CMAPLE: efficient phylogenetic inference in the pandemic era.

We have recently introduced MAPLE (MAximum Parsimonious Likelihood Estimation), a new pandemic-scale phylogenetic inference method exclusively designed for genomic epidemiology. In response to the need for enhancing MAPLE's performance and scalability, here we present two key components: (1) CMAPLE software, a highly optimized C++ reimplementation of MAPLE with many new features and advancements; and (2) CMAPLE library, a suite of Application Programming Interfaces to facilitate the integration of the CMAPLE algorithm into existing phylogenetic inference packages. Notably, we have successfully integrated CMAPLE into the widely used IQ-TREE 2 software, enabling its rapid adoption in the scientific community. These advancements serve as a vital step towards better preparedness for future pandemics, offering researchers powerful tools for large-scale pathogen genomic analysis.

Order of amino acid recruitment into the genetic code resolved by Last Universal Common Ancestor's protein domains.

The current "consensus" order in which amino acids were added to the genetic code is based on potentially biased criteria such as absence of sulfur-containing amino acids from the Urey-Miller experiment which lacked sulfur. Even if inferred perfectly, abiotic abundance might not reflect abundance in the organisms in which the genetic code evolved. Here, we instead exploit the fact that proteins that emerged prior to the genetic code's completion are likely enriched in early amino acids and depleted in late amino acids. We identify the most ancient protein-coding sequences born prior to the archaeal-bacterial split. Amino acid usage in protein sequences whose ancestors date back to a single homolog in the Last Universal Common Ancestor (LUCA) largely matches the consensus order. However, our findings indicate that metal-binding (cysteine and histidine) and sulfur-containing (cysteine and methionine) amino acids were added to the genetic code much earlier than previously thought. Surprisingly, even more ancient protein sequences - those that had already diversified into multiple distinct copies in LUCA - show a different pattern to single copy LUCA sequences: significantly less depleted in the late amino acids tryptophan and tyrosine, and enriched rather than depleted in phenylalanine. This is compatible with at least some of these sequences predating the current genetic code. Their distinct enrichment patterns thus provide hints about earlier, alternative genetic codes.

Optimization of elution conditions and comparison of emerging biocompatible columns on the resolving power and detection sensitivity of oligonucleotides by ion-pairing reversed-phase liquid chromatography mass spectrometry.

A generic performance comparison strategy has been developed to evaluate the impact of mobile-phase additives (ion-pairing agent / counter ion systems), distinct stationary phases on resulting resolving power, and MS detectability of oligonucleotides and their critical impurities in gradient IP-RPLC. Stationary-phase considerations included particle type (core-shell vs. fully porous particles), particle diameter, and pore size. Separations were carried out at 60°C to optimize mass transfer (C-term). The incorporation of an active column preheater mitigated thermal mismatches, leading to narrower peaks and overcoming peak splitting. Acetonitrile as organic modifier outweighed methanol in terms of peak-capacity generation and yielded a 30% lower back pressure. Performance screening experiments were conducted varying ion-pairing agents and counter ions, while adjusting gradient span achieved an equivalent effective retention window. Hexafluoromethylisopropanol yielded superior chromatographic resolution, whereas hexafluoroisopropanol yielded significantly higher MS detection sensitivity. The 1.7 µm core-shell particle columns with 100 Å pores provided maximum resolving power for small (15-35 mers) oligonucleotides. Sub-min analysis for 15-35 polyT ladders was achieved operating a 50 mm long column at the kinetic performance limits. High-resolution separations between a 21-mer modified RNA sequence oligonucleotides and its related (shortmer and phosphodiester) impurities and complementary strand were obtained using a coupled column set-up with a total length of 450 mm.

Multivariate Statistical Analysis for the Classification of Sausages Based on Physicochemical Attributes, Using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS).

