domain: sde sdv

text:
S Moreau

M Audebert

T Jouen

S Loisel°

R Clement

WHAT'S NEW IN THE FIELD OF GEOPHYSICAL METHODS TO STUDY WASTE MASS BEHAVIOUR LANDFILLED

Electrical Resistivity Tomography, ERT, is a well known methodology which can be used in landfill bioreactor to assess leachate injection systems. Even if the measurement equipments are more effective, the greatest advances come from the computing power allowing 3D inversion and statistical data analysis. The complex geometry of landfill lined by geomembranes can now be taken into account by inversion codes to avoid artefacts. Also, a methodology based on the assessment of a wide range of inversion parameters can be used to avoid the a priori choice of one inversion parameter set, depending on ERT user experience, even if the experience is crucial to manage these tools. Statistical application is presented to increase the relevance of ERT data results and have to be encouraged.

INTRODUCTION

Landfilling is still one of the most used waste management methods in France and all over the world. For a long time, the industrial operators act to increase the waste mass biodegradation and the concept of an ingeneering under control is no longer debatable in european countries. Bioreactor concept using leachate recirculation to reach optimal moisture or aeration process to change the microbial metabolism are applied to propose an active management process of municipal solid waste landfills. One of the key points is the measurement tool to study the waste mass evolution impacted by the industrial strategy proposed. Even if leachate and biogas analyses are necessary, they are associated with a global trend of the medium studied and couldn't highlight the evolution of the different areas.

Over the last few decades, geophysical methods were mainly implemented to locate old landfills or leachate plumes using the difference of electrical conductivity, from natural soils surronding those sites, with leachate. Among the available methods, ERT began a robust method to investigate landfill sites (Clement et al., 2010[START_REF] Moreau | Electrical resistivity survey to investigate the influence of leachate recirculation in MSW landfill[END_REF][START_REF] Rosqvist | Investigation of water flow and gas migration in a bioreactor landfill using geoelectrical imaging techniques[END_REF]. Electrical resistivity variations recorded during leachate injection event can be used to study the volume of waste mass impacted by leachate diffusion. Waste temperature is also a physical parameter, which can be studied by this method to detect smouldering fire or areas where exothermic reactions are suspected after air injection.

Recently, the significant evolution in using ERT method on waste landfills is mainly linked to: (1) inversion softwares applied to apparent resistivity measured which can now take into account the 3D geometry of the waste cell with the position of the highly resistive geomembrane (Audebert et al., 2014a), and (2) application of statistical analysis on many inversion results in order to avoid the subjective choice of one inversion parameter set (Audebert et al., 2014b).

A research program was developed with the landfill operator SAS Les Champs Jouault to evaluate the relevance of ERT method to assess leachate injection system and biodegradation evolution of waste mass during the post-closure period. The study presented here focuses on the waste cell, named CELL4, which was implemented in 2012 [START_REF] Grossin-Debattista | Innovative project for in-situ instrumentation of bioreactor landfill: Short and long term monitoring by geophysical and bio-chemical methods[END_REF] with ERT tools and optical fibers for distributed temperature measurements.

CHAMPS JOUAULT LANDFILL BIOREACTOR

The landfill bioreactor configuration of the industrial operator SAS Les Champs Jouault was presented in Sardinia 2013, Figure 1., [START_REF] Grossin-Debattista | Ii Situ innovative full scale instrumentation on bioreactor landfill[END_REF]. All the waste cells are operated according to bioreactor landfill strategy with leachate reinjection. The network implemented is composed of horizontal pipes located on the top of the waste body, used successively for biogas collection and leachate recirculation. The volume of waste deposit cell is around 60 000 m 3 for 10 to 15 m high and a surface of 5000 m 2 . Waste comes from municipal solid waste (50 % of waste mass) and non hazardous industrial waste (50 % of waste mass) collected in western France (Manche, 50).

In 2011, after 10 years of research studies on landfill bioreactors to assess leachate injection system [START_REF] Moreau | Electrical resistivity survey to investigate the influence of leachate recirculation in MSW landfill[END_REF], the research program addressed to the operator SAS Les Champs Jouault was to implement, on the same waste deposit cell, all our measurement techniques used independently before and with the last sotfwares of data interpretation associated. The industrial objectives were to confirm the leachate reinjection strategy applied and to study the potential of the data recorded to monitor the biodegradation evolution of the waste body in order to assess the post closure care.

The physical parameters studied are the electrical resistivity obtained with ERT method and the temperature recorded with fiber optics using the Distributed Temperature Sensing (DTS) method. The equipments used were implemented during landfilling operation with many protections associated to prevent mechanical and chemical impacts of waste mass, especially during the first months after the end of waste storage when important settlements appear.

The waste deposit cell instrumented is named CELL4 and was operated between September 2011 and July 2012 before implementation 4 months later (November 2012) of watertight cover (High density polyethylen HDPE geomembrane + 1 m thick soil) on the top of waste body.

ELECTRICAL RESISTIVITY TOMOGRAPHY ERT

Methods of measurement applied in landfill bioreactor

ERT is a suitable geophysical method to study the electrical resistivity distribution of the waste medium studied. This physical parameter is linked to water content, temperature, leachate conductivity and composition and mechanical characteristics of the waste mass. Several researchs used electrical resistivity variations recorded during leachate recirculation surveys to study the volume of waste impacted by a water content modification (Clement et al, 2010).

In CELL4 of Champs Jouault landfill bioreactor, ERT device is composed of 4 resistivity lines: 1 into the waste mass at 4 m from the bottom of the waste deposit cell and 3 lines at the top under the HDPE geomembrane (Figure 1.). Apparent resistivity is measured with 4 electrodes buried in the medium studied named a quadrupole. 2 electrodes (A and B) are used for current injection, I (A), and 2 other electrodes (M and N), for the potential difference measurement, V (V). Apparent resistivity,  app , is equal to:

) m . ( I V K app     (1)
with K (m) a geometric factor depending on electrode spacing and location. To increase confidence on resistivity data according to depth and volume of measurement, quadrupoles with electrodes coming from different resistivity lines have been imagined. Each resistivity line located at the top allowed creating 1530 quadrupoles with gradient, Wenner and dipole-dipole arrays. Crossed measurements between the lines at the top and at the bottom have generated 7432 quadrupoles with cross dipole-dipole and pole-tripole arrays. No interline measurement was used between the 3 shallow surface lines (Table 1.). A total of 12022 quadrupoles, data of apparent electrical resistivity are recorded each month since November 2012.

To get the distribution of the interpreted electrical resistivity linked to a position in the waste body, an inversion code has to be used. Different solutions are available taking into account different boundary conditions considering an infinite or finite model for example. The landfill bioreactors are always lined by HDPE geomembranes to prevent environmental impacts from leachate and biogas. HDPE geomembranes are highly resistive, around 10 6 Ω.m, compared to the waste resistivity between 5 to 200 Ω.m. Depending on the distance between electrode lines and HDPE geomembranes, electrical current lines could be deflected by the finite boundary condition linked to the presence of the geomembranes and thereby apparent resistivity values could be impacted. To avoid inversion results plagued by artefacts, the choice of the inversion software and its application conditions are crucial for the electrical resistivity interpretation. Table 1. The three geophysical arrays used for the geophysical measurements of waste cell 4: the (a) dipole-dipole, (b) gradient and (c) tripole arrays

Inversion softwares used for landfill data application

Classical inversion softwares such as RES2DINV (Loke et al., 2003) or DC2DInv [START_REF] Günther | University of Mining and Technology[END_REF] have to be used on landfill applications with caution. The most important point is to know that these tools consider infinite boundary conditions. When the electrical resistivity array is far enough away from the geomembrane, application can be validated without risks to highlight arterfacts on the interpreted resistivity results (Audebert et al, 2014). For some years, new inversion softares are available and proposed 3 dimensional for interpretation of electrical resistivity data sets.

We used the inversion software BERT [START_REF] Günther | 3-D modeling and inversion of DC resistivity data incorporating topography -Part II: inversion[END_REF] to calculate interpreted resistivity from apparent resistivity measured. One of its potential on landfill applications is to take into account the complex geometry of the waste cell studied and to adress finite conditions to the boundaries where the HDPE is located. The size and the shape of the waste cells are known by the landfill operators for the recent constructions but could be difficult to get for the old ones.

