Research Note: Possible influence of thermal selection on patterns of HSP70 and HSP90 gene polymorphisms in Thai indigenous and local chicken breeds and red junglefowls

The thermal stress caused by global climate change adversely affects the welfare, productivity, and reproductive performance of farm animals, including chickens, and causes substantial economic losses. However, the understanding of the genetic basis of the indigenous chicken adaptation to high ambient temperatures is limited. Hence, to reveal the genetic basis of thermal stress adaptation in chickens, this study investigated polymorphisms in the heat shock protein 70 (HSP70) and HSP90 genes, known mechanisms of cellular defense against thermal stress in indigenous and local chicken breeds and red junglefowls in Thailand. The result revealed seven alleles of the HSP70 gene. One allele exhibited a missense mutation, where an amino acid changed from Asn to His in the substrate-binding and peptide-binding domains, which is exclusive to the Lao Pa Koi chicken breed. Twenty new alleles with silent mutations in the HSP90 gene highlighted its greater complexity. Despite this diversity, distinct population structures were not found for either HSP70 or HSP90, which suggests incomplete impact on the domestication process and selection. The low genetic diversity, shown by the sharing of alleles between red junglefowls and Thai indigenous and local chicken breeds, aligns with the hypothesis that these alleles have undergone selection in tropical regions, such as Thailand. Selection signature analysis suggests the purifying selection of HSP70 for thermotolerance. This study provides valuable insights for enhancing the conservation of genetic resources with thermotolerant traits, which are essential for developing breeding programs to increase poultry production in the context of global climate change.

Improving neural machine translation with POS-tag features for low-resource language pairs

Integrating linguistic features has been widely utilized in statistical machine translation (SMT) systems, resulting in improved translation quality. However, for low-resource languages such as Thai and Myanmar, the integration of linguistic features in neural machine translation (NMT) systems has yet to be implemented. In this study, we propose transformer-based NMT models (transformer, multi-source transformer, and shared-multi-source transformer models) using linguistic features for two-way translation of Thai-to-Myanmar, Myanmar-to-English, and Thai-to-English. Linguistic features such as part-of-speech (POS) tags or universal part-of-speech (UPOS) tags are added to each word on either the source or target side, or both the source and target sides, and the proposed models are conducted. The multi-source transformer and shared-multi-source transformer models take two inputs (i.e., string data and string data with POS tags) and produce string data or string data with POS tags. A transformer model that utilizes only word vectors was used as the first baseline model for comparison with the proposed models. The second baseline model, an Edit-Based Transformer with Repositioning (EDITOR) model, was also used to compare with our proposed models in addition to the baseline transformer model. The findings of the experiments show that adding linguistic features to the transformer-based models enhances the performance of a neural machine translation in low-resource language pairs. Moreover, the best translation results were yielded using shared-multi-source transformer models with linguistic features resulting in more significant Bilingual Evaluation Understudy (BLEU) scores and character n-gram F-score (chrF) scores than the baseline transformer and EDITOR models.

Computer-Supported Meta-reflective Learning Model via mathematical word problem learning for training metacognition

To become a self-regulated learner, one needs to have a skill required to induce himself to comprehend their own cognition. In this paper, we provided a definition of Seed skill to become a self-regulated learner (S2SRL) as a basis terminology for developing our proposed framework, CREMA-Computer-Supported Meta-Reflective Learning Model via MWP in order to design an environment to encourage learners to use intrinsic comprehension of metacognitive questioning to acquire S2SRL in mathematical word problem (MWP) learning. To assess our proposed framework, we addressed these questions: (i) Can CREMA really support learner to gain S2SRL and (ii) How does it work in a practical environment? To answer these two questions, three classes of low performance students of grade 9 (total 101 students) were assigned into three different learning groups: (i) a group of students who learnt MWP with our proposed method by implementing CREMA, (ii) a group of students who learnt MWP in traditional method combining MetaQ-metacognitive questions and motivational statements, and (iii) a class of students who learnt MWP in traditional method. The result from our investigation showed that MetaQ played an important role in CREMA, while integrating computer and technology enhanced students' learning sense and empowered methodology to facilitate learning objects in the implementation of CREMA to effectively support students to gain S2SRL in MWP learning.