Sausage is a convenient food that is widely consumed in the world and in Vietnam. Due to the rapid development of this product, the authenticity of many famous brands has faded by the rise of adulteration. Therefore, in this study, principal component analysis (PCA) was combined with chemical analysis to identify 6 sausage brands. Sausage samples were dried and then ground to a fine powder for both instrumental analyses of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and inductively coupled plasma-mass spectrometry (ICP-MS). Dried measurements of ATR-FTIR was performed directly on the ZnSe crystal, while elemental data were obtained through microwave digestion before the ICP-MS analysis. Principal component analysis (PCA) within the framework software of XLSTAT and STATISTICA 12 was performed on the spectroscopy and elemental dataset of sausage samples. PCA visualized the distinction of 6 sausage brands on both datasets of ATR-FTIR and ICP-MS. The classification on the spectroscopy profile showed that although more than 90% variation of the dataset was explained on the first two PCs, the difference between several brands was not detected as the distribution of data overlapped with one another. The PCA observation of the elemental composition on PC1 and PC3 has separated the sausage brands into 6 distinctive groups. Besides, several key elements contributed to the brands' identification have been detected, and the most distinctive elements are Na, K, Ca, and Ba. PCA visualization showed the feasibility of the classification of sausage samples from different brands when combined with the results of FT-IR and ICP-MS methods. The experiment was able to differentiate the sausages from the 5 brands using multivariate statistics.

A novel flood risk management approach based on future climate and land use change scenarios.

Climate change and increasing urbanization are two primary factors responsible for the increased risk of serious flooding around the world. The prediction and monitoring of the effects of land use/land cover (LULC) and climate change on flood risk are critical steps in the development of appropriate strategies to reduce potential damage. This study aimed to develop a new approach by combining machine learning (namely the XGBoost, CatBoost, LightGBM, and ExtraTree models) and hydraulic modeling to predict the effects of climate change and LULC change on land that is at risk of flooding. For the years 2005, 2020, 2035, and 2050, machine learning was used to model and predict flood susceptibility under different scenarios of LULC, while hydraulic modeling was used to model and predict flood depth and flood velocity, based on the RCP 8.5 climate change scenario. The two elements were used to build a flood risk assessment, integrating socioeconomic data such as LULC, population density, poverty rate, number of women, number of schools, and cultivated area. Flood risk was then computed, using the analytical hierarchy process, by combining flood hazard, exposure, and vulnerability. The results showed that the area at high and very high flood risk increased rapidly, as did the areas of high/very high exposure, and high/very high vulnerability. They also showed how flood risk had increased rapidly from 2005 to 2020 and would continue to do so in 2035 and 2050, due to the dynamics of climate change and LULC change, population growth, the number of women, and the number of schools - particularly in the flood zone. The results highlight the relationships between flood risk and environmental and socio-economic changes and suggest that flood risk management strategies should also be integrated in future analyses. The map built in this study shows past and future flood risk, providing insights into the spatial distribution of urban area in flood zones and can be used to facilitate the development of priority measures, flood mitigation being most important.

Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS.

Floods are arguably the most impactful of natural hazards. The increasing magnitude of their effects on the environment, human life, and economic activities calls for improved management of water resources. Flood susceptibility modeling has been used around the world to reduce the damage caused by flooding, although the extrapolation problem still presents a significant challenge. This study develops a machine learning (ML) model utilizing deep neural network (DNN) and optimization algorithms, namely earthworm optimization algorithm (EOA), wildebeest herd optimization (WHO), biogeography-based optimization (BBO), satin bowerbird optimizer (SBO), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO), to solve the extrapolation problem in the construction of flood susceptibility models. Quang Nam Province was chosen as a case study as it is subject to the significant impact of intense flooding, and Nghe An Province was selected as the region for extrapolation of the flood susceptibility model. Root mean square error (RMSE), receiver operating characteristic (ROC), the area under the ROC curve (AUC), and accuracy (ACC) were applied to assess and compare the fit of each of the models. The results indicated that the models in this study are a good fit in establishing flood susceptibility maps, all with AUC > 0.9. The deep neural network (DNN)-BBO model enjoyed the best results (AUC = 0.99), followed by DNN-WHO (AUC = 0.99), DNN-SBO (AUC = 0.98), DNN-EOA (AUC = 0.96), DNN-GOA (AUC = 0.95), and finally, DNN-PSO (AUC = 0.92). In addition, the models successfully solved the extrapolation problem. These new models can modify their behavior to evaluate flood susceptibility in different regions of the world. The models in this study distribute a first point of reference for debate on the solution to the extrapolation problem, which can support urban planners and other decision-makers in other coastal regions in Vietnam and other countries.

MAST: Phylogenetic Inference with Mixtures Across Sites and Trees.

