Bayesian Network structure learning algorithm for highly missing and non imputable data: Application to breast cancer radiotherapy data

It is not uncommon for real-life data produced in healthcare to have a higher proportion of missing data than in other scopes. To take into account these missing data, imputation is not always desired by healthcare experts, and complete case analysis can lead to a significant loss of data. The algorithm proposed here, allows the learning of Bayesian Network graphs when both imputation and complete case analysis are not possible. The learning process is based on a set of local bootstrap learnings performed on complete sub-datasets which are then aggregated and locally optimized. This learning method presents competitive results compared to other structure learning algorithms, whatever the mechanism of missing data.

Towards a More Accurate Differential Analysis of Multiple Imputed Proteomics Data with mi4limma

Imputing missing values is a common practice in label-free quantitative proteomics. Imputation replaces a missing value by a user-defined one. However, the imputation itself is not optimally considered downstream of the imputation process. In particular, imputed datasets are considered as if they had always been complete. The uncertainty due to the imputation is not properly taken into account. Hence, the mi4p package provides a more accurate statistical analysis of multiple-imputed datasets. A rigorous multiple imputation methodology is implemented, leading to a less biased estimation of parameters and their variability, thanks to Rubin's rules. The imputation-based peptide's intensities' variance estimator is then moderated using Bayesian hierarchical models. This estimator is finally included in moderated t-test statistics to provide differential analyses results.

How visual attention span and phonological skills contribute to N170 print tuning: An EEG study in French dyslexic students

Developmental dyslexia is a disorder characterized by a sustainable learning deficit in reading. Based on ERP-driven approaches focusing on the visual word form area, electrophysiological studies have pointed a lack of visual expertise for written word recognition in dyslexic readers by contrasting the left-lateralized N170 amplitudes elicited by alphabetic versus non-alphabetic stimuli. Here, we investigated in 22 dyslexic participants and 22 age-matched control subjects how two behavioural abilities potentially affected in dyslexic readers (phonological and visual attention skills) contributed to the N170 expertise during a word detection task. Consistent with literature, dyslexic participants exhibited poorer performance in these both abilities as compared to healthy subjects. At the brain level, we observed (1) an unexpected preservation of the N170 expertise in the dyslexic group suggesting a possible compensatory mechanism and (2) a modulation of this expertise only by phonological skills, providing evidence for the phonological mapping deficit hypothesis.

Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics

Imputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one. However, the imputation itself may not be optimally considered downstream of the imputation process, as imputed datasets are often considered as if they had always been complete. Hence, the uncertainty due to the imputation is not adequately taken into account. We provide a rigorous multiple imputation strategy, leading to a less biased estimation of the parameters’ variability thanks to Rubin’s rules. The imputation-based peptide’s intensities’ variance estimator is then moderated using Bayesian hierarchical models. This estimator is finally included in moderated t-test statistics to provide differential analyses results. This workflow can be used both at peptide and protein-level in quantification datasets. Indeed, an aggregation step is included for protein-level results based on peptide-level quantification data. Our methodology, named mi4p, was compared to the state-of-the-art limma workflow implemented in the DAPARR package, both on simulated and real datasets. We observed a trade-off between sensitivity and specificity, while the overall performance of mi4p outperforms DAPAR in terms of F-Score.

Statistical inference methods commonly used in quantitative proteomics are based on the measurement of peptide intensities. They allow the deduction of protein abundances provided that sufficient peptides per protein are available. However, they do not satisfactorily consider peptides or proteins whose intensities are missing under certain conditions, even though they are particularly interesting from a biological or medical point of view, since they may explain a difference between the groups being compared. Some state-of-the-art statistical proteomics data processing software proposes to impute these missing values, while others simply remove proteins with too many missing peptides. The statistical treatment is not entirely satisfactory when imputation methods are used, notably multiple imputation techniques. Indeed, even if these statistical tools are relevant in this context, the data sets once imputed are considered as having always been complete in the subsequent analyses: the uncertainty caused by the imputation is not taken into account. These analyses generally conclude with a study of the differences in protein abundances between the different conditions, either using Student’s or Welch’s test for the most rudimentary approaches or using the t-tempered testing techniques based on empirical Bayesian approaches. Thus, we propose a new methodology that starts by imputing missing values at the peptide level and estimating the uncertainty associated with this imputation and naturally extends by incorporating this uncertainty into the current moderated variance estimation techniques.

