Abstract
Serotonin has been shown to modulate probabilistic reversal learning (PRL) and negative feedback sensitivity (NFS) in both animal and human studies. Whilst these two measures are tightly coupled, some studies have suggested that these may be mediated by independent mechanisms; the former, representing perseveration and cognitive flexibility, and the latter measuring the ability to maintain a response set (win-stay) at the expense of lose-shift behavior when occasional misleading feedback has been presented. Here, we tested this hypothesis in 44 healthy participants who were administered tryptophan (22 placebo, 22 tryptophan), a precursor to serotonin. We found a dissociable effect of tryptophan supplementation on PRL/NFS. Specifically, tryptophan administration increased NFS compared to the placebo group but had no effect on PRL. We discuss these findings in relation to dosages and with a particular focus on the acute tryptophan depletion (ATD) procedures.
Introduction
Serotonin (5-HT) has long been implicated in probabilistic reversal learning (PRL) and in processing negative feedback. Both are measured by the PRL task, originally developed in humans (Cools et al., ), but then also adapted in animal studies (Bari et al., ; Ineichen et al., ). In this task, the subject is instructed to choose one of two visual stimuli in order to maximize correct feedback/rewards. Once a certain number of correct responses are made, stimulus-reward contingencies are reversed. Additionally, for a minority of trials (usually 20%), misleading feedback is provided to a normally rewarded response. Negative feedback sensitivity (NFS) is defined as the frequency for which responses shift to the usually non-rewarded choice on such trials. Depressed patients, who are known to have impaired serotonergic function, display normal PRL but higher NFS compared to healthy subjects (Murphy et al., ; Taylor Tavares et al., ), suggesting that at least behaviorally, these two processes can be decoupled. This effect also appears to be dose and acute/chronic administration dependent; low (acute: 1 mg/kg) doses of the selective serotonin reuptake inhibitor (SSRI) citalopram in rats impaired PRL and increased NFS, whereas high (acute; 10 mg/kg) doses produced the opposite effect. In the same study, chronic high doses (5 mg/kg, daily, 7 days), in contrast, improved PRL but did not affect NFS (Bari et al., ; Robbins, ). Similar findings for low acute doses of citalopram (30 mg) have also been reported in humans (Chamberlain et al., ). Using a different SSRI in rats, escitalopram, all doses (0.03, 0.3 or 1.0 mg/kg) improved PRL but left NFS unaffected (Brown et al., ). These findings may partially be explained by the slightly different characteristics of citalopram and escitalopram. Whilst both have similar pharmacokinetics, escitalopram has higher potency and selectivity than citalopram (Carandang et al., ). There is also some evidence to suggest that polymorphisms in the gene encoding the serotonin transporter (SERT) can affect NFS and PRL differentially. In this study (den Ouden et al., ), individuals who were L’-homozygotes (i.e., decreased levels of extracellular serotonin) had higher NFS than S’ carriers whilst there was no PRL performance difference between these two groups. It is important, nevertheless, to stress that the association between the SERT promoter polymorphism and depression has not been confirmed by a recent meta-analysis (Culverhouse et al., ), and may suggest a more complex interaction (Rygula et al., ). Studies using the acute tryptophan depletion (ATD) procedure have produced mixed results. Some have reported no effect on PRL (but NFS was not investigated; Evers et al., ; Talbot et al., ; Finger et al., ; van der Plasse and Feenstra, ), whereas in others ATD impaired PRL (Murphy et al., ). Overall, the studies reviewed on depressed patients, SSRI administration and ATD suggest a potential inverted U-shaped relationship between serotonin concentration and NFS/PRL whereby too low or high 5-HT levels impair performance (Hulsken et al., ). Interestingly, a similar relationship has long been reported for the other monoamine neurotransmitter, dopamine (Cools and D’Esposito, ).
Here, using a double-blind, placebo-controlled, mixed-design method, we hypothesized that tryptophan supplementation may selectively affect NSF but leave PRL performance intact. Although numerous studies have looked at tryptophan’s modulation of memory, response to unfairness, emotional processing, attention and executive function (Sobczak et al., ; Booij et al., ; Murphy et al., ; Dougherty et al., ; Morgan et al., ; Silber and Schmitt, ; Cerit et al., ; Mohajeri et al., ), to the best of our knowledge, this is the first study looking at its effects on PRL and NFS.
