Being master regulators of gene expression, transcription factors (TFs) play important roles in determining plant growth, development and reproduction. To date, many TFs have been shown to positively mediate plant responses to environmental stresses. In the current study, the biological functions of a stress-responsive NAC [NAM (No Apical Meristem), ATAF1/2 (Arabidopsis Transcription Activation Factor1/2), CUC2 (Cup-shaped Cotyledon2)]-TF encoding gene isolated from soybean (GmNAC019) in relation to plant drought tolerance and abscisic acid (ABA) responses were investigated. By using a heterologous transgenic system, we revealed that transgenic Arabidopsis plants constitutively expressing the GmNAC019 gene exhibited higher survival rates in a soil-drying assay, which was associated with lower water loss rate in detached leaves, lower cellular hydrogen peroxide content and stronger antioxidant defense under water-stressed conditions. Additionally, the exogenous treatment of transgenic plants with ABA showed their hypersensitivity to this phytohormone, exhibiting lower rates of seed germination and green cotyledons. Taken together, these findings demonstrated that GmNAC019 functions as a positive regulator of ABA-mediated plant response to drought, and thus, it has potential utility for improving plant tolerance through molecular biotechnology.
In this study, we characterized the first clinical Klebsiella pneumoniae strain co- harboring mcr-1 and blaNDM-4 genes in Vietnam, which was recovered from a patient admitted to hospital in 2015. This strain demonstrated nonsusceptible to all tested antibiotics, including last-line antibiotics such as carbapenems (MICs ≥128 μg/mL) and colistin (MIC =32 μg/mL), except tigecycline (MIC =1 μg/mL). Whole-genome analysis using both MinION and MiSeq data revealed that the strain carried 29 resistance genes. Particularly, mcr-1 and blaNDM-4 genes were carried by different self-conjugative plasmids and able to be transferred to a recipient by conjugation. The colistin resistance of this strain was conferred by mcr-1 and additional chromosomal resistance determinants. Eight amino acid substitutions found in PmrA, PmrB, PmrC, PmrI, and PmrJ, all proteins that are involved in lipopolysaccharide modifications, may be associated with chromosomal colistin resistance. The accumulation of multiple antibiotic resistance mechanisms in this clinical isolate raises alarm on potential spread of extensively drug-resistant K. pneumoniae in healthcare settings.
PurposeThe purpose of this paper is to expand and analyze deeply customer emotions, concretize the levels of positive or negative emotions with the aim of using machine learning methods, and build a model to identify customer emotions.Design/methodology/approachThe study proposed a customer emotion detection model and data mining method based on the collected dataset, including 80,593 online reviews on agoda.com and booking.com from 2009 to 2022.FindingsBy discerning specific emotions expressed in customers' comments, emotion detection, which refers to the process of identifying users' emotional states, assumes a crucial role in evaluating the brand value of a product. The research capitalizes on the vast and diverse data sources available on hotel booking websites, which, despite their richness, remain largely unexplored and unanalyzed. The outcomes of the model, pertaining to the detection and classification of customer emotions based on ratings and reviews into four distinct emotional states, offer a means to address the challenge of determining customer satisfaction regarding their actual service experiences. These findings hold substantial value for businesses operating in this domain, as the findings facilitate the evaluation and formulation of improvement strategies within their business models. The experimental study reveals that the proposed model attains an exact match ratio, precision, and recall rates of up to 81%, 90% and 90%, respectively.Research limitations/implicationsThe study has yet to mine real-time data. Prediction results may be influenced because the amount of data collected from the web is insufficient and preprocessing is not completely suppressed. Furthermore, the model in the study was not tested using all algorithms and multi-label classifiers. Future research should build databases to mine data in real-time and collect more data and enhance the current model.Practical implicationsThe study's results suggest that the emotion detection models can be applied to the real world to quickly analyze customer feedback. The proposed models enable the identification of customers' emotions, the discovery of customer demand, the enhancement of service, and the general customer experience. The established models can be used by many service sectors to learn more about customer satisfaction with the offered goods and services from customer reviews.Social implicationsThe research paper helps businesses in the hospitality area analyze customer emotions in each specific aspect to ensure customer satisfaction. In addition, managers can come up with appropriate strategies to bring better products and services to society and people. Subsequently, fostering the growth of the hotel tourism sector within the nation, thereby facilitating sustainable economic development on a national scale.Originality/valueThis study developed a customer emotions detection model for detecting and classifying customer ratings and reviews as 4 specific emotions: happy, angry, depressed and hopeful based on online booking hotel websites agoda.com and booking.com that contains 80,593 reviews in Vietnamese. The research results help businesses check and evaluate the quality of their services, thereby offering appropriate improvement strategies to increase customers' satisfaction and demand more effectively.
Copy number variation (CNV) analysis is a powerful tool for discovering structural genomic variation. Still, no program uses this tool to analyze chromosomal aneuploidies in the Vietnamese population. Pregnant women attending routine prenatal checkups in Vietnam from October 2018 to May 2021 were included in this study and contributed fetal tissue to test the utility of CNV analysis for prenatal screening. Among 5,008 women screened, 958 (19.13%) harbored at least one CNV, comprising segmental aneuploidy (8.49%), trisomy (6.91%), multiple anomalies (2.10%), and sex chromosome abnormality (1.64%). The rate of segmental aneuploidy detection increased with gestational age, but trisomy and sex chromosomal abnormalities detection decreased as the pregnancy continued. This study also found an association between abnormal CNVs and several phenotypic markers. For ultrasound soft markers, an increased nuchal fold thickness correlated with a higher risk of abnormal CNVs. In addition, many soft indicators or structural abnormalities were significantly associated with an increased likelihood of abnormal CNVs. This work highlights the importance of CNV analysis for the early detection of prenatal congenital abnormalities, especially in the first trimester. This study’s findings will meaningfully aid policymakers in developing cost-effective genetic prenatal screening programs.
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