The MHC class I MICA gene is a histocompatibility antigen in kidney transplantation

The identity of histocompatibility loci, besides human leukocyte antigen (HLA), remains elusive. The major histocompatibility complex (MHC) class I MICA gene is a candidate histocompatibility locus. Here, we investigate its role in a French multicenter cohort of 1,356 kidney transplants. MICA mismatches were associated with decreased graft survival (hazard ratio (HR), 2.12; 95% confidence interval (CI): 1.45–3.11; P < 0.001). Both before and after transplantation anti-MICA donor-specific antibodies (DSA) were strongly associated with increased antibody-mediated rejection (ABMR) (HR, 3.79; 95% CI: 1.94–7.39; P < 0.001; HR, 9.92; 95% CI: 7.43–13.20; P < 0.001, respectively). This effect was synergetic with that of anti-HLA DSA before and after transplantation (HR, 25.68; 95% CI: 3.31–199.41; P = 0.002; HR, 82.67; 95% CI: 33.67–202.97; P < 0.001, respectively). De novo-developed anti-MICA DSA were the most harmful because they were also associated with reduced graft survival (HR, 1.29; 95% CI: 1.05–1.58; P = 0.014). Finally, the damaging effect of anti-MICA DSA on graft survival was confirmed in an independent cohort of 168 patients with ABMR (HR, 1.71; 95% CI: 1.02–2.86; P = 0.041). In conclusion, assessment of MICA matching and immunization for the identification of patients at high risk for transplant rejection and loss is warranted.

Analysis of a multicenter cohort of kidney transplants shows that mismatches in the MICA locus and the presence of anti-MICA donor-specific antibodies are associated with reduced graft survival and increased rejection.

A 4He vector zero-field optically pumped magnetometer operated in the Earth-field

Low intrinsic noise, high bandwidth, and high accuracy vector magnetometers are key components for many ground or space geophysical applications. Here, we report the design and the test of a ⁴He vector optically pumped magnetometer specifically dedicated to these needs. It is based on a parametric resonance magnetometer architecture operated in the Earth magnetic field with closed-loop compensation of the three components of the magnetic field. It provides offset-free vector measurements in a ±70 μT range with a DC to 1 kHz bandwidth. We demonstrate a vector sensitivity up to 130 fT/√Hz, which is about ten times better than the best available fluxgate magnetometers currently available for the same targeted applications.

selectBoost: a general algorithm to enhance the performance of variable selection methods

Motivation

With the growth of big data, variable selection has become one of the critical challenges in statistics. Although many methods have been proposed in the literature, their performance in terms of recall (sensitivity) and precision (predictive positive value) is limited in a context where the number of variables by far exceeds the number of observations or in a highly correlated setting.

Results

In this article, we propose a general algorithm, which improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. Our algorithm can either produce a confidence index for variable selection or be used in an experimental design planning perspective. We demonstrate the performance of our algorithm on both simulated and real data. We then apply it in two different ways to improve biological network reverse-engineering.

Availability and implementation

Code is available as the SelectBoost package on the CRAN, https://cran.r-project.org/package=SelectBoost. Some network reverse-engineering functionalities are available in the Patterns CRAN package, https://cran.r-project.org/package=Patterns.

Supplementary information

Supplementary data are available at Bioinformatics online.

Combining label-free and label-based accurate quantifications with SWATH-MS: Comparison with SRM and PRM for the evaluation of bovine muscle type effects

Mass spectrometry has proven to be a valuable tool for the accurate quantification of proteins. In this study, the performances of three targeted approaches, namely selected reaction monitoring (SRM), parallel reaction monitoring (PRM) and sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS), to accurately quantify ten potential biomarkers of beef meat tenderness or marbling in a cohort of 64 muscle samples were evaluated. So as to get the most benefit out of the complete MS2 maps that are acquired in SWATH-MS, an original label-free quantification method to estimate protein amounts using an I-spline regression model was developed. Overall, SWATH-MS outperformed SRM in terms of sensitivity and dynamic range, while PRM still performed the best, and all three strategies showed similar quantification accuracies and precisions for the absolute quantification of targets of interest. This targeted picture was extended by 585 additional proteins for which amounts were estimated using the label-free approach on SWATH-MS; thus, offering a more global profiling of muscle proteomes and further insights into muscle type effect on candidate biomarkers of beef meat qualities as well as muscle metabolism.