Moreover, one advantage of BERT is to accept all the resistivity arrays available. The well known configurations such as Wenner or dipole-dipole arrays can be taken into account but our resistivity equipment with quadrupoles using electrodes from the resistivity lines located at the bottom and at the top of the waste body can also be used. These configurations are really interesting to increase the sensitivity area desired which is one of the limitation of the ERT technic when the electrodes are only located at the surface of the medium studied. But to achieve better results in ERT measurements and in inversion interpretation, the ratio between lines spacing and the total length of the resistivity line must not exceed 0.75, ideally 0.5.

To perform the 3D inversions, many parameters have to be selected. In our study, the selection for BERT application was: a Gauss-Newton minimisation with isotropic constraints (no preferential direction), an option to recalculate the Jacobian matrix each iteration and the "blocky model" option to yield sharp changes. Moreover, the value of the regularisation parameter λ has an influence on the interpreted resistivity models. Small values of λ will produce a highly structured model with huge parameter contrasts and large values of λ will provide smooth resistivity models [START_REF] Günther | University of Mining and Technology[END_REF].

Correction and interpretation of electrical resistivity data set

Temperature correction applied to electrical resistivity data set

The distribution of the interpreted resistivity available from inversion process gives us an overview of the heterogeneity of this physical parameter influenced mainly by water content, temperature, leachate conductivity and the composition and mechanical characteristics of waste mass. One of the difficulties is to explain this spatial resistivity distribution which is going to evolve over time.

Among all these influent parameters on electrical resistivity data, the impact of the temperature T can be removed by normalizing the electrical resistivity ρ T to a standard temperature equal to 25°C using the Campbell equation (Keller, G.V. and Frischknecht, F.C., 1966):

)) 25 T ( . 025 . 0 1 .( T 25      (Ω.m) (2)
For long term study, several years of resistivity data comparison, this correction is necessary due to temperature variations induced by waste biodegradation and because of the influence of the boundaries of cell deposit colder than the heart of waste body. To achieve this goal, temperature sensors or temperature modeling can be used to understand the temperature variations of the waste mass from the beginning of the landfilling to the end of the post closure period. The spatial temperature distribution of waste body can be achieved with implementation of fiber optics and applying the Distributed Temperature Sensing method [START_REF] Grossin-Debattista | Ii Situ innovative full scale instrumentation on bioreactor landfill[END_REF]. The critical point of this technique is the location of the fiber optic into the waste mass.

The study of performances of leachate reinjection networks is less impacted by temperature variation because the period is shorter, only one or two days. During this survey, no modification of waste mass is considered and the only temperature variations could come from the difference between the leachate temperature reinjected and the waste mass temperature. This difference is almost always neglected because the influence of water content variations is much more significant than the temperature impact on the variations of electrical resistivity observed.

Multiple inversions managed on electrical resistivity measurements

The computing power of the available computers today allows to carry out a large number of inversion in a very short time. This possibility allows to avoid the a priori choice of the parameters of inversion process based only on the experience of the operators. With only one apparent resistivity data set, several inversions were performed by varying parameters that have a significant influence on the resistivity model: (1) the model norm L1 or L2, which tends to produce a resistivity model with sharp or smooth boundaries, respectively, (2) the anisotropic factor w z (i.e. the flatness ratio), which can emphasise horizontal or vertical structures and (3) the regularisation parameter λ (i.e. the damping factor), which can lead to highly rough-textured models with good data misfit for small λ values or to smooth models with weak data misfit for large λ [START_REF] Labrecque | ERT monitoring of environmental remediation processes[END_REF][START_REF] Tikhonov | Solutions of ill-Posed Problems[END_REF].

The large amount of resistivity data interpreted could lead to a new question of interpretation of all these results. First, the relevance of the inversion results is analysed through the error criteria: RMS (root mean square error between the apparent resistivity measured and calculated by the model) or the CHI 2 which takes into account the error for each quadrupoles of measurement. Secondly, statistical analyse can be provided.

Statistical analysis of electrical resistivity interpreted

The study of leachate reinjection network performances asked the question of the delineation of the infiltration area. The decrease of electrical resistivity induced by leachate resistivity around 0.5-2 Ω.m lower than waste resistivity, from 5 to 200 Ω.m, is calculated according to Equation (3):

(%) 100 . 1 0 t              (3)
where ρ 0 (Ω m) is the resistivity of the initial resistivity model and ρ t the resistivity of the resistivity model at time t. The decrease is very important under the recirculation pipe and decreases the farther away [START_REF] Moreau | Using electrical resistivity 2D tomography in solid waste to evaluate hydraulic performances of leachate recirculation networks in landfills[END_REF].

Applying a k-means iterative clustering algorithm (Dubes and [START_REF] Jain | Algorithms for Clustering Data[END_REF] on resistivity model, the objective of this clustering is to divide model resulting into two clusters: the leachate infiltration area (corresponding to a negative Δρ) and the surrounding waste medium (corresponding to a Δρ close to zero). The k-means clustering method is based on a collection of genes. In the present case, a gene corresponds to a mesh cell of the resistivity model. The distance between two genes is computed with the Euclidian distance between the two Δρ values. The k-means clustering starts with a priori user information on the number of clusters k chosen equal to 2 and with the location of the two corresponding centroids (i.e. the two cells representing each cluster). The centroid representing the infiltration area is located under the leachate injection pipe, whereas the centroid representing the surrounding medium is located far enough from the pipe where no resistivity decrease is expected. Then each gene of the resistivity model is assigned to the centroid from which it has the smallest distance. A new centroid is assigned in computing the average in each cluster. Again, each gene is assigned to the closest centroid. These two last steps are repeated until an optimal solution is found. Audebert (2014b) proposed an interpretation methodology of time-lapse ERT for leachate injection monitoring by associating this approach leading to the partitioning of the resistivity model and the multiple inversions presented before in a methodology called MICS. Applying, for all time steps, the clustering strategy to all the inversion results, the area of waste mass impacted by leachate reinjection is the one that appears systematically on all. This highly stringent criterion of 100% for a mesh cell to belong to the infiltration area was chosen because it allows us to be more confident about the infiltration delimitation obtained.

RESULTS AND DISCUSSION

Application of statistical analysis during leachate reinjection survey

BERT inversion software allowed us to take into account the 3D complex geometry of the waste cell CELL4 and the boundary conditions linked to the HDPE geomembranes, electrically insulated. According to the 4 resistivity lines implemented, the high sensitivity area is presented in Figure 2.

Two dimension results

During leachate injection survey conducted in 2013, resistivity was recorded to calculate variations of electrical resistivity according to Equation (3). As presented before, MICS for Multiple Inversions and a Clustering Strategy is applied to all data sets and the results are illustrated for the same cross section showed in Figure 2.

The results of a standard inversion with a unique inversion parameter set (i.e. the L1-norm, w z =1 and λ=30) are presented in Figure 3 (profile of ρ before infiltration and of Δρ at the two time steps after the beginning of the infiltration) to show the advantages of using MICS. On the two cross sections (Figure 3a) corresponding to two time steps (4.5 h and 6.5 h after the beginning of leachate injection), a decrease in resistivity ranging between -20 and -25% can easily be observed under the leachate injection pipe, corresponding to the infiltration area. As expected, the infiltration area for the second time step is larger than the first one. These standard results also show the problem choosing one Δρ isocontour to delineate the leachate wetting front and the volume of waste wetted by the leachate injection process. Moreover, a 25% increase in resistivity can be observed for the two profiles on the left side of the infiltration area, where no changes are expected. This artefact is most probably due to the inversion process and due to the choice of inversion parameters. Indeed, this artefact disappears with a higher λ value, with a more diffuse infiltration front at the same time. Figure 3b presents the MICS final cross sections for the same two time steps. MICS allows us to obtain an infiltration area with sharp boundaries (blue mesh cells; Figure 3b) taking into account a wide range of resistivity models and avoiding the choice of a Δρ isocontour. As previously, the second infiltration area is larger than the first one. Moreover, MICS eliminates the artefact which appeared on the standard inversion results (Figure 3a).

Three dimension results

Using the same data set, the zone impacted by leachate injection and calculated using MICS can be represented in 3D inside the high sensitivity area for five time steps after the beginning of the injection (i.e. at 2 h, 2.5 h, 5 h, 6.5 h, and 9 h). Figure 4 presents the volume delimited by MICS, V MICS , for each time step. V MICS increases from 500 m 3 at 2 h to 910 m 3 , 9 h after the beginning of the injection.