Hundreds or thousands of loci are now routinely used in modern phylogenomic studies. Concatenation approaches to tree inference assume that there is a single topology for the entire dataset, but different loci may have different evolutionary histories due to incomplete lineage sorting, introgression, and/or horizontal gene transfer; even single loci may not be treelike due to recombination. To overcome this shortcoming, we introduce an implementation of a multi-tree mixture model that we call MAST. This model extends a prior implementation by Boussau et al. (2009) by allowing users to estimate the weight of each of a set of pre-specified bifurcating trees in a single alignment. The MAST model allows each tree to have its own weight, topology, branch lengths, substitution model, nucleotide or amino acid frequencies, and model of rate heterogeneity across sites. We implemented the MAST model in a maximum-likelihood framework in the popular phylogenetic software, IQ-TREE. Simulations show that we can accurately recover the true model parameters, including branch lengths and tree weights for a given set of tree topologies, under a wide range of biologically realistic scenarios. We also show that we can use standard statistical inference approaches to reject a single-tree model when data are simulated under multiple trees (and vice versa). We applied the MAST model to multiple primate datasets and found that it can recover the signal of incomplete lineage sorting in the Great Apes, as well as the asymmetry in minor trees caused by introgression among several macaque species. When applied to a dataset of four Platyrrhine species for which standard concatenated maximum likelihood and gene tree approaches disagree, we observe that MAST gives the highest weight (i.e. the largest proportion of sites) to the tree also supported by gene tree approaches. These results suggest that the MAST model is able to analyse a concatenated alignment using maximum likelihood, while avoiding some of the biases that come with assuming there is only a single tree. We discuss how the MAST model can be extended in the future.

An open dataset on individual perceptions of transport policies.

Many cities are facing challenges caused by the increasing use of motorised transport and Hanoi, Vietnam, is no exception. The proliferation of petrol powered motorbikes has caused serious problems of congestion, pollution, and road safety. This paper reports on a new survey dataset that was created as part of the Urban Transport Modelling for Sustainable Well-Being in Hanoi (UTM-Hanoi) project. The survey of nearly 30,000 respondents gathers data on households' demographics, perceptions, opinions and stated behaviours. The data are informative in their own right and have also been used to experiment with multi-scale spatial statistics, synthetic population generation and machine learning approaches to predicting an individual's perceptions of potential government policies. The paper reports on the key findings from the survey and conducts a technical validation to contrast the outcomes to similar datasets that are available.

Combining angiotensin receptor blockade and enzyme replacement therapy for vascular disease in mucopolysaccharidosis type I.

Vascular involvement in the genetic disorder mucopolysaccharidosis type I (MPS I) has features of atherosclerotic disease near branch points of arterial vasculature, such as intimal thickening with disruption of the internal elastic lamina, and proliferation of macrophages and myofibroblasts. Inflammatory pathways are implicated in the pathogenesis of vascular disease in MPS I animal models, evidenced by cytokines like CD18 and TGF-ß within arterial plaques. The angiotensin II-mediated inflammatory pathway is well studied in human atherosclerotic coronary artery disease. Recent work indicates treatment with the angiotensin receptor blocker losartan may improve vascular MPS I disease in mouse models. Here, we combined losartan with the standard therapy for MPS I, enzyme replacement therapy (ERT), to measure effects on cytokines in serum and aortic vasculature. Each treatment group (losartan, ERT, and their combination) equally normalized levels of cytokines that were largely differential between normal and mutant mice. Some cytokines, notably CD30 ligand, Eotaxin-2, LIX, IL-13, IL-15, GM-CSF, MCP-5, MIG, and CCL3 showed elevations in mice treated with ERT above normal or mutant levels; these elevations were reduced or absent in mice that received losartan or combination therapy. The observations suggest that losartan may impact inflammatory cascades due to MPS I and may also blunt inflammation in combination with ERT.

Somatic Genomic and Transcriptomic Characterization of Primary Ovarian Serous Borderline Tumors and Low-Grade Serous Carcinomas.

