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Plants

PREDICTION OF MONO-, BI- , AND TRI-VALENT METAL CATION RELATIVE

TOXICITY TO THE SEAWEED GRACILARIA DOMINGENSIS (GRACILARIALES,

RHODOPHYTA) IN SYNTHETIC SEAWATER

LUIZ FERNANDO MENDES, LEONARDO ZAMBOTTI-VILLELA, NAIR SUMIE YOKOYA, ERICK LEITE

BASTOS, CASSIUS VINICIUS STEVANI, and PIO COLEPICOLO

Environ Toxicol Chem., Accepted Article • DOI: 10.1002/etc.2340

Accepted Article "Accepted Articles" are peer-reviewed, accepted manuscripts that have not been edited, formatted, or in any way altered by the authors since acceptance. They are citable by the Digital Object Identifier (DOI). After the manuscript is edited and formatted, it will be removed from the “Accepted Articles” Web site and published as an Early View article. Note that editing may introduce changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. SETAC cannot be held responsible for errors or consequences arising from the use of information contained in these manuscripts.

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Plants Environmental Toxicology and Chemistry DOI 10.1002/etc.2340

PREDICTION OF MONO-, BI- , AND TRI-VALENT METAL CATION RELATIVE

TOXICITY TO THE SEAWEED GRACILARIA DOMINGENSIS (GRACILARIALES,

RHODOPHYTA) IN SYNTHETIC SEAWATER

LUIZ FERNANDO MENDES,†* LEONARDO ZAMBOTTI-VILLELA,† NAIR SUMIE YOKOYA,‡ ERICK

LEITE BASTOS,§ CASSIUS VINICIUS STEVANI,§ and PIO COLEPICOLO†

† Instituto de Química, Universidade de São Paulo, Departamento de Bioquímica, 26077, 05599-970

São Paulo, SP, Brazil

‡ Instituto de Botânica, Núcleo de Pesquisa em Ficologia, São Paulo, SP, Brazil

§ Instituto de Química, Universidade de São Paulo, Departamento de Química Fundamental, São

Paulo, SP, Brazil

Running title: Predicting metal toxicity to seaweed by QICAR

* Address correspondence to lfm@iq.usp.br.

Additional Supporting Information may be found in the online version of this article.

© 2013 SETAC

Submitted 22 April 2013; Returned for Revisions 17 June 2013; Accepted 24 July 2013

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Abstract: This study reports a 48-h aquatic metal-toxicity assay based on daily growth rates of the red

seaweed Gracilaria domingensis (Gracilariales, Rhodophyta) in synthetic seawater. The median

inhibitory concentration (IC50) for each metal cation was experimentally determined, and the ratios of

free ions (aqueous complex) were calculated by software minimization of the total equilibrium activity

(MINTEQA2) to determine the free median inhibitory concentration (IC50F). A model for predicting

the toxicity of fourteen metal cations was developed using the Generic Function Approximation

algorithm (GFA) with logIC50F values as the dependent variables and the following properties as

independent variables: ionic radius (r), atomic number (AN), electronegativity (Xm), covalent index

(Xm2r), first hydrolysis constant (|logKOH|), softness index (σp), ion charge (Z), ionization potential

(ΔIP), electrochemical potential (ΔEo), atomic number divided by ionization potential (AN/ΔIP), and

the cation polarizing power for Z2/r and Z/AR. The three-term independent variables were predicted as

the best-fit model (logIC50F: - 23.64 + 5.59 Z/AR + 0.99 |logKOH| + 37.05 σp , adj-R2: 0.88, pred-R2:

0.68, Friedman lack-of-fit score: 1.6). This mathematical expression can be used to predict metal-

biomolecule interactions, as well as the toxicity of mono-, bi- and trivalent metal cations, which have

not been experimentally tested in seaweed to date. Quantitative Ion-Character Relationships

(QICARs) allowed us to infer that the mechanism of toxicity might involve an interaction between

metals and functional groups of biological species containing sulfur or oxygen.