A parameter, ε a , corresponds to an estimate of the pore volume used for leachate flow and was defined as an "apparent porosity" by Clément et al. [2011a]. ε a can be expressed as follow:

(%) 100 . V V MICS inj a           (4)
where V inj corresponds to the leachate injected volume into the waste mass determined from flow rate measurements and V MICS the volume delimited by the MICS methodology. This parameter was calculated by Audebert et al., 2016a to study hydrodynamic behavior of waste media The very low ε a values obtained in this study indicate that a very small fraction of the pore volume is available for leachate flow. The authors assumed that this small fraction of the pore volume implies preferential pathways into the waste medium. Following these results, Audebert et al. 2016b proposed to model waste medium with a dual continuum approach, in considering two interacting pore domains, the first one corresponding to preferential pathways (or macropores) with rapid water flow and the second one corresponding to the surrounding matrix domain (or micropores) with slow water movement.

CONCLUSIONS

Electrical resistivity tomography is becoming a popular method to monitor electrical resistivity variations during the leachate recirculation process in bioreactors for example. The inversion softwares used to calculate interpreted resistivity from apparent resistivity measurements are numerous but it is important to know their good conditions of use in order to avoid errors during resistivity data interpretation.

BERT inversion software takes into account the complex geometry of different waste cells in landfill, all the resistivity arrays using resistivity lines implemented at different levels in waste mass and many parameters to fit the data. The large amont of results induces statistical analysis for resistivity data interpretation in time. MICS is a strategy to manage many inversions on the same apparent resistivity data in varyring inversion parameters and to propose a clustering methodology in order to interpret one final result. Applied to leachate injection surveys, the waste mass can be separated in two clusters corresponding to the leachate infiltration area and to the surrounding waste medium. The results propose and validate a new methodology which will allow bioreactor landfill operators to delimitate the leachate infiltration area more accurately compared to the classical ERT inversion results presenting smoothed contours. This methodology also opens up many research perspectives. Indeed, a better delineation of the infiltration area may allow us to assess waste hydrodynamic properties using a combined hydro-geophysical approach including MICS.

Electrical Resistivity Tomography and the interpretation data associated applied on municipal solid waste landfill have to be encouraged to evaluate the potential of this methodology to monitor waste biodegradation during post-closure period. For this purpose, a research program is underway on SAS Les Champs Jouault landfill.

AKNOWLEDGEMENTS

Figure 1 .

 1 Figure 1. (a) Location and geometry of experimental waste deposit CELL4 and (b) distribution of electrode lines.

Figure 2 .

 2 Figure 2. High sensitivity area of the ERT measurements related to resistivity lines implemented and one cross section of interpreted resistivity

Figure 3

 3 Figure 3. a) Resistivity models for two time steps (4.5 h and 6.5 h) corresponding to a standard inversion with one inversion parameter set, b) final cross sections resulting from MICS

Figure 4 .

 4 Figure 4. MICS results for five time steps after the beginning of the injection, volume of leachate injected V inj and volume delimited by MICS methodology V MICS

This work was supported by the Région Basse Normandie. We would also thank all the Champs Jouault company staff for their assistance during the sensors implementation and Acome Company for the supply of optical fiber.


Didier Ménard
email: dmenard@pasteur-kh.org

Nimol Khim

Johann Beghain

Ayola A Adegnika

Mohammad Shafiul-Alam

Olukemi Amodu

Ghulam Rahim-Awab

Céline Barnadas

Antoine Berry

M.D Yap Boum

J Bustos

J.-H Cao

L Chen

L Collet

G.-D Cui

A Thakur

D Dieye

M A Djallé

C E Dorkenoo

F.-E.-C J Eboumbou- Moukoko

T Espino

M.-F Fandeur

A A Ferreira-Da-Cruz

H.-P Fola

A M Fuehrer

S Hassan

B Herrera

S Hongvanthong

M L Houzé

M Ibrahim

L Jahirul-Karim

S Jiang

W Kano

M Ali-Khan

P G Khanthavong

M Kremsner

R Lacerda

M Leang

M Leelawong

K Li

J.-B Lin

S Mazarati

I Ménard

H Morlais

L Muhindo-Mavoko

K Musset

M Na-Bangchang

K Nambozi

H Niaré

J.-B Noedl

D R Ouédraogo

B Pillai

B Pradines

M Quang-Phuc

M Ramharter

J Randrianarivelojosia

A Sattabongkot

K D Sheikh-Omar

S B Silué

C Sirima

D Sutherland

R Syafruddin

L.-H Tahar

O A Tang

P Touré

I Tshibangu-Wa- Tshibangu

M Vigan-Womas

L Warsame

S Wini

S Zakeri

R Kim

L Eam

C Berne

S Khean

M Chy

K Ken

L Loch

V Canier

E Duru

J.-C Legrand

B Barale

J Stokes

B Straimer

D A Witkowski

C Fidock

P Rogier

F Ringwald

O Ariey

Mercereau-Puijalon

A Worldwide Map of Plasmodium falciparum K13-Propeller Polymorphisms

are active against a large range of intraerythrocytic developmental stages, but their usefulness is curtailed by ring-stage resistance. 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[START_REF] Roper | Intercontinental spread of pyrimethamine-resistant malaria[END_REF][START_REF] Wongsrichanalai | Epidemiology of drug-resistant malaria[END_REF][START_REF] Trape | The public health impact of chloroquine resistance in Africa[END_REF][START_REF] Trape | Combating malaria in Africa[END_REF][START_REF] Vinayak | Origin and evolution of sulfadoxine resistant Plasmodium falciparum[END_REF] Therapeutic efficacy studies are the standard method for determining the efficacy of antimalarial drugs, [START_REF]Methods for surveillance of antimalarial drug efficacy[END_REF] but resource constraints restrict the numbers of sites and patients studied each year. The recent discovery of mutations in portions of a P. falciparum gene encoding kelch (K13)-propeller domains as the primary determinant of artemisinin resistance provided unprecedented opportunities for improving resistance monitoring. [START_REF] Ariey | A molecular marker of artemisinin-resistant Plasmodium falciparum malaria[END_REF][START_REF] Straimer | Drug resistance: K13-propeller mutations confer artemisinin resistance in Plasmodium falciparum clinical isolates[END_REF] To date, 13 independent K13 mutations have been shown to be associated with clinical resistance, [START_REF]Status report on artemisinin and ACT resistance[END_REF][START_REF] Ashley | Spread of artemisinin resistance in Plasmodium falciparum malaria[END_REF][START_REF] Huang | A single mutation in K13 predominates in southern China and is associated with delayed clearance of Plasmodium falciparum following artemisinin treatment[END_REF][START_REF] Amaratunga | Plasmodium falciparum founder populations in western Cambodia have reduced artemisinin sensitivity in vitro[END_REF][START_REF] Amaratunga | Artemisinin resistance in Plasmodium falciparum[END_REF][START_REF] Takala-Harrison | Independent emergence of artemisinin resistance mutations among Plasmodium falciparum in Southeast Asia[END_REF] with evidence of independent emergence of the same mutation in different geographic areas. [START_REF] Takala-Harrison | Independent emergence of artemisinin resistance mutations among Plasmodium falciparum in Southeast Asia[END_REF][START_REF] Miotto | Genetic architecture of artemisinin-resistant Plasmodium falciparum[END_REF] Four Asian mutations (C580Y, R539T, I543T, and Y493H) have been validated in vitro. [START_REF] Ariey | A molecular marker of artemisinin-resistant Plasmodium falciparum malaria[END_REF][START_REF] Straimer | Drug resistance: K13-propeller mutations confer artemisinin resistance in Plasmodium falciparum clinical isolates[END_REF][START_REF] Amaratunga | Plasmodium falciparum founder populations in western Cambodia have reduced artemisinin sensitivity in vitro[END_REF][START_REF] Amaratunga | Artemisinin resistance in Plasmodium falciparum[END_REF] In regions with no documented clinical artemisinin resistance, scattered studies have indicated that K13 mutations are rare. 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[START_REF] Borrmann | Genome-wide screen identifies new candidate genes associated with artemisinin susceptibility in Plasmodium falciparum in Kenya[END_REF][START_REF] Henriques | Directional selection at the pfmdr1, pfcrt, pfubp1, and pfap2mu loci of Plasmodium falciparum in Kenyan children treated with ACT[END_REF][START_REF] Hawkes | Slow clearance of Plasmodium falciparum in severe pediatric malaria, Uganda, 2011-2013[END_REF] No data are available for large regions of Africa, South America, Oceania, Central and East Asia, Indonesia, and the Philippines. The risk of the emergence or dissemination of resistance has spurred increased efforts to track artemisinin resistance in all geographic areas in which malaria is endemic. The use of a molecular indicator of artemisinin resistance is particularly timely, since rapid, large-scale screening is needed to provide an early warning about emergence or invasion events. [START_REF] Menard | Towards real-time monitoring of artemisinin resistance[END_REF][START_REF] Roper | Molecular surveillance for artemisinin resistance in Africa[END_REF] To assess the global distribution of K13 polymorphisms, we launched in 2014 the K13 Artemisinin Resistance Multicenter Assessment (KARMA) study, in which we analyzed parasites that were collected from regions in which malaria is endemic, using a dedicated molecular toolbox and validation procedures for sequence data. Sequencing of the K13propeller domain was combined with an analysis of flanking haplotypes to ascertain the origin and dissemination of specific mutations. We performed genome editing to confirm the phenotypic effect of the most frequent African A578S mutation. This worldwide survey was designed to provide critical information for drug policymakers and to outline methods for future surveillance activities.