Low-grade serous carcinoma (LGSC) may develop from serous borderline tumor (SBT) tissue, where the micropapillary type (mSBT) presents the highest risk for progression. The sensitivity of LGSC to standard chemotherapy is limited, so alternative therapeutic approaches, including targeted treatment, are needed. However, knowledge about the molecular landscape of LGSC and mSBT is limited. A sample set of 137 pathologically well-defined cases (LGSC, 97; mSBT, 40) was analyzed using capture DNA next-generation sequencing (727 genes) and RNA next-generation sequencing (147 genes) to show the landscape of somatic mutations, gene fusions, expression pattern, and prognostic and predictive relevance. Class 4/5 mutations in the main driver genes (KRAS, BRAF, NRAS, ERBB2, USP9X) were detected in 48% (14/29) of mSBT cases and 63% (47/75) of LGSC cases. The USP9X mutation was detected in only 17% of LGSC cases. RNA next-generation sequencing revealed gene fusions in 6 of 64 LGSC cases (9%) and 2 of 33 mSBT cases (9%), and a heterogeneous expression profile across LGSC and mSBT. No molecular characteristics were associated with greater survival. The somatic genomic and transcriptomic profiles of 35 mSBT and 85 LGSC cases are compared for the first time. Candidate oncogenic gene fusions involving BRAF, FGFR2, or NF1 as a fusion partner were identified. Molecular testing of LGSC may be used in clinical practice to reveal therapeutically significant targets.

Neuroendocrine Marker Expression in Primary Non-neuroendocrine Epithelial Tumors of the Ovary: A Study of 551 Cases.

Expression of neuroendocrine (NE) markers in primary ovarian non-NE epithelial tumors has rarely been evaluated. The aim of our study was to evaluate the expression of the most widely used NE markers in these neoplasms and to determine any prognostic significance of NE marker expression. The cohort consisted of 551 primary ovarian tumors, including serous borderline tumors, low-grade serous carcinomas, high-grade serous carcinomas (HGSC), clear cell carcinomas, endometroid carcinomas, mucinous borderline tumors, and mucinous carcinomas. Immunohistochemical analysis was performed using antibodies against INSM1, synaptophysin, chromogranin, and CD56 on tissue microarray. Positivity for INSM1, synaptophysin, chromogranin, and CD56 was most frequently observed in mucinous tumors (48.7%, 26.0%, 41.5%, and 100%, respectively). The positivity for these NE markers was mostly restricted to nonmucinous elements distributed throughout the tumor. The mucinous borderline tumor and mucinous carcinomas groups had similar proportions of positivity (mucinous borderline tumor: 53%, mucinous carcinomas: 39%). In the other tumor types, except for HGSC, there was only focal expression (5%-10%) or negativity for NE markers. HGSC showed high CD56 expression (in 26% of cases). Survival analysis was only performed for CD56 in HGSC as this was the only group with sufficient positive cases, and it showed no prognostic significance. Except for mucinous tumors, expression of NE markers in non-NE ovarian epithelial tumors is low. CD56 expression in HGSC occurs frequently but is without diagnostic or prognostic value.

Classification of Vietnamese Cashew Nut (Anacardium occidentale L.) Products Using Statistical Algorithms Based on ICP/MS Data: A Study of Food Categorization.

Fingerprinting techniques, which utilize the unique chemical and physical properties of food samples, have emerged as a promising approach for food authentication and traceability. Recent studies have demonstrated significant advancements in food authentication through the use of fingerprinting methods, such as multivariate statistical analysis techniques applied to trace elements and isotope ratios. However, further research is required to optimize these methods and ensure their validity and reliability in real-world applications. In this study, the inductively coupled plasma mass spectrometry (ICP-MS) analytical method was employed to determine the content of 21 elements in 300 cashew nut (Anacardium occidentale L.) samples from 5 brands. Multivariate statistical methods, such as principal components analysis (PCA), were employed to analyze the data obtained and establish the provenance of the cashew nuts. While cashew nuts are widely marketed in many countries, no universal method has been utilized to differentiate the origin of these nuts. Our study represents the initial step in identifying the geographical origin of commercial cashew nuts marketed in Vietnam. The analysis showed significant differences in the means of 21 of the 40 analyzed elements among the cashew nut samples from the 5 brands, including 7Li, 11B, 24Mg, 27Al, 44Ca, 48Ti, 51V, 52Cr, 55Mn, 57Fe, 60Ni, 63Cu, 66Zn, 93Nb, 98Mo, 111Cd, 115In, 121Sb, 138Ba, 208Pb, and 209Bi. The PCA analysis indicated that the cashew nut samples can be accurately classified according to their original locations. This research serves as a prerequisite for future studies involving the combination of elemental composition analysis with statistical classification methods for the accurate establishment of cashew nut provenance, which involves the identification of key markers for the original discrimination of cashew nuts.