Keywords: Daily growth rates, GFA, MINTEQA2, Free ions, QICAR models

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INTRODUCTION

Linear free-energy relationships have been successfully applied for decades to predict the

toxicity of metal cations in several organisms [1-9]. Nevertheless, the potential use of the Quantitative

Ion-Character Relationship (QICAR) to investigate metal toxicity to living organisms has been

underexplored in comparison to the Quantitative Structure-Activity Relationship (QSAR), which is

commonly used for organic toxicants [7,10]. As in the case of QSAR studies with organic compounds,

the physico-chemical properties of metallic species can be used to predict the metal-biomolecule

interactions of biological systems (e.g., tissues, cells, and organelles), as well as to rationalize observed

toxic effects [3,8,9].

The physico-chemical properties of metallic species are caused by their kinetic reactivity/lability,

the affinity and thermodynamic stability of metal-ligand bonds (metal-donor atom affinities), solubility

(metal ion speciation), stereochemistry, redox potential, periodic tendencies in charge/radius,

ionization and polarizability, and Brønsted acid-base equilibria [11]. These physico-chemical

properties can be translated as electronegativity (Xm), or the ability of an atom or functional group to

attract electrons or electron density towards itself in a covalent bond [12,13]; the Pauling ionic radius

(r, Å), the size of an ion in a crystal lattice; the atomic radius (AR, Å), the distance from the nucleus

to the boundary of the surrounding cloud of electrons; the ion charge (Z), the charge on an ion; the

atomic number (AN), the number of protons in the nucleus [14]; the softness index (σp), the

coordinate bond energy of metal fluoride/metal iodide/coordinate bond energy of the metal fluoride or

the tendency for the other electron shell to deform (i.e., polarizability) [7,15,16]; the ionization

potential (ΔIP, eV), the difference in ionization potentials between the ion oxidation number OX and

OX–1 or the energy required to detach an electron in its lowest energy state from a gaseous ground state

atom or molecule; the electrochemical potential (ΔEo, V), the absolute difference in electrochemical

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potential between the ion and its first stable reduced state [17]; and the log of the first hydrolysis

constant (|logKOH|, KOH for Mn+ + H2O MOHn-1 + H+), which is the tendency of a metal cation to

covalently bind to the functional groups of important biomolecules normally containing oxygen-donor

atoms [17,18]. Two parameters can be combined to generate a new one such as the ionic index, or the

ratio of cation Z charge to its ionic radius (Z2/r), which reflects the ionic bond stability of metal-ligand

electrostatic interactions [19,20]. Other parameters of cation polarizing power can be obtained for Z/r,

Z/r2, Z/AR, and Z/AR2 [20]. The electronegativity (Xm) and Pauling ionic radius (r) are composed of

two fundamental ionic characteristics. The result of the last combination, or covalent index (Xm2r),

reflects the cation-binding tendency of soft ligands as sulfur-containing biomolecules [19,21].

Despite the considerable ecological and economic importance of marine seaweeds from the

Gracilaria genus to the pharmaceutical industry and as raw materials for agar production [22-25],

metal toxicity studies using the QICAR approach are scarce. For the present study, we modeled the

effects of some metal cation physico-chemical parameters on their toxicity to a representative species

of the red seaweed Gracilaria domingensis (Kütz.) Sonder ex Dickie in synthetic seawater medium. A

data set of fourteen previously tested metal cations was used to predict the toxicity and cationic-

biomolecule interactions in G. domingensis [24]. The best model was used to estimate the free median

inhibitory concentration (logIC50F) values of metal ions that were not experimentally tested [i.e.,

Ag(I), Cs(I), Ba(II), Hg(II), Fe(II), Fe(III), Cr(III), and Al(III)]. Moreover, based on the results

obtained, we proposed some hypotheses to explain the toxicity of metal cations to G. domingensis at

the molecular level.

Toxicological studies are time-consuming and labor-intensive, demanding trained personal to

maintain the organisms and to perform the experiments. Hence, it is very useful to generate linear free

energy relationships such the ones presented here that can reasonably predict the toxicity of different

classes of substances to a specific target organism. It is also noteworthy to mention that the synthetic

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medium assures complete control over the culture and assay conditions, which is crucial to the

determination of toxicological parameters and experimental replicability [24].