METHODS

STUDY DESIGN, SAMPLING, AND OVERSIGHT

Investigators from Institut Pasteur in Paris and in Cambodia designed the study, which was coordinated by the Cambodian branch. According to the study design, the investigators planned to analyze 200 blood samples positive for P. falciparum that had been collected since 2012 and to contribute blood samples, K13 products obtained on polymerase-chainreaction (PCR) assay, or K13 sequence data (Fig. S1 in the Supplementary Appendix, available with the full text of this article at NEJM.org). Sites that had few available samples or samples that were collected before 2012 were included if they were located in regions in which K13 diversity had not been documented. The study was approved by a national or institutional ethics committee or other appropriate authority at each site, and all investigators signed a declaration of ethical clearance. Samples were obtained from patients seeking treatment at sites involved in national surveys of antimalarial drug resistance, from patients enrolled in therapeutic efficacy studies, [START_REF]Methods for surveillance of antimalarial drug efficacy[END_REF] from asymptomatic participants who were enrolled in surveillance programs, and from travelers returning to Europe with malaria. Investigators at Institut Pasteur in Cambodia collected all biologic material and performed K13 sequence and haplotype analysis; they also conducted quality-control analyses, and they vouch for the accuracy and completeness of the molecular data. Investigators who conducted therapeutic efficacy studies vouch for the accuracy and completeness of the clinical data. The sponsors had no role in the study design or in the collection or analysis of the data. There was no confidentiality agreement between the sponsors and the investigators.

GENOTYPING

The Institut Pasteur in Cambodia provided reagents, blinded quality-control samples, and standard operating procedures as a K13 toolbox (Table S1 in the Supplementary Appendix). DNA extraction and amplification of the K13-propeller domain (codons 440-680, 720 bp) [START_REF] Ariey | A molecular marker of artemisinin-resistant Plasmodium falciparum malaria[END_REF] were performed accordingly. PCR products were sequenced by Macrogen. We analyzed electropherograms on both strands, using PF3D7_1343700 as the reference sequence. We performed external quality assessment that included proficiency testing (in which 6 blinded quality-control samples were tested in each 96-well sequencing plate by each partner) and external quality control (in which 359 blood samples [2.6%] were independently retested) (Fig. S2 in the Supplementary Appendix). Isolates with mixed alleles were considered to be mutated for the purposes of mutation-frequency estimation.

The PF3D7_1337500 (K13_151) and PF3D7_1339700 (K13_159) loci were amplified (Table S2 in the Supplementary Appendix) and DNA sequences were analyzed as indicated above. Individual alleles were identified for each locus and haplotypes generated.

A578S GENE EDITING

We performed zinc-finger nuclease engineering, plasmid construction, and gene editing of the Dd2 line (which was collected in Indochina) with the A578S mutation. [START_REF] Straimer | Drug resistance: K13-propeller mutations confer artemisinin resistance in Plasmodium falciparum clinical isolates[END_REF] We assessed the in vitro susceptibility to artemisinin of the Dd2 A578S mutation, the Dd2 parent, and the Dd2 C580Y mutation (as a positive control), using the ring-stage survival assay (RSA) performed in red cells that had been infected with the malaria parasite in its ring stage (the developmental stage that is associated with resistance to artemisinin) for an estimated 0 to 3 hours. The cells were then exposed to dihydroartemisinin (the main metabolite of all artemisinin derivatives), and drug susceptibility was measured at 72 hours. [START_REF] Witkowski | Novel phenotypic assays for the detection of artemisinin-resistant Plasmodium falciparum malaria in Cambodia: in-vitro and ex-vivo drugresponse studies[END_REF][START_REF] Straimer | Drug resistance: K13-propeller mutations confer artemisinin resistance in Plasmodium falciparum clinical isolates[END_REF]

GEOGRAPHICAL MAPPING

We calculated the proportion of parasites with a 3D7, wild-type allele (which is associated with artemisinin susceptibility) in each country and recorded the geospatial coordinates. To generate graphical maps, we interpolated data using ordinary kriging methods (or Gaussian process regression) or inverse distance weighting. (Details about the methods are provided in the Supplementary Appendix.)

STATISTICAL ANALYSIS

We expressed quantitative data as medians and interquartile ranges or as means and 95% confidence intervals. We used the Mann-Whitney test or t-tests of independent samples to compare continuous variables and the chi-square test or Fisher's exact test to compare categorical variables. We obtained estimates of nucleotide diversity, [START_REF] Nei | Mathematical model for studying genetic variation in terms of restriction endonucleases[END_REF] haplotype diversity, [START_REF] Nei | Molecular evolutionary genetics[END_REF] and Tajima's D test [START_REF] Kimura | The neutral theory of molecular evolution[END_REF] using DnaSP software, version 5. [START_REF] Librado | DnaSP v5: a software for comprehensive analysis of DNA polymorphism data[END_REF] All reported P values are twosided, and a P value of less than 0.05 was considered to indicate statistical significance. Data were analyzed with the use of Microsoft Excel and MedCalc software, version 12.

RESULTS

SAMPLE COLLECTION

From May through December 2014, we gathered 14,037 samples from 163 sites in 59 countries. The samples included 11,854 (84.4%) from resident malaria patients in 40 countries, 1232 (8.8%) from residents with asymptomatic infections in 11 countries, and 951 (6.8%) from travelers returning with falciparum malaria from 40 countries (Fig. S3 in the Supplementary Appendix). The samples included those obtained from 2450 patients with malaria (2367 residents and 83 travelers) for whom follow-up data on day 3 were available (Table S3 in the Supplementary Appendix). We collected 4156 samples in 2012, 6440 samples in 2013, and 1671 samples in 2014 (>87% of all samples). Data regarding sample size, sex ratio, age, and parasitemia ranges according to study site are provided in Table S4 in the Supplementary Appendix.

K13-PROPELLER SEQUENCE POLYMORPHISMS

Sequences were generated for 13,157 (93.7%) samples; 880 samples from 35 sites had sequences that could not be interpreted because of poor quality or an insufficient quantity of DNA. Nearly all the samples (99.2%) contained a single K13 allele; 108 of 111 polyclonal infections were identified in samples obtained in Africa. The minor allele in a mixed sample was detected when its proportion was more than 20% (Fig. S4 in the Supplementary Appendix).