Effects of music intervention combined with progressive muscle relaxation on anxiety, depression, stress and quality of life among women with cancer receiving chemotherapy: A pilot randomized controlled trial.

Beneficial effects of music intervention and progressive muscle relaxation alone on psychological issues were reported, however, studies evaluating their combined effects are limited. This study aimed to investigate the feasibility, acceptability and preliminary effects of music intervention combined with progressive muscle relaxation on anxiety, depression, stress, and quality of life among breast and gynaecological cancer patients receiving chemotherapy. METHODS: The study was carried out from March to May 2022 in an oncology hospital in Vietnam. A single-blinded randomized controlled trial was conducted among 24 women with breast and gynaecological cancer undergoing chemotherapy. The intervention group (n = 12) received a face-to-face training program about music listening and progressive muscle relaxation skills. They then performed the self-practice daily at home for three weeks. The control group (n = 12) received standard care, including health assessment, regular health advice and nutrition consultation. Ten participants in the intervention group were interviewed with open-ended questions to explore the acceptability of the intervention. Anxiety, depression and stress were measured using the Depression Anxiety Stress Scale, while The Functional Assessment of Cancer Therapy-General was used to evaluate the quality of life. The outcome measurements were collected at baseline (T0), post-intervention (3rd week, T1) and follow-up (6th week, T2). Appropriate descriptive statistics were used to depict the outcome measures across study time points. RESULTS: A total of 24 patients were eligible to join, and 20 of them completed the study. Greater reductions in anxiety, depression and stress were observed in the intervention group than in the control group at T1 and T2. Greater improvements on quality of life were found in the intervention group than control group at T1 and T2 with respect to T0. The content analysis supported the acceptable intervention of participants through two themes, perceived beneficial effects on psychological and physical health and willingness to keep practising in the future. CONCLUSIONS: Implementing music intervention combined with progressive muscle relaxation is feasible and had a trend in reducing anxiety, depression and stress levels. A larger scale randomized controlled trial is needed to confirm the effect of the intervention on outcomes. TRIAL REGISTRATION: This trial was registered on ClinicalTrials.gov with ID: NCT05262621.

The Prognostic Value of Sequential 18 F-FDG PET/CT Metabolic Parameters in Outcomes of Upper-Third Esophageal Squamous Cell Carcinoma Patients Treated with Definitive Chemoradiotherapy.

Objective The aim of this study is to determine prognostic values of sequential 18 F-FDG PET/CT metabolic parameters in locally advanced esophageal squamous cell carcinoma (ESCC) patients treated with definitive chemoradiotherapy. Materials and Methods Forty locally advanced ESCC patients treated with definitive chemoradiotherapy (dCRT) who received pre-treatment 18 F-FDG PET/CT (PET1) and 3-months post-treatment 18 F-FDG PET/CT (PET2) were enrolled in the prospective study. 18 F-FDG PET parameters of the primary tumor including maximum and mean standardized uptake values (SUVmax, SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were calculated on PET delineated primary tumor. Using Kaplan-Meier curves to estimated overall survival (OS), progression-free survival (PFS), and local-regional control (LRC). Cox regression analysis was performed to find significant prognostic factors for survival. Results With a median follow-up of 13.5 months, the 4-year OS, PFS, and LRC rates were 67.3%, 52.6%, and 53.4% respectively. Patients with MTV 2 > 5.7 had lower OS, PFS, and LRC rates than the lower MTV 2 group (p < 0.05). Univariate Cox regression analysis showed that MTV2 was a significant prognostic factor for OS, PFS, and LRC (p < 0.05). Conclusion MTV parameter of sequential 18 F-FDG PET/CT could be used as a prognostic factor for OS, PFS, and LRC in locally advanced ESCC patients treated with dCRT.

AliSim-HPC: parallel sequence simulator for phylogenetics.