MATERIALS AND METHODS

Determination of daily growth rates (DGR) and free median inhibitory concentration (IC50F)

All free median inhibitory concentration values (IC50F) used in this work were obtained from previous

measurements conducted by our group [25]. In summary, the toxicity of fourteen metal cations to

seaweed was obtained using a 48-h assay based on daily growth rates (DGR, expressed in μg d-1)

[24,25]. The assay consists in add fresh apical segments to metal-free (control) and metal-containing

synthetic culture medium at specific pH and salinity. The DGR values were determined by using the

expression: Δm(mfinal - minitial)/Δt(tfinal - tinitial), where m is the mass and ∆t is 48 h. The average DGR

values of triplicates were used to calculate the IC50 values (fitting growth sigmoidal curves with a

dose-response function). G. domingensis cultures were cultivated and maintained at optimal conditions

as determined previously by a multivariate factorial analysis [25]. The concentration of metal cations

was determined with an ICP-AES according to the standard methods of the U.S. Environmental

Protection Agency (U.S. EPA, method 6010C). Metal distribution (in percentage) to calculate IC50F

values was calculated by computational chemical equilibrium software minimization of the total

equilibrium activity (MINTEQA2, U.S. Environmental Protection Agency), version 3.0, [8,24,26]

under specific experimental conditions.

Model development

The generic function approximation (GFA) technique was performed to generate different Quantitative

Ion-Character Relationship (QICAR) models from the following descriptors (Table S1): Z: ion charge,

AN: atomic number, r: ionic radius in Å, AR: atomic radius in Å, ΔIP: ionization potential in eV, ΔEo:

electrochemical potential in V, Xm: electronegativity, |logKOH|: log of the first hydrolysis constant, σp:

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softness index, Xm2r: covalent index, AN/ΔIP: atomic number divided by ionization potential, and the

cation polarizing power for Z2/r and Z/AR [3-8,18].

The use of the genetic function approximation (GAF) algorithm in QSAR and analogous studies

was introduced by Rogers and Hopfinger [27]. This method for generating statistical models of data

uses the process of evolution and offers many advantages over conventional linear regression analysis

[28]. Moreover, the GFA algorithm creates several models by evolving random initial models, which is

very convenient to analyze data considering a large number of independent variables.

The GFA algorithm (100 random models and 5,000 interactions to evolution) was used to search

the solution space and to identify the most favorable descriptor subsets with which to build QICAR

models. The Friedman lack-of-fit (L.O.F.) score was used to estimate the fitness of each model among

a given number of equations that fit the training set data. Given the small number of descriptors

(fourteen metal ions), the initial model population was set to 100 and the equations consisted of two to

four terms, including a constant. Equations were constructed by considering linear, quadratic and cubic

parameters. The mutation probability, which consists of the addition of a new term, was set to 0.1,

while the smoothing parameter (d) of the L.O.F. expression was set to 0.5. The predictive models were

also evaluated by using a coefficient of determination (R2), adjusted R2 (adj-R2), predicted R2 (pred-

R2), residual error (RMS), and p-value for significance of regression (S.O.R.).

RESULTS AND DISCUSSION

The effects of mono-, bi- and trivalent metals on the seaweed Gracilaria domingensis as

evaluated by DGR (daily growth rate) was previously reported by our group [24]. Table 1 displays the

toxicity of mono-, di-, and trivalent metal cations as total IC50 values as determined from DGR curves

vs. metal concentrations [24]. The IC50 data were converted to free ions (IC50F and logIC50F values)

by MINTEQA2 (computational chemical equilibrium software minimization of total equilibrium

activity) as shown in Table 1. On the basis of the ICF50, Cd(II), Cu(II), Pb(II), Zn(II), Ni(II), Co(II),

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and La(III) were more toxic as measured by a 48-h aquatic metal-toxicity assay to track the growth of

seaweed Gracilaria domingensis in synthetic seawater. As expected, such metals as Li(I), K(I), Na(I),

Mn(II), Sr(II), Ca(II), and Mg(II) presented much lower toxicity.

