RNAi-induced silencing complex (RISC) to partially complementary sequences in target mRNAs to suppress gene expression by a combination of translation inhibition and mRNA decay. The commonly accepted mechanism of miRNA targeting in animals involves an interaction between the 5'-end of the miRNA called the 'seed region' and the 3' untranslated region (3'-UTR) of the mRNA. Many target prediction algorithms are based around such a model, though increasing evidence demonstrates that targeting can also be mediated through sites other than the 3'-UTR and that seed region base pairing is not always required. The power and validity of such in silico data can be therefore hindered by the simplified rules used to represent targeting interactions. Experimentation is essential to identify genuine miRNA targets, however many experimental modalities exist and their limitations need to be understood. This review summarizes and critiques the existing experimental techniques for miRNA target identification.

The state and conservation of Southeast Asian biodiversity

Abstract: Southeast Asia is a region of conservation concern due to heavy losses of its native habitats. In this overview, we highlight the conservation importance of Southeast Asia by comparing its degree of species endemism and endangerment, and its rate of deforestation with other tropical regions (i.e., Meso-America, South America, and Sub-Saharan Africa). Southeast Asia contains the highest mean proportion of country-endemic bird (9%) and mammal species (11%). This region also has the highest proportion of threatened vascular plant, reptile, bird, and mammal species. Furthermore, not only is Southeast Asia’s annual deforestation rate the highest in the tropics, but it has also increased between the periods 1990–2000 and 2000–2005. This could result in projected losses of 13–85% of biodiversity in the region by 2100. Secondary habitat restoration, at least in certain countries, would allow for some amelioration of biodiversity loss and thus potentially lower the currently predicted extinction rates. Nonetheless, urgent conservation actions are needed. Conservation initiatives should include public education, sustaining livelihoods, and ways to enhance the sustainability of agriculture and increase the capacity of conservation institutions. Furthermore, these actions should be country-specific and not ignore areas heavily populated by humans, as they can also harbour high numbers of threatened species. We urge that cooperative conservation initiatives be undertaken and support (e.g., capacity-building) be given by more developed countries in the region and beyond.

Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification

Abstract: The ability to provide different Quality of Service (QoS) guarantees to traffic from different applications is a highly desired feature for many IP network operators, particularly for enterprise networks. Although various mechanisms exist for providing QoS in the network, QoS is yet to be widely deployed. We believe that a key factor holding back widespread QoS adoption is the absence of suitable methodologies/processes for appropriately mapping the traffic from different applications to different QoS classes. This is a challenging task, because many enterprise network operators who are interested in QoS do not know all the applications running on their network, and furthermore, over recent years port-based application classification has become problematic. We argue that measurement based automated Class of Service (CoS) mapping is an important practical problem that needs to be studied.In this paper we describe the requirements and associated challenges, and outline a solution framework for measurement based classification of traffic for QoS based on statistical application signatures. In our approach the signatures are chosen in such as way as to make them insensitive to the particular application layer protocol, but rather to determine the way in which an application is used -- for instance is it used interactively, or for bulk-data transport. The resulting application signature can then be used to derive the network layer signatures required to determine the CoS class for individual IP datagrams. Our evaluations using traffic traces from a variety of network locations, demonstrate the feasibility and potential of the approach.