Overall, 1250 samples (9.5%) had 1295 K13 mutations, including 1097 (84.7%) with nonsynonymous mutations and 198 (15.3%) with synonymous mutations; 186 alleles were identified (Table S5 and Fig. S5 in the Supplementary Appendix). Among these alleles, there were 108 nonsynonymous mutations, of which 70 were newly identified and 38 were among 103 that had been reported previously. [START_REF] Ashley | Spread of artemisinin resistance in Plasmodium falciparum malaria[END_REF][START_REF] Huang | A single mutation in K13 predominates in southern China and is associated with delayed clearance of Plasmodium falciparum following artemisinin treatment[END_REF][START_REF] Thriemer | Delayed parasite clearance after treatment with dihydroartemisinin-piperaquine in Plasmodium falciparum malaria patients in central Vietnam[END_REF][START_REF] Ariey | A molecular marker of artemisinin-resistant Plasmodium falciparum malaria[END_REF][START_REF] Takala-Harrison | Independent emergence of artemisinin resistance mutations among Plasmodium falciparum in Southeast Asia[END_REF][START_REF] Mishra | Surveillance of artemisinin resistance in Plasmodium falciparum in India using the kelch13 molecular marker[END_REF][START_REF] Huang | Molecular epidemiology of drug resistance markers of Plasmodium falciparum in Yunnan Province, China[END_REF][START_REF] Feng | Amplification of pfmdr1, pfcrt, pvmdr1, and K13 propeller polymorphisms associated with Plasmodium falciparum and Plasmodium vivax isolates from the China-Myanmar border[END_REF][START_REF] Mohon | Mutations in Plasmodium falciparum K13 propeller gene from Bangladesh (2009-2013)[END_REF][START_REF] Conrad | Polymorphisms in K13 and falcipain-2 associated with artemisinin resistance are not prevalent in Plasmodium falciparum isolated from Ugandan children[END_REF][START_REF] Isozumi | Novel mutations in K13 propeller gene of artemisininresistant Plasmodium falciparum[END_REF][START_REF] Kamau | K13-propeller polymorphisms in Plasmodium falciparum parasites from sub-Saharan Africa[END_REF][START_REF] Ouattara | Polymorphisms in the K13-propeller gene in artemisininsusceptible Plasmodium falciparum parasites from Bougoula-Hameau and Bandiagara, Mali[END_REF][START_REF] Taylor | Absence of putative artemisinin resistance mutations among Plasmodium falciparum in Sub-Saharan Africa: a molecular epidemiologic study[END_REF][START_REF] Cheeseman | Pooled sequencing and rare variant association tests for identifying the determinants of emerging drug resistance in malaria parasites[END_REF][START_REF] Nyunt | Molecular assessment of artemisinin resistance markers, polymorphisms in the k13 propeller, and a multidrug-resistance gene in the eastern and western border areas of Myanmar[END_REF][START_REF] Wang | Prevalence of K13-propeller polymorphisms in Plasmodium falciparum from China-Myanmar border in 2007-2012[END_REF][START_REF] Tun | Spread of artemisinin-resistant Plasmodium falciparum in Myanmar: a cross-sectional survey of the K13 molecular marker[END_REF][START_REF] Talundzic | Selection and spread of artemisinin-resistant alleles in Thailand prior to the global artemisinin resistance containment campaign[END_REF][START_REF] Feng | Evaluation of antimalarial resistance marker polymorphism in returned migrant workers in China[END_REF][START_REF] Malariagen | P. falciparum Community Project data (beta release)[END_REF] Only 9 nonsynonymous mutations (C580Y, F446I, R539T, A578S, Y493H, P574L, P553L, N458Y, and R561H) had a frequency of more than 1%; 72 alleles were observed only once (Tables S6 andS7 in the Supplementary Appendix).

There was marked geographic disparity in the proportion and distribution of K13 polymorphisms. We measured nucleotide diversity, haplotype diversity, and Tajima's D ratio to highlight the continent-specific frequency of mutant alleles (Fig. S6 in the Supplementary Appendix). Of the 1097 nonsynonymous mutations, 957 (87.2%) were observed in samples obtained in Asia, and 169 of the 198 synonymous mutations (85.4%) were observed in samples obtained in Africa.

CONTINENTAL DISTRIBUTION AND PROPORTION OF K13-PROPELLER MUTATIONS

The proportion of K13 nonsynonymous mutations was heterogeneous in Asia, ranging from fixed (>95%) to very high (80 to 94%) in western Cambodia (Fig. S7 in the Supplementary Appendix), to intermediate (40 to 50%) in Myanmar and Vietnam, to moderate (10 to 20%) in eastern Cambodia, Thailand, China, and Laos, to low (<5%) elsewhere (Fig. 1). In South America, Oceania, and Africa, K13 nonsynonymous mutations were uncommon, except for a few African countries (range, 3.0 to 8.3% in Gambia, Central African Republic, Zambia, Comoros, Guinea, Kenya, and Chad). K13 nonsynonymous mutations were not detected in 27 countries from which samples were obtained (19 in Africa, 2 in Asia, 1 in Oceania, and 5 in South America), as shown on maps of wild-type allele distribution (Fig. 2). Individual K13 nonsynonymous mutations showed restricted geographic localization. In Asia, two distinct areas were identified, with different frequencies of individual mutations: one area that includes Cambodia, Vietnam, and Laos, where C580Y, R539T, Y493H, and I543T mutations were frequent or specific, and a second area that includes western Thailand, Myanmar, and China, where F446I, N458Y, P574L, and R561H mutations were specific (Fig. S8 in the Supplementary Appendix). The P553L allele was distributed in the two areas (Fig. 3).

In Africa, no Asian artemisinin-resistance allele was observed among 9542 sequences, but 150 distinct alleles were identified, 92% of which were found in only one or two samples. Apart from A578S, V589I, S522C, V534A, F583L, and G665C, most alleles were Africaspecific and localized. A578S, which ranked fourth in abundance among mutant isolates, was observed in 1 sample from Thailand and 41 samples from Africa (Tables S6 andS7 in the Supplementary Appendix).

NEW EMERGENCE OR DISSEMINATION?

We investigated the genetic relatedness of isolates harboring the same frequent K13 mutation by assessing the polymorphism of two neighboring loci. We observed 10 alleles for K13_151 and 42 alleles for K13_159, which resulted in 80 flanking haplotypes (Tables S8 and S9 in the Supplementary Appendix). This assessment identified numerous emergence events alongside spreading of a small group of mutations for artemisinin resistance (Fig. 3). Of 17 distinct C580Y haplotypes, 3 common haplotypes were distributed across Cambodia, Vietnam, and Laos and 14 in specific areas of Cambodia, Thailand, and Vietnam. Similar observations were made for the Y493H, R539T, and I543T haplotypes, with probable westto-east dissemination for the Y493H and R539T mutations and movement from Vietnam to eastern Cambodia for I543T mutations. Three prevalent F446I haplotypes were distributed across Myanmar, and 8 haplotypes were localized within China or Myanmar. Site-specific localization was observed for the 5 P553L haplotypes, the 6 P574L haplotypes, and the 2 E605K and R561H haplotypes (Fig. S9 in the Supplementary Appendix). For the common African mutation A578S, we identified multiple independent events of emergence (31 different haplotypes among 35 isolates) (Fig. S10 in the Supplementary Appendix).

TESTING OF THE A578S ALLELE FOR ARTEMISININ RESISTANCE

We wanted to evaluate whether the A578S mutation had a selective advantage against artemisinin, since there have been reports about a conflicting phenotype of the allele, [START_REF] Ashley | Spread of artemisinin resistance in Plasmodium falciparum malaria[END_REF][START_REF] Mishra | Surveillance of artemisinin resistance in Plasmodium falciparum in India using the kelch13 molecular marker[END_REF][START_REF] Cooper | Lack of artemisinin resistance in Plasmodium falciparum in Uganda based on parasitological and molecular assays[END_REF][START_REF] Hawkes | Slow clearance of Plasmodium falciparum in severe pediatric malaria, Uganda, 2011-2013[END_REF] which has been found at multiple sites in Africa and Asia [START_REF] Ashley | Spread of artemisinin resistance in Plasmodium falciparum malaria[END_REF][START_REF] Mishra | Surveillance of artemisinin resistance in Plasmodium falciparum in India using the kelch13 molecular marker[END_REF][START_REF] Huang | Molecular epidemiology of drug resistance markers of Plasmodium falciparum in Yunnan Province, China[END_REF][START_REF] Mohon | Mutations in Plasmodium falciparum K13 propeller gene from Bangladesh (2009-2013)[END_REF][START_REF] Conrad | Polymorphisms in K13 and falcipain-2 associated with artemisinin resistance are not prevalent in Plasmodium falciparum isolated from Ugandan children[END_REF][START_REF] Cooper | Lack of artemisinin resistance in Plasmodium falciparum in Uganda based on parasitological and molecular assays[END_REF][START_REF] Isozumi | Novel mutations in K13 propeller gene of artemisininresistant Plasmodium falciparum[END_REF][START_REF] Kamau | K13-propeller polymorphisms in Plasmodium falciparum parasites from sub-Saharan Africa[END_REF][START_REF] Ouattara | Polymorphisms in the K13-propeller gene in artemisininsusceptible Plasmodium falciparum parasites from Bougoula-Hameau and Bandiagara, Mali[END_REF][START_REF] Taylor | Absence of putative artemisinin resistance mutations among Plasmodium falciparum in Sub-Saharan Africa: a molecular epidemiologic study[END_REF][START_REF] Malariagen | P. falciparum Community Project data (beta release)[END_REF] (Table S6 and Fig. S10 in the Supplementary Appendix). We therefore introduced the A578S mutation in the Dd2 line that had been used to investigate the effect of several Asian K13 mutations. [START_REF] Straimer | Drug resistance: K13-propeller mutations confer artemisinin resistance in Plasmodium falciparum clinical isolates[END_REF] The Dd2 line with the A578S mutation was susceptible to artemisinin on RSA. The survival rate (mean [±SE], 0.29±0.11%) was equivalent to that in the parental line (mean, 0.14±0.03%), which was unlike the survival rate (mean, 4.01±0.16%) in the positive control Dd2 line with the C580Y mutation (Fig. S11 in the Supplementary Appendix).