MOTIVATION: Sequence simulation plays a vital role in phylogenetics with many applications, such as evaluating phylogenetic methods, testing hypotheses, and generating training data for machine-learning applications. We recently introduced a new simulator for multiple sequence alignments called AliSim, which outperformed existing tools. However, with the increasing demands of simulating large data sets, AliSim is still slow due to its sequential implementation; for example, to simulate millions of sequence alignments, AliSim took several days or weeks. Parallelization has been used for many phylogenetic inference methods but not yet for sequence simulation. RESULTS: This paper introduces AliSim-HPC, which, for the first time, employs high-performance computing for phylogenetic simulations. AliSim-HPC parallelizes the simulation process at both multi-core and multi-CPU levels using the OpenMP and message passing interface (MPI) libraries, respectively. AliSim-HPC is highly efficient and scalable, which reduces the runtime to simulate 100 large gap-free alignments (30 000 sequences of one million sites) from over one day to 11 min using 256 CPU cores from a cluster with six computing nodes, a 153-fold speedup. While the OpenMP version can only simulate gap-free alignments, the MPI version supports insertion-deletion models like the sequential AliSim. AVAILABILITY AND IMPLEMENTATION: AliSim-HPC is open-source and available as part of the new IQ-TREE version v2.2.3 at https://github.com/iqtree/iqtree2/releases with a user manual at http://www.iqtree.org/doc/AliSim.

Perfluoroalkyl substances in food contact materials: preliminary investigation in Vietnam and global comparison.

Per- and polyfluoroalkyl substances (PFASs) are a group of concerned persistent toxic substances, especially for their application or unintentional formation in food contact materials (FCMs). However, information about the occurrence, sources, and fate of these pollutants in food packaging materials from Vietnam as well as Southeast Asian countries is probably still obscured. In this study, levels of 13 perfluoroalkyl carboxylic acids (PFCAs) and 4 sulfonates (PFSs) were determined in various types of food packaging samples collected from Vietnamese markets. Generally low concentrations of total 17 PFASs (median 0.341; max 624 ng/g) suggested that these compounds were mainly inadvertently produced rather than intentionally added to the packaging materials. A few mochi paper tray samples had relatively high PFAS levels (372-624 ng/g), which were dominated by long-chain (C8-C12) PFCAs. A comprehensive and updated overview of PFASs in FCMs from different countries in the world was also provided. Current database could not provide conclusive trends of PFAS concentrations and profiles in FCMs between continents and countries. The highest levels up to ppm were reported for PFCAs (e.g., PFBA, PFHxA, PFOA, and PFDA) and several fluorotelomer alcohols and carboxylic acids, while PFSs were almost absent in FCMs. FPASs can emit from FCMs, migrate to food, and then contribute to dietary exposure in humans and animals. Additional investigations on the occurrence, sources, behavior and fate, and impacts of PFASs in FCMs are critically needed, especially in emerging and developing countries.

Molecular surveillance of Plasmodium falciparum drug-resistance markers in Vietnam using multiplex amplicon sequencing (2000-2016).

Emergence and spread of Plasmodium falciparum resistance to artemisinin-based combination therapies (ACT) is a major challenge for Greater Mekong Subregion countries in their goal to eliminate malaria by 2030. Tools to efficiently monitor drug resistance beyond resource-demanding therapeutic efficacy studies are necessary. A custom multiplex amplicon sequencing assay based on Illumina technology was designed to target the marker of partial resistance to artemisinin (K13), five candidate modulators of artemisinin resistance, the marker of resistance to chloroquine (crt), and four neutral microsatellite loci. The assay was used to genotype 635 P. falciparum-positive blood samples collected across seven provinces of Vietnam and one of Cambodia between 2000 and 2016. Markers of resistance to artemisinin partner-drugs piperaquine (copy number of plasmepsin-2) and mefloquine (copy number of multidrug-resistance 1) were determined by qPCR. Parasite population structure was further assessed using a 101-SNP barcode. Validated mutations of artemisinin partial resistance in K13 were found in 48.1% of samples, first detection was in 2000, and by 2015 prevalence overcame > 50% in Central Highlands and Binh Phuoc province. K13-C580Y variant became predominant country-wide, quickly replacing an outbreak of K13-I543T in Central Highlands. Mutations in candidate artemisinin resistance modulator genes paralleled the trends of K13 mutants, whereas resistance to piperaquine and mefloquine remained low (≈ 10%) by 2015-2016. Genomic tools applied to malaria surveillance generate comprehensive information on dynamics of drug resistance and population structure and reflect drug efficacy profiles from in vivo studies.