Impact of the COVID-19 pandemic on clinical research in hospitals: observational study in the first epicenter of the epidemic during the general lockdown in France

OBJECTIVE

The COVID-19 epidemic has had a strong impact on the entire healthcare sector in France with priority being given to research for new therapeutic options for COVID-19. Nevertheless, continuity of care for patients suffering from other diseases represents a crucial challenge, and clinical research is no exception in this respect. This study aims to assess the impact of the strict Covid-19 lockdown on non-Covid-19 clinical research in the French University Hospital of Strasbourg.

MATERIALS AND METHODS

Clinical research activity (non-Covid-19) from the point of view of pharmacy department was estimated and compared to the pre-lockdown period. The impact of lockdown was assessed through five indicators: site initiation visits, the initiation of experimental therapies in non-Covid-19 patients, the delivery of non-Covid-19 investigational medical products, the number of drug shipments to patients' homes, and the number of monitoring or closure visits.

RESULTS

During the study period, the number of site initiation visits decreased by 90%, total inclusions by 72%, and delivery of investigational medical products by 30%. During the lockdown period, 15 treatments were sent to patients' homes. Monitoring activity decreased by 98%.

CONCLUSIONS

Although the COVID-19 outbreak has created an incredible momentum in the field of clinical research, research not focused on SaRS-CoV-2 has suffered greatly from this situation. The impact on patients is difficult to estimate but should be further investigated.

Compatibility at amino acid position 98 of MICB reduces the incidence of graft-versus-host disease in conjunction with the CMV status

Graft-versus-host disease (GVHD) and cytomegalovirus (CMV)-related complications are leading causes of mortality after unrelated-donor hematopoietic cell transplantation (UD-HCT). The non-conventional MHC class I gene MICB, alike MICA, encodes a stress-induced polymorphic NKG2D ligand. However, unlike MICA, MICB interacts with the CMV-encoded UL16, which sequestrates MICB intracellularly, leading to immune evasion. Here, we retrospectively analyzed the impact of mismatches in MICB amino acid position 98 (MICB98), a key polymorphic residue involved in UL16 binding, in 943 UD-HCT pairs who were allele-matched at HLA-A, -B, -C, -DRB1, -DQB1 and MICA loci. HLA-DP typing was further available. MICB98 mismatches were significantly associated with an increased incidence of acute (grade II-IV: HR, 1.20; 95% CI, 1.15 to 1.24; P < 0.001; grade III-IV: HR, 2.28; 95% CI, 1.56 to 3.34; P < 0.001) and chronic GVHD (HR, 1.21; 95% CI, 1.10 to 1.33; P < 0.001). MICB98 matching significantly reduced the effect of CMV status on overall mortality from a hazard ratio of 1.77 to 1.16. MICB98 mismatches showed a GVHD-independent association with a higher incidence of CMV infection/reactivation (HR, 1.84; 95% CI, 1.34 to 2.51; P < 0.001). Hence selecting a MICB98-matched donor significantly reduces the GVHD incidence and lowers the impact of CMV status on overall survival.

Determining the number of components in PLS regression on incomplete data set

Partial least squares regression - or PLS regression - is a multivariate method in which the model parameters are estimated using either the SIMPLS or NIPALS algorithm. PLS regression has been extensively used in applied research because of its effectiveness in analyzing relationships between an outcome and one or several components. Note that the NIPALS algorithm can provide estimates parameters on incomplete data. The selection of the number of components used to build a representative model in PLS regression is a central issue. However, how to deal with missing data when using PLS regression remains a matter of debate. Several approaches have been proposed in the literature, including the Q2 criterion, and the AIC and BIC criteria. Here we study the behavior of the NIPALS algorithm when used to fit a PLS regression for various proportions of missing data and different types of missingness. We compare criteria to select the number of components for a PLS regression on incomplete data set and on imputed data set using three imputation methods: multiple imputation by chained equations, k-nearest neighbour imputation, and singular value decomposition imputation. We tested various criteria with different proportions of missing data (ranging from 5% to 50%) under different missingness assumptions. Q2-leave-one-out component selection methods gave more reliable results than AIC and BIC-based ones.

