Methods: A total of 204 Malaysian parents of children aged 2 to 11 years old were recruited for this study using a combination of purposive and snowball sampling approaches. Parents were required to fill an online questionnaire hosted on Google Forms, which consisted of socio-demographic characteristics (including child's gender, age, and ethnicity, as well as parental income during the MCO) and a 35-item list from the Children's Eating Behaviour Questionnaire (CEBQ). Data analysis was conducted by further stratifying the children's eating behaviour according to socio-demographic characteristics.
Results: No significant differences were observed in the eating behaviour of the children across age and parental income groups during the MCO. Malaysian Indian children had significantly lower mean scores for the food responsiveness (2.50±0.64) and emotional over-eating (2.13±0.72) subscales than Malaysian Chinese children. Girls had a significantly higher mean score for the slowness in eating subscale during the MCO than boys.
Conclusion: Children's eating behaviour were comparable across socio-demographic characteristics. Nonetheless, the findings of the current study provide an overview of Malaysian children's eating behaviour during the MCO.
METHODS: This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise.
RESULTS: Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.
CONCLUSION: This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters.
MATERIALS AND METHODS: A literature review was performed following the PRISMA guidelines. Systematic searches were performed in PubMed, Scopus, Cochrane and Embase databases from the earliest record up to September 2022. Related studies on deep learning models for radiotherapy toxicity prediction were selected based on predefined PICOS criteria.
RESULTS: Fourteen studies of radiotherapy-treated patients on different types of cancer [prostate (n=2), HNC (n=4), liver (n=2), lung (n=4), cervical (n=1), and oesophagus (n=1)] were eligible for inclusion in the systematic review. Information regarding patient characteristics and model development was summarized. Several approaches, such as ensemble learning, data augmentation, and transfer learning, that were utilized by selected studies were discussed.
CONCLUSION: Deep learning techniques are able to produce a consistent performance for toxicity prediction. Future research using large and diverse datasets and standardization of the study methodologies are required to improve the consistency of the research output.