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Sexual dimorphism is widely observed in almost all farmed aquatic animal species but giant freshwater pawn (GFP) is unique, with males characterized by three main morphotypes (blue claw, orange claw and small males) and females by different reproduction status (ovary, berried egg and already‐spawned females). There has been reported evidence that the effect of male morphotype may have masked genetic variation in growth‐related traits, as a result the heritability for male body weight was lower than that estimated in female. A pending question has arisen whether selection should be made in female only. To answer this question, we used an 8‐year data set from a long‐term selection programme (2008–2015) for high growth in this species comprising 106,756 individuals that were offspring of 515 sires and 810 dams. The body weight data of female and male GFP or of each morphotype was treated as a separate trait and a multi‐trait approach was used to estimate genetic correlations for homologous traits between sexes and between morphotypes. Our analysis showed that there were little differences in the heritability estimates between female and male. In female, mature ovary individual displayed higher heritability than berried egg and already‐spawned females. For male, the heritability for blue claw, orange claw and small males were 0.11, 0.06 and 0.00 respectively. Between‐sex genetic correlation was moderate (0.55 ± 0.11) for body weight, suggesting that the trait expressions in female and male may be genetically different. In female, the genetic correlations for body weight among three female types were close to one (0.91–0.94). In contrast, the genetic correlations for body weight between male morphotypes especially between blue claw or orange claw and small males were low (0.15–0.25). Furthermore, we estimated genetic gain as the difference in least square means (LSM) or estimated breeding values (EBV) between the selection line and control group. The genetic gain in body weight was smaller in females than in males. It is concluded that there is no need to run separate breeding programme for female and male GFP. A combined selection using both female and male data can achieve selection response for body weight as demonstrated in the present study.

Assessments of genomic prediction accuracies using artificial intelligence (AI) algorithms (i.e.,, machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e.,, pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a non-linear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e.,, ML-KAML) and deep learning (i.e.,, DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP and BayesR) were conducted for two main disease resistance traits (i.e.,, survival status coded as 0 and 1 and survival time, i.e.,, days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 Single Nucleotide Polymorphism (SNPs). The results using 6,470 SNPs after quality control showed that machine learning methods outperformed PBLUP, GBLUP and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1–15.4%. However, the prediction accuracies obtained from machine learning methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3–19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3–5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318 - 400 SNPs for survival status and 1,362–1,589 SNPs for survival time) were somewhat lower (0.3 to 15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that although there are prospects for the application of genomic selection to increase disease resistance to Edwardsiella ictaluri in striped catfish breeding programs, further evaluation of these methods should be made in independent families/populations when more data are accumulated in future generations to avoid possible biases in the genetic parameters estimates and prediction accuracies for the disease resistant traits studied in this population of striped catfish P. hypophthalmus.

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English prepositions play a significant role in helping students form a well-structured sentence in their learning and communicating. To help Vietnamese learners of English acquire their competence, the authors have done survey research to investigate the factors affecting the uses of English prepositions made by Vietnamese learners of English. The population included 200 female and 200 male participants. A total of 400 answers on the questions provided in the 100-question questionnaire were used for hypothesis testing. The items in the survey were given different weights, and the total attainable marks were 100. The results showed that Vietnamese intra-lingual interference strongly affected prepositional sense expressed by Vietnamese EFL learners. Genders, level of learning (low, intermediate, and advanced), writing and speaking, and cognitive embodiment also played a significant role in terms of language transfer, affecting the usage of English prepositions by EFL learners.