Materials and Methods
Participants
Participants consisted of 44 university students, who were either administered tryptophan (N = 22, M = 21.4, SD = 3.0, 13 females and nine males) or placebo (N = 22, M = 20.8, SD = 2.6, 15 females and seven males). The study was approved by the ethics committee of Sheffield Hallam University and complied with the Declaration of Helsinki. Written informed consent was obtained for all participants before testing could take place. Exclusion criteria included: those suffering from cardiac, hepatic, renal and neurological disorders and individuals with a history of alcohol or drug addiction, or psychiatric illness (including individuals who had a history of taking antidepressants). Individuals having a history of taking tryptophan supplements were also excluded.
Drug Administration
Participants received either 0.8 grams of Tryptophan (supplied by BulkPowders Ltd., Colchester, UK) or 0.8 grams of microcrystalline cellulose (Sigma-Aldrich Co. LLC., St. Louis, MO, USA) dissolved in 200 ml of orange juice as per previously published protocols (Steenbergen et al., ). Peak plasma concentrations of tryptophan using this dosage have been shown to occur 60 min following oral administration (Markus et al., ).
Probabilistic Reversal Learning Task
To assess NFS and reversal learning, we used the PRL paradigm developed by Cools et al. () and which runs in PEBL software (Mueller and Piper, ). Here, using trial-and-error feedback, participants need to discover which of two patterns is correct (see Figure 1). To complete the PRL, participants had to finish one block of trials, consisting of 10 reversals. Each block had approximately 150 trials. Each reversal occurred after a variable 10–15 correct responses (including probabilistic errors: here defined as misleading feedback provided to the usually correct and rewarded response). The number of probabilistic errors per reversal varied between 0 and 4. The task was self-paced meaning that there was no timeout period to produce a response in each trial, however, participants were asked to respond as quickly and accurately as possible. Participants were given a full block of practice trials before testing began. We measured the following dependent measures: total errors, reversal errors and NFS. Total errors were made up of incorrect responses occurring before and after each reversal. Reversal errors were counted as the number of incorrect responses after each reversal and before the first correct response following a reversal. NFS was measured by calculating the probability (measured in %) of switching a response following the presentation of misleading negative feedback (i.e., no reward), which occurs in a low proportion of trials. Thus, NFS was high if participants switched response following negative misleading feedback (i.e., lose-shift behavior) and low if participants maintained the usually rewarded option following negative misleading feedback (i.e., lose-stay behavior). See Figure 1 for an illustration of the PRL task.
Figure 1
Control Measures: Mood and Double-Blinding Efficacy
Transient changes in mood state have been demonstrated to influence cognitive functioning (Federmeier et al.,
Procedure
This was a double-blind, placebo-controlled, mixed design experiment. Participants were required to attend a session lasting approximately 70 min. After screening for eligibility, participants were instructed to refrain from eating/drinking for a minimum of 3 h. They first signed a consent form, followed by the mood questionnaire (VAS; time 1), and were then asked to complete two blocks of the (PRL time 1). They were then randomly assigned to receive either tryptophan or placebo. Sixty minutes following tryptophan intake which corresponds to peak plasma concentration (Markus et al.,
Statistical Analyses
Statistical analyses were performed using SPSS version 23 (SPSS Inc., Chicago, IL, USA). Sample size was chosen to deliver power at 0.8, an alpha level set at 0.05, and a large effect size (d) of 0.8 (G*Power 3.1.9.2, Germany). For all the dependent measures of the PRL, we first run an independent sample t-test at baseline (time 1), between placebo and tryptophan participants. If there was no significant performance difference at baseline, we calculated a percentage change at time 2 compared with time 1, and used these new values to compare placebo with tryptophan using another independent sample t-test. A full breakdown of the raw data is shown in Table 1.