K13 MUTATIONS AND POSITIVITY RATE

Our sampling included 2450 patients in whom the presence of parasites was assessed on day 3 after 7 days of artesunate monotherapy or a standard 3-day course of artemisinin-based combination therapy. Of these patients, 121 had positive parasite results on day 3 (Table S3 in the Supplementary Appendix). Of 40 nonsynonymous mutations that were detected in isolates obtained from these patients, only 8 were associated with positivity on day 3 (F446I, N458Y, N537D, R539T, I543T, P553L, P574L, and C580Y); all these alleles were observed only in Southeast Asia and China (Fig. S12 in the Supplementary Appendix). No circulating blood-stage parasites were found on day 3 in samples obtained from 1533 African patients in 25 countries, except for 9 patients who were each carrying a wild-type allele. The 9 African patients who were carrying an A578S allele on day 0 were parasite-free on day 3.

DISCUSSION

In this study, we have attempted to profile the global distribution of K13 polymorphisms. The breadth of this survey, with 163 sites that were sampled for molecular mapping and 36 countries with data regarding therapeutic efficacy, led us to conclude that artemisinin resistance is confined to Southeast Asia and China, where resistance-associated K13 mutations have reached intermediate frequency to fixation. This status contrasts with the situation in South America, Oceania, the Philippines, and Central and South Asia, which are essentially free of nonsynonymous K13 mutations. We observed many highly diverse, lowfrequency nonsynonymous mutations in Africa, although none of these mutations were associated with clinical artemisinin resistance, as assessed by the presence of parasites on day 3 after artesunate monotherapy or 3-day treatment with artemisinin-based combination therapy. We saw no evidence of invasion of Africa by Asian resistance-conferring alleles, a finding that was consistent with the results of previous smaller studies. [START_REF] Conrad | Polymorphisms in K13 and falcipain-2 associated with artemisinin resistance are not prevalent in Plasmodium falciparum isolated from Ugandan children[END_REF][START_REF] Cooper | Lack of artemisinin resistance in Plasmodium falciparum in Uganda based on parasitological and molecular assays[END_REF][START_REF] Isozumi | Novel mutations in K13 propeller gene of artemisininresistant Plasmodium falciparum[END_REF][START_REF] Kamau | K13-propeller polymorphisms in Plasmodium falciparum parasites from sub-Saharan Africa[END_REF][START_REF] Taylor | Absence of putative artemisinin resistance mutations among Plasmodium falciparum in Sub-Saharan Africa: a molecular epidemiologic study[END_REF][START_REF] Torrentino-Madamet | Limited polymorphisms in k13 gene in Plasmodium falciparum isolates from Dakar, Senegal in 2012-2013[END_REF][START_REF] Hawkes | Slow clearance of Plasmodium falciparum in severe pediatric malaria, Uganda, 2011-2013[END_REF][START_REF] Escobar | Polymorphisms in Plasmodium falciparum K13-propeller in Angola and Mozambique after the introduction of the ACTs[END_REF][START_REF] Miotto | Multiple populations of artemisinin-resistant Plasmodium falciparum in Cambodia[END_REF] Finally, no positive selection was observed aside from that in Asia, which suggests no immediate threat to artemisinin efficacy in most countries in which malaria is endemic.

In the two independent foci of resistance that have been identified in the region that includes Cambodia, Vietnam, and Laos and the one that includes western Thailand, Myanmar, and China, we observed many more examples of emergence of resistance-associated mutations than had been reported previously. [START_REF] Takala-Harrison | Independent emergence of artemisinin resistance mutations among Plasmodium falciparum in Southeast Asia[END_REF][START_REF] Miotto | Genetic architecture of artemisinin-resistant Plasmodium falciparum[END_REF][START_REF] Talundzic | Selection and spread of artemisinin-resistant alleles in Thailand prior to the global artemisinin resistance containment campaign[END_REF] This finding indicates that contemporary artemisinin resistance is a result of numerous independent emergence events involving the same mutations together with the dissemination of a small group of endemic mutations (Fig. 3). The endemic mutations are probably the oldest ones, as suggested by an analysis of our archived samples, which showed that all four common contemporary C580Y haplotypes that were observed in Cambodia were already quite frequent 10 years earlier in Pailin province, the epicenter of the emergence of artemisinin-resistant parasites (Table S9 in the Supplementary Appendix). [START_REF] Dondorp | Artemisinin resistance in Plasmodium falciparum malaria[END_REF][START_REF] Noedl | Evidence of artemisinin-resistant malaria in western Cambodia[END_REF] We observed no overlap between the sets of mutations and haplotypes in the two main resistance areas in Asia, which suggests the presence of different selective pressures in the two areas. Such differences may stem from the use of different artemisinin-based combination therapies as first-line treatment, different types of founder populations, [START_REF] Miotto | Genetic architecture of artemisinin-resistant Plasmodium falciparum[END_REF][START_REF] Miotto | Multiple populations of artemisinin-resistant Plasmodium falciparum in Cambodia[END_REF] or different epidemiologic ecosystems.

To date, 13 nonsynonymous mutations have been associated with slow parasite clearance, [START_REF]Status report on artemisinin and ACT resistance[END_REF][START_REF] Ashley | Spread of artemisinin resistance in Plasmodium falciparum malaria[END_REF][START_REF] Ariey | A molecular marker of artemisinin-resistant Plasmodium falciparum malaria[END_REF][START_REF] Straimer | Drug resistance: K13-propeller mutations confer artemisinin resistance in Plasmodium falciparum clinical isolates[END_REF][START_REF] Takala-Harrison | Independent emergence of artemisinin resistance mutations among Plasmodium falciparum in Southeast Asia[END_REF][START_REF] Huang | Molecular epidemiology of drug resistance markers of Plasmodium falciparum in Yunnan Province, China[END_REF] and 4 mutations have been validated as conferring an increased rate of ringstage survival in drug-resistant field isolates in vitro [START_REF] Ariey | A molecular marker of artemisinin-resistant Plasmodium falciparum malaria[END_REF] or in gene-edited parasite lines. [START_REF] Menard | Towards real-time monitoring of artemisinin resistance[END_REF] In the KARMA study, 8 nonsynonymous mutations that were observed in Southeast Asia and China -F446I, N458Y, N537D, R539T, I543T, P553L, P574L, and C580Ywere associated with positive results on day 3, findings that were consistent with data from previous studies that used parasite-clearance half-lives to identify artemisinin resistance [START_REF] Ashley | Spread of artemisinin resistance in Plasmodium falciparum malaria[END_REF][START_REF] Huang | A single mutation in K13 predominates in southern China and is associated with delayed clearance of Plasmodium falciparum following artemisinin treatment[END_REF][START_REF] Ariey | A molecular marker of artemisinin-resistant Plasmodium falciparum malaria[END_REF][START_REF] Amaratunga | Plasmodium falciparum founder populations in western Cambodia have reduced artemisinin sensitivity in vitro[END_REF][START_REF] Amaratunga | Artemisinin resistance in Plasmodium falciparum[END_REF][START_REF] Takala-Harrison | Independent emergence of artemisinin resistance mutations among Plasmodium falciparum in Southeast Asia[END_REF] and that provide further evidence of the positivity rate on day 3 as a sensitive indicator of clinical resistance to artemisinins. The A578S mutation that has been commonly observed in Africa [START_REF] Ashley | Spread of artemisinin resistance in Plasmodium falciparum malaria[END_REF][START_REF] Conrad | Polymorphisms in K13 and falcipain-2 associated with artemisinin resistance are not prevalent in Plasmodium falciparum isolated from Ugandan children[END_REF][START_REF] Cooper | Lack of artemisinin resistance in Plasmodium falciparum in Uganda based on parasitological and molecular assays[END_REF][START_REF] Isozumi | Novel mutations in K13 propeller gene of artemisininresistant Plasmodium falciparum[END_REF][START_REF] Kamau | K13-propeller polymorphisms in Plasmodium falciparum parasites from sub-Saharan Africa[END_REF][START_REF] Ouattara | Polymorphisms in the K13-propeller gene in artemisininsusceptible Plasmodium falciparum parasites from Bougoula-Hameau and Bandiagara, Mali[END_REF][START_REF] Taylor | Absence of putative artemisinin resistance mutations among Plasmodium falciparum in Sub-Saharan Africa: a molecular epidemiologic study[END_REF][START_REF] Hawkes | Slow clearance of Plasmodium falciparum in severe pediatric malaria, Uganda, 2011-2013[END_REF] and detected in India, [START_REF] Mishra | Surveillance of artemisinin resistance in Plasmodium falciparum in India using the kelch13 molecular marker[END_REF] Bangladesh, [START_REF] Mohon | Mutations in Plasmodium falciparum K13 propeller gene from Bangladesh (2009-2013)[END_REF] and Thailand was sensitive on in vitro RSAs; this finding was unrelated to positivity on day 3. Numerous events of independent emergence were observed with no evidence of dissemination. These findings are consistent with the sensitive phenotype of one A578S Ugandan parasite as seen on RSA performed ex vivo (i.e., in samples obtained directly from patients) [START_REF] Cooper | Lack of artemisinin resistance in Plasmodium falciparum in Uganda based on parasitological and molecular assays[END_REF] or the lack of association with slow-clearing infections in earlier studies. [START_REF] Ashley | Spread of artemisinin resistance in Plasmodium falciparum malaria[END_REF][START_REF] Mishra | Surveillance of artemisinin resistance in Plasmodium falciparum in India using the kelch13 molecular marker[END_REF] Thus, except for one recent study involving a few severely ill children, [START_REF] Hawkes | Slow clearance of Plasmodium falciparum in severe pediatric malaria, Uganda, 2011-2013[END_REF] the available data suggest that A578S is not an artemisinin-resistance mutation.