BCR-associated factors driving chronic lymphocytic leukemia cells proliferation ex vivo

A chronic antigenic stimulation is believed to sustain the leukemogenic development of chronic lymphocytic leukemia (CLL) and most of lymphoproliferative malignancies developed from mature B cells. Reproducing a proliferative stimulation ex vivo is critical to decipher the mechanisms of leukemogenesis in these malignancies. However, functional studies of CLL cells remains limited since current ex vivo B cell receptor (BCR) stimulation protocols are not sufficient to induce the proliferation of these cells, pointing out the need of mandatory BCR co-factors in this process. Here, we investigated benefits of several BCR co-stimulatory molecules (IL-2, IL-4, IL-15, IL-21 and CD40 ligand) in multiple culture conditions. Our results demonstrated that BCR engagement (anti-IgM ligation) concomitant to CD40 ligand, IL-4 and IL-21 stimulation allowed CLL cells proliferation ex vivo. In addition, we established a proliferative advantage for ZAP70 positive CLL cells, associated to an increased phosphorylation of ZAP70/SYK and STAT6. Moreover, the use of a tri-dimensional matrix of methylcellulose and the addition of TLR9 agonists further increased this proliferative response. This ex vivo model of BCR stimulation with T-derived cytokines is a relevant and efficient model for functional studies of CLL as well as lymphoproliferative malignancies.

Does partial titin degradation affect sarcomere length nonuniformities and force in active and passive myofibrils?

The aim of this study was to determine the role of titin in preventing the development of sarcomere length nonuniformities following activation and after active and passive stretch by determining the effect of partial titin degradation on sarcomere length nonuniformities and force in passive and active myofibrils. Selective partial titin degradation was performed using a low dose of trypsin. Myofibrils were set at a sarcomere length of 2.4 µm and then passively stretched to sarcomere lengths of 3.4 and 4.4 µm. In the active condition, myofibrils were set at a sarcomere length of 2.8 µm, activated, and actively stretched by 1 µm/sarcomere. The extent of sarcomere length nonuniformities was calculated for each sarcomere as the absolute difference between sarcomere length and the mean sarcomere length of the myofibril. Our main finding is that partial titin degradation does not increase sarcomere length nonuniformities after passive stretch and activation compared with when titin is intact but increases the extent of sarcomere length nonuniformities after active stretch. Furthermore, when titin was partially degraded, active and passive stresses were substantially reduced. These results suggest that titin plays a crucial role in actively stretched myofibrils and is likely involved in active and passive force production.

Magnetocardiography measurements with 4He vector optically pumped magnetometers at room temperature

In this paper, we present a proof of concept study which demonstrates for the first time the possibility of recording magnetocardiography (MCG) signals with ⁴He vector optically pumped magnetometers (OPM) operated in a gradiometer mode. Resulting from a compromise between sensitivity, size and operability in a clinical environment, the developed magnetometers are based on the parametric resonance of helium in a zero magnetic field. Sensors are operated at room temperature and provide a tri-axis vector measurement of the magnetic field. Measured sensitivity is around 210 f T (√Hz)^-1 in the bandwidth (2 Hz; 300 Hz). MCG signals from a phantom and two healthy subjects are successfully recorded. Human MCG data obtained with the OPMs are compared to reference electrocardiogram recordings: similar heart rates, shapes of the main patterns of the cardiac cycle (P/T waves, QRS complex) and QRS widths are obtained with both techniques.

Macroscopic discontinuous shear thickening versus local shear jamming in cornstarch

We study the emergence of discontinuous shear thickening (DST) in cornstarch by combining macroscopic rheometry with local magnetic resonance imaging measurements. We bring evidence that macroscopic DST is observed only when the flow separates into a low-density flowing and a high-density jammed region. In the shear-thickened steady state, the local rheology in the flowing region is not DST but, strikingly, is often shear thinning. Our data thus show that the stress jump measured during DST, in cornstarch, does not capture a secondary, high-viscosity branch of the local steady rheology but results from the existence of a shear jamming limit at volume fractions quite significantly below random close packing.

Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data

MOTIVATION

A vast literature from the past decade is devoted to relating gene profiles and subject survival or time to cancer recurrence. Biomarker discovery from high-dimensional data, such as transcriptomic or single nucleotide polymorphism profiles, is a major challenge in the search for more precise diagnoses. The proportional hazard regression model suggested by Cox (1972), to study the relationship between the time to event and a set of covariates in the presence of censoring is the most commonly used model for the analysis of survival data. However, like multivariate regression, it supposes that more observations than variables, complete data, and not strongly correlated variables are available. In practice, when dealing with high-dimensional data, these constraints are crippling. Collinearity gives rise to issues of over-fitting and model misidentification. Variable selection can improve the estimation accuracy by effectively identifying the subset of relevant predictors and enhance the model interpretability with parsimonious representation. To deal with both collinearity and variable selection issues, many methods based on least absolute shrinkage and selection operator penalized Cox proportional hazards have been proposed since the reference paper of Tibshirani. Regularization could also be performed using dimension reduction as is the case with partial least squares (PLS) regression. We propose two original algorithms named sPLSDR and its non-linear kernel counterpart DKsPLSDR, by using sparse PLS regression (sPLS) based on deviance residuals. We compared their predicting performance with state-of-the-art algorithms on both simulated and real reference benchmark datasets.

RESULTS

sPLSDR and DKsPLSDR compare favorably with other methods in their computational time, prediction and selectivity, as indicated by results based on benchmark datasets. Moreover, in the framework of PLS regression, they feature other useful tools, including biplots representation, or the ability to deal with missing data. Therefore, we view them as a useful addition to the toolbox of estimation and prediction methods for the widely used Cox's model in the high-dimensional and low-sample size settings.

AVAILABILITY AND IMPLEMENTATION

The R-package plsRcox is available on the CRAN and is maintained by Frédéric Bertrand. http://cran.r-project.org/web/packages/plsRcox/index.html.

CONTACT

pbastien@rd.loreal.com or fbertran@math.unistra.fr.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Assessment of the safety and immunogenicity of Rhodococcus equi-secreted proteins combined with either a liquid nanoparticle (IMS 3012) or a polymeric (PET GEL A) water-based adjuvant in adult horses and foals--identification of promising new candidate antigens

Rhodococcus equi is the most common infectious cause of mortality in foals between 1 and 6 months of age. Because of an increase in the number of antibiotic-resistant strains, the optimization of a prophylactic strategy is a key factor in the comprehensive management of R. equi pneumonia. The objectives of this study were to assess the safety and immunogenicity of R. equi-secreted proteins (ReSP) co-administered with either the nanoparticular adjuvant Montanide™ IMS 3012 VG, or a new polymeric adjuvant Montanide™ PET GEL A, and to further investigate the most immunogenic proteins for subsequent immunization/challenge experiments in the development of a vaccine against rhodoccocal pneumonia. The approach involved two phases. The first phase aimed to investigate the safety of vaccination in six adult horses. The second phase aimed to determine the safety and immunogenicity of vaccination in twelve 3-week-old foals. We set out to develop a method based on ultrasound measurements for safety assessment in adult horses in order to evaluate any in situ changes at the injection site, in the skin or the underlying muscle, with quantitative and qualitative data revealing that administration of ReSP combined with the Pet Gel A adjuvant led to an increase in local inflammation, associated with 4- to 7-fold higher levels of anti-R. equi IgGa, IgGb and IgGT, compared to administration of ReSP associated with IMS 3012 adjuvant, but without any impact on animal demeanor. Investigations were then performed in foals with serological and clinical follow-up until 6 months of age. Interestingly, we observed in foals a much lower incidence of adverse local tissue reactions at the injection site than in adult horses, with transient and moderate swelling for the group that received ReSP combined with Pet Gel A. Immunized foals with Pet Gel A adjuvant exhibited a similar response in both IgGa and IgGT levels, but a lower response in IgGb levels, compared to adult horses, with a subisotype profile that may however reflect a bias favorable to R. equi resistance. From the crude extract of secreted proteins, dot-blot screening enabled identification of cholesterol oxidase, mycolyl transferase 3, and PSP (probable secreted protein) as the most immunogenic candidates. Taken together, these results are encouraging in developing a vaccine for foals.

Cascade: a R package to study, predict and simulate the diffusion of a signal through a temporal gene network

SUMMARY

Temporal gene interactions, in response to environmental stress, form a complex system that can be efficiently described using gene regulatory networks. They allow highlighting the more influential genes and spotting some targets for biological intervention experiments. Despite that many reverse engineering tools have been designed, the Cascade package is an integrated solution adding several new and original key features such as the ability to predict changes in gene expressions after a biological perturbation in the network and graphical outputs that allow monitoring the spread of a signal through the network.