Table 1
| Group | NFS (%) | Reversal errors | Total errors |
|---|---|---|---|
| Placebo (pre-drug) | 48.71 (6.5) | 18.20 (6.3) | 80.30 (35.6) |
| Placebo (post-drug) | 43.23 (8.2) | 17.47 (3.5) | 63.68 (31.4) |
| Tryptophan (pre-drug) | 46.34 (4.5) | 20.31 (7.0) | 76.80 (23.4) |
| Tryptophan (post-drug) | 46.36 (6.1) | 17.43 (4.9) | 71.00 (35.2) |
Mean and SD (in brackets) for negative feedback sensitivity (NFS), reversal errors and total errors in placebo and tryptophan participants.
Mood data were analyzed using a two-way factorial repeated measures analysis of variance (ANOVA), where the factor of Time had two levels (VAS 1–2) and drugs had two (placebo and tryptophan).
The double blinding efficacy of tryptophan/placebo was analyzed using a percentage correct measure. A score of 100 was given if a participant correctly identified condition whereas a score of 0 if not.
Results
PRL Negative Feedback Sensitivity (NFS)
To assess the impact of tryptophan administration on NFS, we first took a measurement at baseline (i.e., prior to drug intake). There was no significant difference in NFS performance between placebo and tryptophan participants, t(42) = −1.38, p = 0.172, d = 0.41. Therefore, we calculated a change in performance, expressed in percentage, from time 2 (after drug intake) to time 1 (before drug intake; as in time 2/time 1).
Here, whilst there was a reduction in NFS between time 1 and time 2 for placebo participants, tryptophan abolished this change. As a result, NFS was significantly higher for tryptophan than placebo participants, t(42) = −2.80, p = 0.008, d = 0.84, see Figure 2A.
Figure 2

(A) Percentage change from baseline (time 2/time 1) for placebo and tryptophan participants with respect to NFS performance. NFS was significantly higher when comparing tryptophan to placebo participants. (B) As for (A). There was no significant difference between the two groups on reversal learning errors. (C) As for (A,B). There was no significant difference between placebo and tryptophan participants on total errors. ***Indicates p < 0.01. Error bars as standard error of the mean (SEM).
PRL Reversal Learning Errors
The same analyses were applied for reversal learning errors. At baseline, there was no significant difference in performance between placebo and tryptophan participants, t(42) = −1.04, p = 0.301, d = 0.31.
Both placebo and tryptophan participants improved from baseline. This change in performance from post-drug to pre-drug was not significantly different between the two groups, t(42) = −1.33, p = 0.188, d = 0.40, see Figure 2B.
PRL Total Errors
As for NFS and reversal learning errors, there was no significant difference between placebo and tryptophan participants at baseline, t(42) = 0.34, p = 0.703, d = 0.11. Tryptophan, also did not affect changes in performance from baseline, t(42) = 1.21, p = 0.231, d = 0.36, see Figure 2C.
Changes in Reversal Errors Are Independent of Changes in NFS
To confirm that NFS and reversal errors provide separate measures of performance, we run a bivariate correlation analysis between the variables reversal errors (measured as a change in performance from pre-drug to post-drug) and NFS (also measured as a change in performance). There was no significant correlation between reversal errors and NFS, r = 0.05, p = 0.701, see Figure 3.
Figure 3

Correlation scatterplot for the variables reversal errors (Y axis) and NFS (X axis) across participants. X and Y values represent a percentage change (%) from baseline (i.e., before drug) to after tryptophan or placebo intake. + for Reversal Errors indicate a reduction in errors, whereas + for NFS equals an increase in NFS.
Control Measures: Mood and Double Blinding Efficacy
There was neither a significant main effect of time, F(1,42) = 1.04, p = 0.313 on mood scores nor a main effect of drugs, F(1,42) = 1.45, p = 0.234, nor a drugs × time interaction, F(1,42) = 0.81, p = 0.373. The probability of participants guessing the correct drug (placebo or tryptophan) was at about chance level (56%).
Discussion
We report a selective effect of tryptophan supplementation on NFS but not on PRL. Whereas for participants in the placebo group their NFS decreased from pre-drug administration to post-drug (due to a practice effect), tryptophan suppressed this reduction. This had the net effect that NFS was higher for participants in the tryptophan condition than the placebo controls (i.e., when measured as a change in performance from baseline to post-drug administration). PRL (reversal errors) and total errors performance, however, was unaffected. Exploratory analyses (i.e., Figure 3), confirm that reversal errors and NFS are dissociated from one another. The observation that reversal and total errors decreased for the tryptophan group from pre-drug to post-drug but NFS did not, demonstrates that tryptophan did not affect the overall acquisition of the task but inhibited NFS learning specifically.