A set of fourteen common free metal ion toxicity descriptions (which were plotted as the

logIC50F data vs. linear free-energy parameters) was used for the generation of Quantitative Ion-

Character Relationship (QICAR) models. The first ten best-scored models share very similar

information in terms of statistical parameters and types of descriptors (Table 2). These metals can be

classified into three- or four-term equations for independent variables and they contain common

descriptors, such as the cation polarizing power (Z/AR), softness index (σp), and the absolute value of

the log of the first hydrolysis constant |logKOH|. All listed predictive equations in Table 2 were

determined to be statistically significant at α = 0.05. Although some three-term models (e.g., 2 and 5)

had adj-R2 values (0.88 and 0.87, respectively) that were slightly better with respect to the remaining

models, the models were ultimately classified according to their Friedman lack-of-fit (L.O.F.) values.

The L.O.F. values increased from 1.6 (equation 1) to 2.6 (equation 10), and the RMS went from 0.8 to

1.5 when fitting data for all metal cations on the QICAR models. Additionally, by using a minimum

L.O.F., the best fitting mathematical approach was obtained for a three-term independent variable

(equation 1: logIC50F: -23.64 + 5.59 Z/AR + 0.99 |logKOH| + 37.05 σp). In several studies the covalent

index is an important parameter to predict metal-ligand binding tendencies and thus their toxicity to the

target organism [7-9,29]. Surprinsingly, the correlation determined with the parameter Xm2r was very

weak (R2 = - 0.40).

Equation 1 was applied and a strong correlation (R = 0.94, R2 = 0.88) was found between the

experimental toxicity of the training set [as log(IC50F) values], and a response predicted toxicity

(Figure 1) as determined by GFA (Table 2) was observed (Supporting Information, Table S2). The

individual correlation between physico-chemical parameters displayed in Table S1 and logIC50F

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values in Table 1 showed that the smaller the σp and |logKOH| of a metal ion (Figure S1), the smaller its

logIC50F values in the synthetic seawater medium. In contrast, metal cation logIC50F values increased

proportionally with Z/AR (Figure S1).

Based in the soft index model, metal cations can be separated into three classes according to ion

softness, namely, hard, soft and borderline (Table 1) [30]. The results of the present study indicate that

soft ions (i.e., Cd), which form complexes with sulfhydryl groups, and borderline ions (i.e., Cu, Pb, Zn,

Co, and Ni), which form more stable complexes on S-donor atoms compared to O-donor atoms, were

more toxic to seaweed than hard ions (i.e., Ca, Li, Sr, Mg, Na, and K), which form more stable

complexes on O-donor atoms compared to S-donor atoms [8,31]. The exception is La(III) ions (hard),

which are more toxic than Mn(II) (borderline) ions. The observed toxicity pattern can also be

rationalized as follows: i) the higher the hardness of the metal cation, the lower its polarizability, and

the higher its electronegativity, which yields a feature of ionic binding; ii) the higher the softness of the

metal cation, the higher its polarization, the lower its electronegativity and the more covalent the bond,

which can be associated with soft-based donor atom interactions in biological systems [8,31].

Moreover, the softness parameter can be translated as the metal cation's ability to donate its valence

electrons [4]. Hence, the metal cation toxicity of mainly soft and borderline ions is directly

proportional to its potential for a change of oxidation state.

A combination of Z/AR and σp descriptors or variables would be related to the static effective

polarizability of the ion [32,33]. On the other hand, the correlation between σp and |logKOH| allowed us

to speculate that some metal ions might covalently bind to intermediate ligands bearing functional

groups containing S- and O-donor atoms (such as -SH, S2-, RSH, RS, R2S, -OH, -COOH, -PO42-, -O-,

CO, ROH, H2O, NO-, ROSO-, and others groups) [6,8], which are of vital importance to the proper

protein function during the algal life cycle. It was reported that the presence of Cd(II) and Pb(II) ions

decreases superoxide dismutase levels and induced the increase in glutathione and phytochelatin levels

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as a consequence of covalent binding to S-donor atoms [34,35], which can compromise the defense

mechanism against oxyradicals and finally lead to cellular damage [36]. On the other hand, hard ions

(La, Ca, Li, Sr, Mg, Na, and K) have low or very low toxicity, which most likely reflects a less specific

toxic action, which might involve membrane depolarization, osmosis or electrolyte redox imbalance.

