Downscaling of rainfall from global circulation model to station-level data

Due to modernization and rapid industrialization worldwide, burning of fossil fuels to generate enormous amount of energy for usage has become an inevitable process. However, the downside of it is large amount of carbon dioxide is being produced from the burning of fossil fuels. Carbon dioxide is the primary greenhouse gases that contribute to global warming which has a serious impact on the climate these days. Singapore is also affected such that Singapore’s weather has become more unpredictable, irregular and out of the usual norm in the recent years. This year, during the period of January and February 2014, Singapore which experiences rain on most of the days, has suffered its longest dry spell and had little rainfall. While some dry weather is expected at this time of year, the abnormal lack of rain is raising concerns about the pace of climate change in the region. Singapore’s temperature is also getting hotter and rising at a rate of 0.26 °C per decade since 1951. Local government authorities are viewing this climate change issue as one of its main concerns because it will pose serious consequences to the well being of its citizens in the near future. To be able to predict the future precipitate and temperature, preventive measures can be taken in time to handle and deal with the problems caused from climate change. Therefore, this project aims to downscale rainfall and temperature data from Global Circulation Model (GCM) to station level data using Statistical DownScaling Model (SDSM). After assessing the accuracy and performance of SDSM, future prediction of rainfall and temperature can be generated by SDSM and the trends of these future prediction data will be further discussed and analyzed.Bachelor of Engineering (Civil

Comparison of midwife-led care and obstetrician-led care on maternal and neonatal outcomes in Singapore: A retrospective cohort study

10.1016/j.midw.2017.07.010MIDWIFERY5371-7

Cost-effective hybrid long-short read assembly delineates alternative GC-rich Streptomyces hosts for natural product discovery

With the advent of rapid automated in silico identification of biosynthetic gene clusters (BGCs), genomics presents vast opportunities to accelerate natural product (NP) discovery. However, prolific NP producers, Streptomyces, are exceptionally GC-rich (>80%) and highly repetitive within BGCs. These pose challenges in sequencing and high-quality genome assembly which are currently circumvented via intensive sequencing. Here, we outline a more cost-effective workflow using multiplex Illumina and Oxford Nanopore sequencing with hybrid long-short read assembly algorithms to generate high quality genomes. Our protocol involves subjecting long read-derived assemblies to up to 4 rounds of polishing with short reads to yield accurate BGC predictions. We successfully sequenced and assembled 8 GC-rich Streptomyces genomes whose lengths range from 7.1 to 12.1 Mb with a median N50 of 8.2 Mb. Taxonomic analysis revealed previous misrepresentation among these strains and allowed us to propose a potentially new species, Streptomyces sydneybrenneri. Further comprehensive characterization of their biosynthetic, pan-genomic and antibiotic resistance features especially for molecules derived from type I polyketide synthase (PKS) BGCs reflected their potential as alternative NP hosts. Thus, the genome assemblies and insights presented here are envisioned to serve as gateway for the scientific community to expand their avenues in NP discovery