This finding is in line with the tryptophan depletion studies (Evers et al.,
Our finding that tryptophan supplementation increased NFS is similar to some reports in which small (but not large) doses of SSRI were administered (Chamberlain et al.,
Statements
Data availability statement
The datasets for this manuscript are not publicly available because Data can be made available upon request. Requests to access the datasets should be directed to luca.aquili@shu.ac.uk.
Ethics statement
The study was approved by the Sheffield Hallam University Psychology Ethics committee. All participants signed a consent form before testing could begin. A set of inclusion/exclusion criteria were used to select participants.
Author contributions
L-MB, TG and NN performed the experiments. MT wrote the manuscript. LA designed the experiments, performed the statistical analysis of the data and wrote the manuscript.
Funding
This work was supported by Sheffield Hallam University.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
- ATD
acute tryptophan depletion
- NFS
negative feedback sensitivity
- PRL
probabilistic reversal learning
- SERT
serotonin transporter
- SSRI
serotonin reuptake inhibitor
- VAS
visual analog scale
- 5-HT
serotonin.
Abbreviations
References
1
BakicJ.JepmaM.De RaedtR.PourtoisG. (2014). Effects of positive mood on probabilistic learning: behavioral and electrophysiological correlates. Biol. Psychol.103, 223–232. 10.1016/j.biopsycho.2014.09.012
2
BariA.TheobaldD. E.CaprioliD.MarA. C.Aidoo-MicahA.DalleyJ. W.et al. (2010). Serotonin modulates sensitivity to reward and negative feedback in a probabilistic reversal learning task in rats. Neuropsychopharmacology35, 1290–1301. 10.1038/npp.2009.233
3
BooijL.MerensW.MarkusC. R.Van der DoesA. W. (2006). Diet rich in α-lactalbumin improves memory in unmedicated recovered depressed patients and matched controls. J. Psychopharmacol.20, 526–535. 10.1177/0269881105057466
4
BoulougourisV.GlennonJ. C.RobbinsT. W. (2008). Dissociable effects of selective 5-HT 2A and 5-HT 2C receptor antagonists on serial spatial reversal learning in rats. Neuropsychopharmacology33, 2007–2019. 10.1038/sj.npp.1301584
5
BrownH. D.AmodeoD. A.SweeneyJ. A.RagozzinoM. E. (2012). The selective serotonin reuptake inhibitor, escitalopram, enhances inhibition of prepotent responding and spatial reversal learning. J. Psychopharmacol.26, 1443–1455. 10.1177/0269881111430749
6
CarandangC.JabbalR.MacbrideA.ElbeD. (2011). A review of escitalopram and citalopram in child and adolescent depression. J. Can. Acad. Child Adolesc. Psychiatry20, 315–324.
7
CeritH.SchuurR. J.de BruijnE. R.Van der DoesW. (2015). Tryptophan supplementation and the response to unfairness in healthy volunteers. Front. Psychol.6:1012. 10.3389/fpsyg.2015.01012
8
ChamberlainS. R.MüllerU.BlackwellA. D.ClarkL.RobbinsT. W.SahakianB. J. (2006). Neurochemical modulation of response inhibition and probabilistic learning in humans. Science311, 861–863. 10.1126/science.1121218
9
ClarkeH.DalleyJ.CroftsH.RobbinsT.RobertsA. (2004). Cognitive inflexibility after prefrontal serotonin depletion. Science304, 878–880. 10.1126/science.1094987
10
ClarkeH.WalkerS.CroftsH.DalleyJ.RobbinsT.RobertsA. C. (2005). Prefrontal serotonin depletion affects reversal learning but not attentional set shifting. J. Neurosci.25, 532–538. 10.1523/JNEUROSCI.3690-04.2005
11
CoolsR.ClarkL.OwenA. M.RobbinsT. W. (2002). Defining the neural mechanisms of probabilistic reversal learning using event-related functional magnetic resonance imaging. J. Neurosci.22, 4563–4567. 10.1523/JNEUROSCI.22-11-04563.2002
