RESULTS: We found evidence of genetic influx from Indians to Malays, more in Melayu Kedah and Melayu Kelantan which are genetically different from the other Malay sub-ethnic groups, but similar to Thai Pattani. More than 98% of these northern Malays haplotypes could be found in either Indians or Chinese populations, indicating a highly admixture pattern among populations. Nevertheless, the ancestry lines of Malays, Indonesians and Thais were traced back to have shared a common ancestor with the Proto-Malays and Chinese.
CONCLUSIONS: These results support genetic admixtures in the Peninsular Malaysia Malay populations and provided valuable information on the enigmatic demographical history as well as shed some insights into the origins of the Malays in the Malay Peninsula.
RESULTS: We analyzed the whole-genome deep sequencing data (~ 30×) of five native trios from Peninsular Malaysia and North Borneo, and characterized the genomic variants, including single nucleotide variants (SNVs), small insertions and deletions (indels) and copy number variants (CNVs). We discovered approximately 6.9 million SNVs, 1.2 million indels, and 9000 CNVs in the 15 samples, of which 2.7% SNVs, 2.3% indels and 22% CNVs were novel, implying the insufficient coverage of population diversity in existing databases. We identified a higher proportion of novel variants in the Orang Asli (OA) samples, i.e., the indigenous people from Peninsular Malaysia, than that of the North Bornean (NB) samples, likely due to more complex demographic history and long-time isolation of the OA groups. We used the pedigree information to identify de novo variants and estimated the autosomal mutation rates to be 0.81 × 10- 8 - 1.33 × 10- 8, 1.0 × 10- 9 - 2.9 × 10- 9, and ~ 0.001 per site per generation for SNVs, indels, and CNVs, respectively. The trio-genomes also allowed for haplotype phasing with high accuracy, which serves as references to the future genomic studies of OA and NB populations. In addition, high-frequency inherited CNVs specific to OA or NB were identified. One example is a 50-kb duplication in DEFA1B detected only in the Negrito trios, implying plausible effects on host defense against the exposure of diverse microbial in tropical rainforest environment of these hunter-gatherers. The CNVs shared between OA and NB groups were much fewer than those specific to each group. Nevertheless, we identified a 142-kb duplication in AMY1A in all the 15 samples, and this gene is associated with the high-starch diet. Moreover, novel insertions shared with archaic hominids were identified in our samples.
CONCLUSION: Our study presents a full catalogue of the genome variants of the native Malaysian populations, which is a complement of the genome diversity in Southeast Asians. It implies specific population history of the native inhabitants, and demonstrated the necessity of more genome sequencing efforts on the multi-ethnic native groups of Malaysia and Southeast Asia.
METHODS: We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance.
RESULTS: We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent.
CONCLUSIONS: Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.
METHODS: We collected and analyzed functional near-infrared spectroscopy data of 38 participants while performing the revised lateralized attention network tast.
RESULTS: Elite players were significantly faster than novices (p = .005), and the experts' overall accuracy rate (ACC) was higher than that of novices (p = .001). The effect of the executive network on reaction time was higher in novices than in elite players (p = .008) and experts (p = .004). The effect of the executive network on the ACC was lower in elite players than in experts (p = .009) and novices (p = .010). Finally, elite player had higher flanker conflict effects on RT (p = .005) under the invalid cue condition. the effect of the alertness network and orientation on the ACC was lower in elite players than in novices (p = .000) and experts (p = .022). Changes in the blood oxygen level-dependent signal related to the flanker effect were significantly different in the right dorsolateral prefrontal cortex (F=3.980, p = .028) and right inferior frontal gyrus (F=3.703, p = .035) among the three groups. Elit players showed more efficient executive control (reduced conflict effect on ACC) (p = .006)in the RH.The changes related to the effect of blood oxygen level on orienting were significantly different in the right frontal eye fields (F=3.883, p = .030) among the three groups, Accompanied by significant activation of the right dorsolateral prefrontal cortex(p = .026).
CONCLUSION: Our findings provide partial evidence of the superior cognitive performance and high neural efficiency of elite ice hockey players during cognitive tasks. These results demonstrate the right hemisphere superiority for executive control.We also found that specific brain activation in hockey players does not show a clear and linear relationship with skill level.
METHODS: A total of 2231 higher vocational students from Shandong Province were surveyed by means of Academic Self-efficacy Questionnaire, Meaning in Life Questionnaire, and Test Anxiety Scale.
RESULTS: There were significant negative correlations among academic self-efficacy, sense of life meaning, and test anxiety. Fear of failure was positively correlated with test anxiety. Sense of life meaning and fear of failure played a mediating role in the relationship between academic self-efficacy and test anxiety. The chain mediating effect was significant only in the female group, not in the male group. In contrast, academic self-efficacy indirectly predicted test anxiety by the independent mediating effect of sense of life meaning or fear of failure in the male group.
CONCLUSION: Academic self-efficacy may influence test anxiety through the independent mediating effect of sense of life meaning, fear of failure, and the chain mediating effect, and there is a gender difference in these effects.