This article aims to investigate the relationship among renewable energy consumption, carbon dioxide (CO2) emissions, and GDP using panel data for 24 Asian countries between 1990 and 2012. Panel cross-sectional dependence tests and unit root test, which considers cross-sectional dependence across countries, are used to ensure that the empirical results are correct. Using the panel cointegration model, the vector error correction model, and the Granger causality test, this paper finds that a long-run equilibrium exists among renewable energy consumption, carbon emission, and GDP. CO2 emissions have a positive effect on renewable energy consumption in the Philippines, Pakistan, China, Iraq, Yemen, and Saudi Arabia. A 1% increase in GDP will increase renewable energy by 0.64%. Renewable energy is significantly determined by GDP in India, Sri Lanka, the Philippines, Thailand, Turkey, Malaysia, Jordan, United Arab Emirates, Saudi Arabia, and Mongolia. A unidirectional causality runs from GDP to CO2 emissions, and two bidirectional causal relationships were found between CO2 emissions and renewable energy consumption and between renewable energy consumption and GDP. The findings can assist governments in curbing pollution from air pollutants, execute energy conservation policy, and reduce unnecessary wastage of energy.
Plasmodium knowlesi is a newly described zoonosis that causes malaria in the human population that can be severe and fatal. The study of P. knowlesi parasites from human clinical isolates is relatively new and, in order to obtain maximum information from patient sample collections, we explored the possibility of generating P. knowlesi genome sequences from archived clinical isolates. Our patient sample collection consisted of frozen whole blood samples that contained excessive human DNA contamination and, in that form, were not suitable for parasite genome sequencing. We developed a method to reduce the amount of human DNA in the thawed blood samples in preparation for high throughput parasite genome sequencing using Illumina HiSeq and MiSeq sequencing platforms. Seven of fifteen samples processed had sufficiently pure P. knowlesi DNA for whole genome sequencing. The reads were mapped to the P. knowlesi H strain reference genome and an average mapping of 90% was obtained. Genes with low coverage were removed leaving 4623 genes for subsequent analyses. Previously we identified a DNA sequence dimorphism on a small fragment of the P. knowlesi normocyte binding protein xa gene on chromosome 14. We used the genome data to assemble full-length Pknbpxa sequences and discovered that the dimorphism extended along the gene. An in-house algorithm was developed to detect SNP sites co-associating with the dimorphism. More than half of the P. knowlesi genome was dimorphic, involving genes on all chromosomes and suggesting that two distinct types of P. knowlesi infect the human population in Sarawak, Malaysian Borneo. We use P. knowlesi clinical samples to demonstrate that Plasmodium DNA from archived patient samples can produce high quality genome data. We show that analyses, of even small numbers of difficult clinical malaria isolates, can generate comprehensive genomic information that will improve our understanding of malaria parasite diversity and pathobiology.