Universiti Malaysia Sarawak

About: Universiti Malaysia Sarawak is a education organization based out in Kuching, Malaysia. It is known for research contribution in the topics: Population & Adsorption. The organization has 3660 authors who have published 5011 publications receiving 66767 citations. The organization is also known as: Universiti Malaysia Sarawak & UNIMAS.

Cryptic species as a window on diversity and conservation

Abstract: The taxonomic challenge posed by cryptic species (two or more distinct species classified as a single species) has been recognized for nearly 300 years, but the advent of relatively inexpensive and rapid DNA sequencing has given biologists a new tool for detecting and differentiating morphologically similar species. Here, we synthesize the literature on cryptic and sibling species and discuss trends in their discovery. However, a lack of systematic studies leaves many questions open, such as whether cryptic species are more common in particular habitats, latitudes or taxonomic groups. The discovery of cryptic species is likely to be non-random with regard to taxon and biome and, hence, could have profound implications for evolutionary theory, biogeography and conservation planning.

Virology, epidemiology, pathogenesis, and control of enterovirus 71.

Abstract: First isolated in California, USA, in 1969, enterovirus 71 (EV71) is a major public health issue across the Asia-Pacific region and beyond. The virus, which is closely related to polioviruses, mostly affects children and causes hand, foot, and mouth disease with neurological and systemic complications. Specific receptors for this virus are found on white blood cells, cells in the respiratory and gastrointestinal tract, and dendritic cells. Being an RNA virus, EV71 lacks a proofreading mechanism and is evolving rapidly, with new outbreaks occurring across Asia in regular cycles, and virus gene subgroups seem to differ in clinical epidemiological properties. The pathogenesis of the severe cardiopulmonary manifestations and the relative contributions of neurogenic pulmonary oedema, cardiac dysfunction, increased vascular permeability, and cytokine storm are controversial. Public health interventions to control outbreaks involve social distancing measures, but their effectiveness has not been fully assessed. Vaccines being developed include inactivated whole-virus, live attenuated, subviral particle, and DNA vaccines.

Plasmodium knowlesi Malaria in Humans Is Widely Distributed and Potentially Life Threatening

Abstract: Background. Until recently, Plasmodium knowlesi malaria in humans was misdiagnosed as Plasmodium malariae malaria. The objectives of the present study were to determine the geographic distribution of P. knowlesi malaria in the human population in Malaysia and to investigate 4 suspected fatal cases. Methods. Sensitive and specific nested polymerase chain reaction was used to identify all Plasmodium species present in (1) blood samples obtained from 960 patients with malaria who were hospitalized in Sarawak, Malaysian Borneo, during 2001-2006; (2) 54 P. malariae archival blood films from 15 districts in Sabah, Malaysian Borneo (during 2003-2005), and 4 districts in Pahang, Peninsular Malaysia (during 2004-2005); and (3) 4 patients whose suspected cause of death was P. knowlesi malaria. For the 4 latter cases, available clinical and laboratory data were reviewed. Results. P. knowlesi DNA was detected in 266 (27.7%) of 960 of the samples from Sarawak hospitals, 41 (83.7%) of 49 from Sabah, and all 5 from Pahang. Only P. knowlesi DNA was detected in archival blood films from the 4 patients who died. All were hyperparasitemic and developed marked hepatorenal dysfunction. Conclusions. Human infection with P. knowlesi, commonly misidentified as the more benign P. malariae, are widely distributed across Malaysian Borneo and extend to Peninsular Malaysia. Because P. knowlesi replicates every 24 h, rapid diagnosis and prompt effective treatment are essential. In the absence of a specific routine diagnostic test for P. knowlesi malaria, we recommend that patients who reside in or have traveled to Southeast Asia and who have received a "P. malariae" hyperparasitemia diagnosis by microscopy receive intensive management as appropriate for severe falciparum malaria.

Feature selection based on mutual information

Abstract: The application of machine learning models such as support vector machine (SVM) and artificial neural networks (ANN) in predicting reservoir properties has been effective in the recent years when compared with the traditional empirical methods. Despite that the machine learning models suffer a lot in the faces of uncertain data which is common characteristics of well log dataset. The reason for uncertainty in well log dataset includes a missing scale, data interpretation and measurement error problems. Feature Selection aimed at selecting feature subset that is relevant to the predicting property. In this paper a feature selection based on mutual information criterion is proposed, the strong point of this method relies on the choice of threshold based on statistically sound criterion for the typical greedy feedforward method of feature selection. Experimental results indicate that the proposed method is capable of improving the performance of the machine learning models in terms of prediction accuracy and reduction in training time.