Recently, the bitter receptor gene (TAS2R38) was identified to be responsible for phenylthiocarbamide (PTC) bitter sensitivity. Its two predominant haplotypes at three Single Nucleotide Polymorphisms (SNPs) are found to be definitive for the PTC status, which the ProAlaVal and AlaValIle haplotypes are associated with tasters and non-tasters, respectively. TAS2R38 haplotypes have been reported to influence food preferences (like cruciferous vegetables and fat foods) and cardiovascular disease risk factors. We examined, in 215 Malaysian subjects (100 males, 115 females), the association of the P49A SNP of TAS2R38 with anthropometric measurements and aversion to a list of 36 vegetables, 4 soy products, green tea and 37 sweet/fat foods. The subjects were successfully genotyped as 110 PA, 81 PP and 24 AA (with the A49 allelic frequency of 0.37), by Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP). Ethnicity (Malay, Chinese or Indian), but not gender, was associated with the P49A TAS2R38 genotypes (p<0.001). However, no significant differences in terms of Body Mass Index, Total Body Fat, waist circumference and Waist-Hip Ratio were found between the genotypes (p<0.05). Only aversions to green tea, mayonnaise and whipped cream, but not soy products, vegetables, and other sweet/fat foods, were associated with the P49A genotypes (p<0.05). Therefore, the P49A SNP of the bitter receptor gene TAS2R38 could not serve as a predictor of anthropometric measurements and aversion to vegetables or sweet/fat foods in the sampled Malaysian subjects, and this suggests the existence of other possible factors influencing food selection among Malaysians.
Nicotine has been shown to exert negative effects on bone. This study determined whether vitamin E supplementation is able to repair the nicotine-induced adverse effects in bone.
Stomatal density is an important trait for breeding selection of drought tolerant oil palms; however, its measurement is extremely tedious. To accelerate this process, we developed an automated system. Leaf samples from 128 palms ranging from nursery (1 years old), juvenile (2-3 years old) and mature (> 10 years old) were collected to build an oil palm specific stomata detection model. Micrographs were split into tiles, then used to train a stomata object detection convolutional neural network model through transfer learning. The detection model was then tested on leaf samples acquired from three independent oil palm populations of young seedlings (A), juveniles (B) and productive adults (C). The detection accuracy, measured in precision and recall, was 98.00% and 99.50% for set A, 99.70% and 97.65% for set B, and 99.55% and 99.62% for set C, respectively. The detection model was cross-applied to another set of adult palms using stomata images taken with a different microscope and under different conditions (D), resulting in precision and recall accuracy of 99.72% and 96.88%, respectively. This indicates that the model built generalized well, in addition has high transferability. With the completion of this detection model, stomatal density measurement can be accelerated. This in turn will accelerate the breeding selection for drought tolerance.
The ability of older people to function independently is crucial as physical disability and functional limitation have profound impacts on health. Interventions that either delay the onset of frailty or attenuate its severity potentially have cascading benefits for older people, their families and society. This study aims to develop and evaluate the effectiveness of a multiComponent Exercise and theRApeutic lifeStyle (CERgAS) intervention program targeted at improving physical performance and maintaining independent living as compared to general health education among older people in an urban poor setting in Malaysia.