12
CoolsR.D’EspositoM. (2011). Inverted-U-shaped dopamine actions on human working memory and cognitive control. Biol. Psychiatry69, e113–e125. 10.1016/j.biopsych.2011.03.028
13
CoolsR.RobinsonO. J.SahakianB. (2008). Acute tryptophan depletion in healthy volunteers enhances punishment prediction but does not affect reward prediction. Neuropsychopharmacology33, 2291–2299. 10.1038/sj.npp.1301598
14
CulverhouseR. C.SacconeN. L.HortonA. C.MaY.AnsteyK. J.BanaschewskiT.et al. (2018). Collaborative meta-analysis finds no evidence of a strong interaction between stress and 5-HTTLPR genotype contributing to the development of depression. Mol. Psychiatry23, 133–142. 10.1038/mp.2017.44
15
den OudenH. E.DawN. D.FernandezG.ElshoutJ. A.RijpkemaM.HoogmanM.et al. (2013). Dissociable effects of dopamine and serotonin on reversal learning. Neuron80, 1090–1100. 10.1016/j.neuron.2013.08.030
16
DoughertyD. M.MarshD. M.MathiasC. W.DawesM. A.BradleyD. M.MorganC. J.et al. (2007). The effects of alcohol on laboratory-measured impulsivity after L-tryptophan depletion or loading. Psychopharmacology193, 137–150. 10.1007/s00213-007-0763-6
17
EversE.TillieD.van der VeenF.LiebenC.JollesJ.DeutzN.et al. (2005). Effects of a novel method of acute tryptophan depletion on plasma tryptophan and cognitive performance in healthy volunteers. Psychopharmacology177, 217–223. 10.1007/s00213-004-2141-y
18
FedermeierK. D.KirsonD. A.MorenoE. M.KutasM. (2001). Effects of transient, mild mood states on semantic memory organization and use: an event-related potential investigation in humans. Neurosci. Lett.305, 149–152. 10.1016/s0304-3940(01)01843-2
19
FingerE. C.MarshA. A.BuzasB.KamelN.RhodesR.VythilinghamM.et al. (2007). The impact of tryptophan depletion and 5-HTTLPR genotype on passive avoidance and response reversal instrumental learning tasks. Neuropsychopharmacology32, 206–215. 10.1038/sj.npp.1301182
20
HulskenS.MärtinA.MohajeriM. H.HombergJ. R. (2013). Food-derived serotonergic modulators: effects on mood and cognition. Nutr. Res. Rev.26, 223–234. 10.1017/s0954422413000164
21
IneichenC.SigristH.SpinelliS.LeschK.-P.SautterE.SeifritzE.et al. (2012). Establishing a probabilistic reversal learning test in mice: evidence for the processes mediating reward-stay and punishment-shift behaviour and for their modulation by serotonin. Neuropharmacology63, 1012–1021. 10.1016/j.neuropharm.2012.07.025
22
MarkusC. R.FirkC.GerhardtC.KloekJ.SmoldersG. F. (2008). Effect of different tryptophan sources on amino acids availability to the brain and mood in healthy volunteers. Psychopharmacology201, 107–114. 10.1007/s00213-008-1254-0
23
MohajeriM.WittwerJ.VargasK.HoganE.HolmesA.RogersP.et al. (2015). Chronic treatment with a tryptophan-rich protein hydrolysate improves emotional processing, mental energy levels and reaction time in middle-aged women. Br. J. Nutr.113, 350–365. 10.1017/s0007114514003754
24
MorganR. M.ParryA. M.AridaR. M.MatthewsP. M.DaviesB.CastellL. M. (2007). Effects of elevated plasma tryptophan on brain activation associated with the Stroop task. Psychopharmacology190, 383–389. 10.1007/s00213-006-0609-7
25
MuellerS. T.PiperB. J. (2014). The psychology experiment building language (PEBL) and PEBL test battery. J. Neurosci. Methods222, 250–259. 10.1016/j.jneumeth.2013.10.024
26
MurphyS. E.LonghitanoC.AyresR. E.CowenP. J.HarmerC. J. (2006). Tryptophan supplementation induces a positive bias in the processing of emotional material in healthy female volunteers. Psychopharmacology187, 121–130. 10.1007/s00213-006-0401-8