Aside from nine patients who carried wild-type K13 alleles, there was no positivity on day 3 among African patients. This result is consistent with the finding that a large number of rare K13 alleles are neutral with respect to artemisinin susceptibility and with the lack of evidence of artemisinin-driven selection on the K13 locus in Africa (Table S5 in the Supplementary Appendix). However, clinical detection of artemisinin resistance in Africa is complicated by the contribution of acquired immunity and maintained efficacy of the partner drugs (amodiaquine and lumefantrine) to parasite clearance. [START_REF] Lopera-Mesa | Plasmodium falciparum clearance rates in response to artesunate in Malian children with malaria: effect of acquired immunity[END_REF][START_REF]WWARN Artemisinin based Combination Therapy (ACT) Africa Baseline Study Group. Clinical determinants of early parasitological response to ACTs in African patients with uncomplicated falciparum malaria: a literature review and meta-analysis of individual patient data[END_REF] In vitro phenotyping with the use of RSA [START_REF] Witkowski | Novel phenotypic assays for the detection of artemisinin-resistant Plasmodium falciparum malaria in Cambodia: in-vitro and ex-vivo drugresponse studies[END_REF] and allele-specific genome-editing studies [START_REF] Straimer | Drug resistance: K13-propeller mutations confer artemisinin resistance in Plasmodium falciparum clinical isolates[END_REF] can provide important information about the potential phenotypic effect of nonsynonymous mutations with respect to artemisinin susceptibility, as we provided for A578S in this study. Similar approaches could be used to assess the potential effect of frequently observed K13 nonsynonymous mutations associated with clinical artemisinin resistance (e.g., F446I, N458Y, P574L, and P553L). Of 108 nonsynonymous mutations that were detected, we observed 72 only once, which was consistent with the results of earlier studies. [START_REF] Conrad | Polymorphisms in K13 and falcipain-2 associated with artemisinin resistance are not prevalent in Plasmodium falciparum isolated from Ugandan children[END_REF][START_REF] Isozumi | Novel mutations in K13 propeller gene of artemisininresistant Plasmodium falciparum[END_REF][START_REF] Kamau | K13-propeller polymorphisms in Plasmodium falciparum parasites from sub-Saharan Africa[END_REF][START_REF] Ouattara | Polymorphisms in the K13-propeller gene in artemisininsusceptible Plasmodium falciparum parasites from Bougoula-Hameau and Bandiagara, Mali[END_REF][START_REF] Taylor | Absence of putative artemisinin resistance mutations among Plasmodium falciparum in Sub-Saharan Africa: a molecular epidemiologic study[END_REF][START_REF] Torrentino-Madamet | Limited polymorphisms in k13 gene in Plasmodium falciparum isolates from Dakar, Senegal in 2012-2013[END_REF] The criteria for prioritizing further laboratory studies include the following: the frequent observation of a new allele with a nonsynonymous mutation, evidence of dissemination (since the absence of dissemination probably indicates that the mutation was lost to genetic drift or intrapopulation competition), and preliminary association with clinical data whenever possible. Some clues about the phenotypic effect of newly discovered mutations might also be obtained from exploring specific metabolic changes (e.g., in phosphatidylinositol 3phosphate levels [START_REF] Mbengue | A molecular mechanism of artemisinin resistance in Plasmodium falciparum malaria[END_REF] ) in parasites with K13 mutations or modeling the consequences of the mutations on K13 protein structure. The recently solved BTB-POZ (broad complextramtrack-bric-a-brac-poxvirus and zinc-finger) propeller structure of K13 protein domains (UniProtKB accession number, PDB AYY8) showed that F446, Y493, R539, and C580 are located in a beta sheet of their respective kelch domains (Fig. S13 in the Supplementary Appendix); these mutations should alter the overall structure (Fig. S14 in the Supplementary Appendix). We predict that A578S, located in the flexible junction between blade 3 and blade 4 of the beta sheet, has a limited effect on the propeller structure.

Our study has several weaknesses. First, the isolates that we evaluated represent only a convenience sample of those in the total population areas that we surveyed. Second, we lacked information about regions in which malaria is endemic such as India, where we needed to clarify the western boundary of resistance distribution. Third, in some countries, the numbers of available samples were small. The numbers of sites surveyed in East Africa, the Philippines, Indonesia, and South America need to be increased in future studies, although we could still discern clear geographic patterns in the distribution of haplotypes. Investigators should be able to implement the K13 toolbox in the field. We expect that future surveillance will fill in the gaps of the existing map.

In conclusion, our study clarifies how K13 monitoring can assist future surveillance of artemisinin resistance (Fig. 4). In regions where resistance is established, K13 sequencing can map its geographic distribution and invasion of the fringes. The dissemination of K13 mutations can be conveniently assessed by determining the identity of flanking haplotypes, as we did here, which provides a more accessible approach than whole-genome sequencing [START_REF] Miotto | Genetic architecture of artemisinin-resistant Plasmodium falciparum[END_REF] or microsatellite typing. [START_REF] Talundzic | Selection and spread of artemisinin-resistant alleles in Thailand prior to the global artemisinin resistance containment campaign[END_REF] In resistance-free areas, molecular surveillance will need to detect possible invasion by known alleles associated with K13 resistance and track any temporal increase in the proportion and dissemination of newly identified nonsynonymous mutations. This strategy should identify foci to be targeted for further in vitro phenotyping and therapeutic efficacy studies as well as for surveillance of other candidate artemisinin-susceptibility loci. [START_REF] Borrmann | Genome-wide screen identifies new candidate genes associated with artemisinin susceptibility in Plasmodium falciparum in Kenya[END_REF][START_REF] Henriques | Directional selection at the pfmdr1, pfcrt, pfubp1, and pfap2mu loci of Plasmodium falciparum in Kenyan children treated with ACT[END_REF] This effort can contribute information to characterize these areas with respect to potential artemisinin resistance and inform treatment guidelines before large-scale dissemination has occurred.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material. Shown are the percentages of nonsynonymous mutations that have been identified in the portion of the Plasmodium falciparum K13 gene encoding the kelchpropeller domain in Asia, Africa, South America, and Oceania. Synonymous mutations that do not modify the protein sequence are not indicated. At present, all the mutations that have been associated with resistance to artemisinin derivatives have resulted in nonsynonymous amino acid changes. The black circles indicate medians and the I bars interquartile ranges for each continent. K13 nonsynonymous mutations were not detected in 27 countries from which samples were obtained (19 in Africa, 2 in Asia, 1 in Oceania, and 5 in South America). Names are not shown (owing to a lack of space) for the following countries: in Asia:

Afghanistan, Iran, Bangladesh, Nepal, Indonesia, and Philippines; in Africa: Cameroon, Congo, Democratic Republic of Congo, Equatorial Guinea, Gabon, Burundi, Ethiopia, Rwanda, Sudan, South Sudan, Somalia, Tanzania, Uganda, Madagascar, Angola, Malawi, Mozambique, South Africa, Zimbabwe, Benin, Burkina Faso, Ghana, Guinea Bissau, Ivory Coast, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, and Togo; in South America: Brazil, Colombia, Ecuador, French Guiana, Peru, and Venezuela; and in Oceania: Papua New Guinea and Solomon Islands. The percentages for Chad and Gambia were derived from a sample size of less than 50. Details regarding sampling and K13 diversity according to country are provided in Tables S4, S5, and S7 and Fig. S3 in the Supplementary Appendix. CAR denotes Central African Republic. Shown are the distributions of the wild-type K13 allele in Asia (Panel A) and around the world (Panel B). Areas in which malaria is endemic are shaded in gray, and areas that are considered to be malaria-free are shown in white. The mean frequency of the wild-type allele is indicated by the color code. In Panel A, the individual sites of sample collection are indicated with a cross. In Panel B, a 100-km radius was used for the area centered on each sampling site or on the capital city of the country if no specific site was used for sampling. Data regarding sampling methods and K13 diversity according to country are provided in Tables S4, S5, and S7 and Fig. S3 in the Supplementary Appendix. Shown is a step-by-step procedure for acquiring information to help develop policies regarding the use of artemisinin-based combination therapy (ACT) and strategies to eliminate malaria. K13 genotyping is integrated into surveillance activities by combining clinical efficacy studies with in vitro susceptibility testing and is supported by gene-editing studies. The validated or confirmed K13 mutations that are associated with resistance to artemisinins are Y493H, R539T, I543T, and C580Y, and the associated K13 mutations are P441L, F446I, G449A, N458Y, P553L, R561H, V568G, P574L, and A675V. [START_REF]Status report on artemisinin and ACT resistance[END_REF] The validation of a K13 mutation as a resistance marker (i.e., confirmed K13 mutation) is based on the following criteria: a significant association with delayed clearance of Plasmodium falciparum and a reduced drug sensitivity (survival rate, >1%) on ex vivo ring-stage survival assay (RSA) or in vitro RSA performed on field isolates, culture-adapted parasites, or geneedited parasite lines, as compared with the K13 wild-type parent control. A K13 mutation is deemed to be associated with artemisinin resistance if it has a significant association with delayed clearance. A K13 mutation is said to be neutral if it has no significant association with delayed clearance or reduced drug sensitivity. A new K13 mutation is one that has been never observed and thus does not appear in mutation databases. Recommendations regarding efficacy trials and treatment policies are provided by the World Health Organization (WHO). [START_REF]Guidelines for the treatment of malaria. 3[END_REF]

International

  Cooperation Agency, and Science and Technology Research Partnership for Sustainable Development), and the President's Malaria Initiative of the U.S. Agency for International Development.

Figure 1 .

 1 Figure 1. K13 Nonsynonymous Mutations, According to Country and Continent

Figure 2 .

 2 Figure 2. Frequency Distribution of the Wild-Type K13 Allele

Figure 3 .

 3 Figure 3. Overview of the Distribution of the Flanking Haplotypes of the C580Y, Y493H, R539T, I543T, P553L, P574L, and F446I Nonsynonymous Mutations in K13 in Two Regions in Asia Shown are two areas in Asia in which individual K13 nonsynonymous mutations (which are labeled in the colored boxes) were frequently identified: one area that includes Cambodia, Vietnam, and Laos and a second area that includes western Thailand, Myanmar, and China. The haplotype groups of C580Y, F446I, P574L, and P553L are shown in distinct colors. Similarly colored boxes indicate shared haplotypes, and hatched boxes indicate the presence of additional countryspecific haplotypes. For example, C580Y haplotypes that are shared between Cambodia, Vietnam, and Laos (H29, H33, or H43) are shown in red, whereas the C580Y haplotype found in Thailand (H42) is shown in light blue. Full details about the distribution of haplotypes are provided in Table S9 and Fig. S9 in the Supplementary Appendix.

Figure 4 .

 4 Figure 4. Algorithm for Surveillance of Artemisinin Resistance on the Basis of K13 Mutations

N Engl J Med. Author manuscript; available in PMC 2016 December 23.

We thank all the patients, health workers, and research teams involved in the worldwide sample collection; Prof. C. Brechot and M. Jouan of Institut Pasteur for their support and advice; the shipping department team of the Institut Pasteur, Paris, for help in shipping the K13 toolbox; and Dr. Raymond Hui for providing information on the crystal structure of the K13 protein.

Funded by Institut Pasteur Paris and others. Supported by the Institut Pasteur Paris, Institut Pasteur International Division, Institut Pasteur Cambodia, and the World Health Organization; by a grant (ANR-10-LABX-62-IBEID) from the French Government Investissement d'Avenir program, Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"; a grant from Natixis Banques; a grant (R01 AI109023, to Dr. Fidock) from the National Institutes of Health; grants from the Fiocruz Fundação Oswaldo Cruz, Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro, Fundação de Amparo à Pesquisa do Estado do Amazonas, the Brazilian National Council for Scientific and Technological Development, the Agence Nationale de la Recherche (13-BSV3-0018-01 and 11-BSV7-009-01), the Austrian Federal Ministry of Science, Research, and Economy, the Calgary Laboratory Services, the Centre International de Recherches Médicales de Franceville, the European and Developing Countries Clinical Trials Partnership (CT-2004-31070-001), the Drugs for Neglected Diseases Initiative, the Else Kroener Fresenius Stiftung, the Holger Poehlmann Stiftung, the European Community African-European Research Initiative "IDEA" (HEALTH-F3-2009-241642), the Fonds Wetenschappelijk Onderzoek, the Vlaamse Interuniversitaire Raad-Universitaire Ontwikkelingssamenwerking, the Belgian Technical Cooperation in Democratic Republic of Congo, the European Community Seventh Framework Program (FP7/2007-2013, 242095, and 223601), the European Commission (REGPOT-CT-2011-285837-STRONGER), the Ministère de la Santé Publique du Niger (Laboratoire National de Référence Résistance aux Antipaludiques), the Foundation of National Science and Technology Major Program (2012ZX10004-220), the French Ministry of Health (Institut National de Veille Sanitaire), the Global Fund to Fight AIDS, Tuberculosis and Malaria, the 5% Initiative program (French Ministry of Foreign Affairs, France Expertise Internationale, 12INI109), the Institut Pasteur de Madagascar, the Government of the Philippines, the Institut de Recherche pour le Développement, the Foundation des Treilles, the Délégation Générale pour l'Armement (PDH-2-NRBC-4-B1-402), the Institut Pasteur de Bangui, the International Society for Health Research and Training, the Malaria Research Initiative Bandarban, Vienna, International Centre for Diarrhoeal Disease Research, Bangladesh, the Médecins sans Frontières (Centre Opérationnel Paris, France), Medicines for Malaria Venture, the National Research Council of Thailand, the Thammasat University, the National Natural Science Foundation of China (81271870, 81361120405, and 81271863), the Natural Science Foundation of Jiangsu Province (BK20130114 and BK20150001), the Jiangsu Science and Technology Department (BM2015024), the National Institutes of Health (R01 AI11646601, AI109023, and ICEMR U19AI089702, U19AI089672), the Pasteur Institute of Iran, the Malaria Division of the Iranian Center for Diseases Management and Control, Public Health England (Malaria Reference Service Contract), the Government of Rwanda, the U.S. Department of Defense Armed Forces Health Surveillance Center, Global Emerging Infections Surveillance and Response System (P0463-14-N6), the Fogarty International Center of the National Institutes of Health training (D43 TW007393), the Mahidol-Oxford Research Unit, the Government of Japan (Science and Technology Agency, Agency for Medical Research and Development, Japan

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