Toxicological assays are time-consuming, labor intensive and expensive. Hence, it is very

important to find other methods to estimate toxicity, which are based on fewer toxicity assays. With

this goal, it is possible to predict the toxic effects of nontested metal cations. The training set used to

develop these prediction models contains mono-, di-, and trivalent cations, so it was used to predict the

metal cation toxicity of Ag(I), Cs(I), Ba(II), Hg(II), Fe(II), Fe(III), Cr(III), and Al(III) (Figure 2, Table

S3). The following toxicity order was predicted within the test set for metal ions that were not

experimentally tested (logIC50F): -10.3 Hg(II) >> -5.3 Ag(I) > -4.2 Fe(III) >> -2.3 Cr(III) >> 1.6

Al(III) >> -0.5 Fe(II) > 1.3 Cs(I) > 1.6 Ba(II).

The Person’s softness index (σp) has been successfully applied to predict covalent interactions

between metals and sulfur-donor ligands in biological systems for several organisms, such as mice (R2

= 0.66) and fruit flies (R2 = 0.62) [37], bacteria (R2 = 0.73)] [17], crustaceans (R2 = 0.89), rainbow

trout (R2 = 0.82), and fathead minnow (R2 = 0,84)] [9,10]. The logarithm of the first hydrolysis

constant, or |logKOH|, has been used to explain metal cation stability, toxicity mode of action and

pathways involving O-donor ligands to Vibrio fischeri in the Microtox assay (R2 = 0.93) [3], nematode

(R2 = 0.89) [4], and sunflower (R2 = 0.77).

The results for G. domingensis indicate the importance of electrostatic interaction strength on

cation-ligand complexation and covalent interactions on metal-ligand complexation of sulfur- and

oxygen-donor atoms (soft and borderline ions). In conclusion, the toxicity observed for 22 different

mono-, di-, and trivalent metal cations to this macroalga could be predicted by a function of three

independent physico-chemical variables, namely, Z/AR, σp and |logKOH|.

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SUPPLEMENTAL DATA

Tables S1–S3.

Figure S1. (306 KB DOC).

Acknowledgment—The authors are grateful for the financial and technical support for this study from

the following institutions and people: Ministério de Ciência e Tecnologia, CNPq, NAP-Biodiversidade

Marinha, INCT-Redoxoma, Fundação de Amparo à Pesquisa do Estado de São Paulo-FAPESP:

09/54718-4 (L.F.M), and NAP-PhotoTech (the USP Research Consortium for Photochemical

Technology) from C. V. Stevani.

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Captions

Figure 1. Predicted vs. observed log(IC50F) for the metal cation speciation training set: logIC50F: -

23.64 + 5.59 Z/AR + 0.99 |logKOH| + 37.05 σp.

Figure 2. Predicted log(IC50F) for the test set.

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Tables Table 1. Total and free ion median inhibitory concentrations (IC50 and IC50F, respectively) were determined for the seaweed Gracilaria domingensis in synthetic seawatera.

IC50 IC50 % of free iona IC50F x % of free ion Metals Classification

(mg L-1) b (mM) b (%) b (mM) b log (IC50F)