27
MurphyF.MichaelA.RobbinsT.SahakianB. (2003). Neuropsychological impairment in patients with major depressive disorder: the effects of feedback on task performance. Psychol. Med.33, 455–467. 10.1017/s0033291702007018
28
MurphyF.SmithK.CowenP.RobbinsT.SahakianB. (2002). The effects of tryptophan depletion on cognitive and affective processing in healthy volunteers. Psychopharmacology163, 42–53. 10.1007/s00213-002-1128-9
29
Ong Lai TeikD.LeeX. S.LimC. J.LowC. M.MuslimaM.AquiliL. (2016). Ginseng and ginkgo biloba effects on cognition as modulated by cardiovascular reactivity: a randomised trial. PLoS One11:e0150447. 10.1371/journal.pone.0150447
30
RibyL. M.OngD. L. T.AzmieN. B. M.OoiE. L.ReginaC.YeoE. K. W.et al. (2017). Impulsiveness, postprandial blood glucose, and glucoregulation affect measures of behavioral flexibility. Nutr. Res.48, 65–75. 10.1016/j.nutres.2017.10.011
31
RobbinsT. W. (2017). Cross-species studies of cognition relevant to drug discovery: a translational approach. Br. J. Pharmacol.174, 3191–3199. 10.1111/bph.13826
32
RobinsonO. J.CoolsR.SahakianB. J. (2012). Tryptophan depletion disinhibits punishment but not reward prediction: implications for resilience. Psychopharmacology219, 599–605. 10.1007/s00213-011-2410-5
33
RygulaR.Noworyta-SokolowskaK.DrozdR.KozubA. (2018). Using rodents to model abnormal sensitivity to feedback in depression. Neurosci. Biobehav. Rev.95, 336–346. 10.1016/j.neubiorev.2018.10.008
34
SilberB.SchmittJ. (2010). Effects of tryptophan loading on human cognition, mood and sleep. Neurosci. Biobehav. Rev.34, 387–407. 10.1016/j.neubiorev.2009.08.005
35
SobczakS.HonigA.SchmittJ.RiedelW. (2003). Pronounced cognitive deficits following an intravenous L-tryptophan challenge in first-degree relatives of bipolar patients compared to healthy controls. Neuropsychopharmacology28, 711–719. 10.1038/sj.npp.1300055
36
SteenbergenL.SellaroR.ColzatoL. S. (2014). Tryptophan promotes charitable donating. Front. Psychol.5:1451. 10.3389/fpsyg.2014.01451
37
TalbotP. S.WatsonD. R.BarrettS. L.CooperS. J. (2006). Rapid tryptophan depletion improves decision-making cognition in healthy humans without affecting reversal learning or set shifting. Neuropsychopharmacology31, 1519–1525. 10.1038/sj.npp.1300980
38
Taylor TavaresJ. V.ClarkL.FureyM. L.WilliamsG. B.SahakianB. J.DrevetsW. C. (2008). Neural basis of abnormal response to negative feedback in unmedicated mood disorders. Neuroimage42, 1118–1126. 10.1016/j.neuroimage.2008.05.049
39
van der PlasseG.FeenstraM. G. (2008). Serial reversal learning and acute tryptophan depletion. Behav. Brain Res.186, 23–31. 10.1016/j.bbr.2007.07.017
Summary
Keywords
tryptophan, serotonin, reversal learning, negative feedback sensitivity, learning
Citation
Thirkettle M, Barker L-M, Gallagher T, Nayeb N and Aquili L (2019) Dissociable Effects of Tryptophan Supplementation on Negative Feedback Sensitivity and Reversal Learning. Front. Behav. Neurosci. 13:127. doi: 10.3389/fnbeh.2019.00127
Received
22 March 2019
Accepted
28 May 2019
Published
28 June 2019
Volume
13 - 2019
Edited by
Gregg Stanwood, Florida State University, United States
Reviewed by
Rafal Rygula, Polish Academy of Sciences, Poland; Alfredo Meneses, Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico
Updates

Check for updates
Copyright
© 2019 Thirkettle, Barker, Gallagher, Nayeb and Aquili.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Luca Aquili luca.aquili@shu.ac.uk
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.