AVAILABILITY AND IMPLEMENTATION

The R package Cascade is available online at http://www-math.u-strasbg.fr/genpred/spip.php?rubrique4.

A refined molecular taxonomy of breast cancer

The current histoclinical breast cancer classification is simple but imprecise. Several molecular classifications of breast cancers based on expression profiling have been proposed as alternatives. However, their reliability and clinical utility have been repeatedly questioned, notably because most of them were derived from relatively small initial patient populations. We analyzed the transcriptomes of 537 breast tumors using three unsupervised classification methods. A core subset of 355 tumors was assigned to six clusters by all three methods. These six subgroups overlapped with previously defined molecular classes of breast cancer, but also showed important differences, notably the absence of an ERBB2 subgroup and the division of the large luminal ER+ group into four subgroups, two of them being highly proliferative. Of the six subgroups, four were ER+/PR+/AR+, one was ER−/PR−/AR+ and one was triple negative (AR−/ER−/PR−). ERBB2-amplified tumors were split between the ER−/PR−/AR+ subgroup and the highly proliferative ER+ LumC subgroup. Importantly, each of these six molecular subgroups showed specific copy-number alterations. Gene expression changes were correlated to specific signaling pathways. Each of these six subgroups showed very significant differences in tumor grade, metastatic sites, relapse-free survival or response to chemotherapy. All these findings were validated on large external datasets including more than 3000 tumors. Our data thus indicate that these six molecular subgroups represent well-defined clinico-biological entities of breast cancer. Their identification should facilitate the detection of novel prognostic factors or therapeutical targets in breast cancer.

Effect of continuous positive airway pressure treatment on the subsequent EEG spectral power and sleepiness over sustained wakefulness in patients with obstructive sleep apnea-hypopnea syndrome

OBJECTIVE

To determine whether sleepiness and its evolution over sustained wakefulness could be reversed by nasal continuous positive airway pressure (CPAP) therapy in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS).

METHODS

Twelve OSAHS patients underwent three 32-h sessions of study: one before CPAP therapy (T0), the second (T3) and the third (T6), respectively, after 3 and 6 months of therapy. Each session included one night of sleep followed by 24 h of sustained wakefulness, during which EEG recordings and subjective ratings were performed every hour.

RESULTS

The waking EEG in treated OSAHS patients was partially improved after 3 months of CPAP and their subjective complaint of sleepiness was normalized after 6 months. Theta power (3.9-7.8 Hz) was decreased as well as its time course during the diurnal period but beta power (12.7-29.2 Hz) remained higher.

CONCLUSIONS

CPAP partially reverses waking EEG abnormalities in OSAHS patients with reduced theta activity after 3 months and removes the subjective complaint of sleepiness after 6 months. Nevertheless, the persistence of increased beta activity in treated patients suggests that efforts to stay awake remain strong after CPAP treatment.

SIGNIFICANCE

CPAP influences the EEG's time course over sustained wakefulness in a frequency-specific manner in OSAHS patients.

Behavioural adjustment in response to increased predation risk: a study in three duck species

Predation directly triggers behavioural decisions designed to increase immediate survival. However, these behavioural modifications can have long term costs. There is therefore a trade-off between antipredator behaviours and other activities. This trade-off is generally considered between vigilance and only one other behaviour, thus neglecting potential compensations. In this study, we considered the effect of an increase in predation risk on the diurnal time-budget of three captive duck species during the wintering period. We artificially increased predation risk by disturbing two groups of 14 mallard and teals at different frequencies, and one group of 14 tufted ducks with a radio-controlled stressor. We recorded foraging, vigilance, preening and sleeping durations the week before, during and after disturbance sessions. Disturbed groups were compared to an undisturbed control group. We showed that in all three species, the increase in predation risk resulted in a decrease in foraging and preening and led to an increase in sleeping. It is worth noting that contrary to common observations, vigilance did not increase. However, ducks are known to be vigilant while sleeping. This complex behavioural adjustment therefore seems to be optimal as it may allow ducks to reduce their predation risk. Our results highlight the fact that it is necessary to encompass the whole individual time-budget when studying behavioural modifications under predation risk. Finally, we propose that studies of behavioural time-budget changes under predation risk should be included in the more general framework of the starvation-predation risk trade-off.