Cd(II) Soft ions 3.60 ± 0.02 0.030 ± 0.0002 0.18 54 x 10-6 ± 36 x 10-8 -4.300 ± 0.003 Cu(II) Borderline ions 54.9 ± 0.3 0.90 ± 0.05 0.05 50 x 10-5 ± 3 x 10-6 -3.300 ± 0.003 Pb(II) Borderline ions 120 ± 3 0.60 ± 0.01 1.4 0.0080 ± 0.0002 -2.10 ± 0.01 Zn(II) Borderline ions 41.5 ± 0.7 0.60 ± 0.01 6.9 0.040 ± 0.001 -1.40 ± 0.01 Ni(II) Borderline ions 123 ± 1 2.10 ± 0.02 34.7 0.70 ± 0.01 -0.20 ± 0.01 Co(II) Borderline ions 164 ± 5 2.8 ± 0.1 57.7 1.60 ± 0.06 0.20 ± 0.02 La(III) Hard ions 811 ± 3 5.80 ± 0.02 31.9 1.90 ± 0.01 0.300 ± 0.001 Mn(II) Borderline ions 694 ± 1 12.60 ± 0.01 65.4 8.20 ± 0.01 0.9000 ± 0.0004 Ca(II) Hard ions 759 ± 2 18.90 ± 0.05 60.7 11.50 ± 0.03 1.100 ± 0.001 Li(I) Hard ions 96 ± 1 13.8 ± 0.1 84.2 11.6 ± 0.1 1.10 ± 0.04 Sr(II) Hard ions 4,153 ± 102 48 ± 1 68.8 32.6 ± 0.8 1.50 ± 0.01

Mg((II) Hard ions 2,863 ± 7 118.0 ± 0.3 49.4 58.3 ± 0.2 1.800 ± 0.001 K(I) Hard ions 5,925 ± 283 151 ± 7 85.3 130 ± 6 2.10 ± 0.02

Na(I) Hard ions 3,982 ± 163 173 ± 7 85.5 148 ± 6 2.20 ± 0.02 a In synthetic seawater at pH = 7.5 ± 0.3 over 48 h [24]. b Quantification of the IC50 values were accomplished for all individual metal cations using inductively coupled plasma atomic emission spectroscopy (ICP-AES, Spectro Genesis). b The ratio of free metal cations in solution at IC50 values were estimated by the software MINTEQA2 (Minimization of Total Equilibrium Activity, U.S. Environmental Protection Agency, Washington, DC, version 3.0).

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Table 2. Summary of statistics for the top 10 QICAR models (free metal cations) made by the GFA learner and their regression statistics.

log(IC50F) = R2 adj-R2 pred-R2 RMS L.O.F. p-value 1 -23.64 + 5.59 Z/AR + 0.99 |logKOH| + 37.05 σp 0.88 0.84 0.68 0.8 1.6 7.7 x 10-5 2 -13.94 • 0.03 AN + 169.19 σp • 431.18 σp σp 0.91 0.88 0.83 0.7 1.9 1.8 x 10-5 3 -14.77 + 159.24 σp • 380.35 σp σp 0.83 0.79 0.76 0.9 1.9 6.8 x 10-5 4 -5.16 + 32.86 σp 0.63 0.60 0.44 1.3 2.0 0.0007 5 -18.09 + 0.41 |logKOH| + 161.02 σp • 431.95 σp σp 0.90 0.87 0.80 0.7 2.0 2.6 x 10-5 6 -2.04 + 1.29 ΔEo 0.61 0.57 0.46 1.3 2.1 0.001 7 -20.50 + 6.61 Z/AR + 34.90 σp + 0.05 |logKOH| |logKOH| 0.89 0.85 0.71 0.8 2.3 4.5 x 10-5 8 -14.81 • 0.19 AN/ΔIP + 172.73 σp • 428.83 σp σp 0.88 0.85 0.75 0.8 2.4 5.7 x 10-5 9 -7.05 + 0.64 |logKOH| 0.55 0.51 0.43 1.4 2.5 0.003 10 4.41 • 3.09 Xm 0.53 0.49 0.39 1.5 2.6 0.003

R2 is the coefficient of determination; Adj-R2 is R2 adjusted for the number of terms in the model; pred-R2 is the prediction (PRESS) R2, equivalent to q2 from a leave-1-out cross-validation; RMS is residual error; L.O.F. is the Friedman lack-of-fit score; S.O.R. p-value is the p-value for significance of regression. Note: a multiplicity correction has not been applied to the p-values, meaning that the values are optimistic.

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Figures

Fig. 1.

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Fig. 2.