Using our novel surgical model of simultaneous intestinal adaptation “A” and neointestinal regeneration “N” conditions in individual rats to determine feasibility for research and clinical application, we further utilized next generation RNA sequencing (RNA-Seq) here in normal control tissue and both conditions (“A” and “N”) across time to decipher transcriptome changes in neoregeneration and adaptation of intestinal tissue at weeks 1, 4, and 12. We also performed bioinformatics analyses to identify key growth factors for improving intestinal adaptation and neointestinal regeneration. Our analyses indicate several interesting phenomena. First, Gene Ontology and pathway analyses indicate that cell cycle and DNA replication processes are enhanced in week 1 “A”; however, in week 1 “N”, many immune-related processes are involved. Second, we found some growth factors upregulated or downregulated especially in week 1 “N” versus “A”. Third, based on each condition and time point versus normal control tissue, we found in week 1 “N” BMP2, BMP3, and NTF3 are significantly and specifically downregulated, indicating that the regenerative process may be inhibited in the absence of these growth factors. This study reveals complex growth factor regulation in small neointestinal regeneration and intestinal adaptation and provides potential applications in tissue engineering by introducing key growth factors identified here into the injury site. In this study, we used next generation RNA sequencing (RNA-Seq) and bioinformatics analyses to explore transcriptome changes in neoregeneration and adaptation tissue and to identify key growth factors affecting regeneration and adaptation of intestinal tissue. Our previous research reveals complex growth factor regulation in small intestine regeneration and adaptation  and provTreatment of patients with short bowel syndrome remains a challenge. Drugs, lengthening surgeries , 3, and Regenerative processes occur in small bowel transplantation, intestinal adaptation , 6, and Using regenerative medicine, many organs and tissues are expected to be manufactured by tissue engineering –11. VariIntestine regeneration and adaptation are regulated by several classes of factors , such asTherefore, in this study, based on our previous rat surgery model , we furt ad libitum. All animals were weighed and switched to oral glucose water 24 hours before surgery for tube implantation or sacrifice.30 adult male Sprague-Dawley rats weighing 250-350 g were used for the present study. All animals were kept in a designated pathogen-free facility at the Animal Center of Keelung Chang-Gung Memorial Hospital in accordance with the rules and regulations of Institutional Animal Care and Use Committee (IACUC). This project was performed using the protocol approved by Keelung Chang-Gung Memorial Hospital IACUC # 2013112801. The animals were housed in individually ventilated cages  in an air-conditioned room at 20-24°C (relative humidity 55±5%), with lights on from 7:00 am to 7:00 pm and given standard rat feed and waterOne microgram total RNA collected from rat small intestine was subjected to Illumina TruSeq RNA library construction. The sequencing libraries were barcoded and sequencing reaction was performed using Illumina HiSeq 2500 in National Yang-Ming University Genome Research Center. Raw sequence data was aligned to Rnor_6.0 genome with Ensembl release-85 gene annotation using TopHat v2.1.1 , 31. The2 inhalation for 5 min.Rat surgery was performed by Dr. Jwo at the Chang Gung Memorial Hospital, Keelung . In brieμm in thickness and stained with hematoxylin and eosin (H&E staining) for microscopic examination of basic histological architecture. Histological analysis, including villus height, crypt depth, and newly regenerated length of neointestinal mucosa and muscle, was performed on each specimen using an Olympus IX-70 microscope accompanied by DP controller 3.2.1.276 calculation software. Villus height and crypt depth in neointestinal specimens were all measured in the mid-portion of each newly regenerated tissue. Common measures of 10 crypts/villus per animal were used to increase the validity.Newly regenerated constructs, along with the adapted segment of intestine, were harvested at weeks 1, 4, and 12 after surgery and normal intestine as control (day 0). The tissues were opened longitudinally, fixed in 10% buffered formalin, and embedded in paraffin. Specimens were sectioned onto slides 5  P value less than 0.05 were classified as significantly enriched. We also sent the differential expressed genes to Ingenuity Pathway Analysis  to perform network analysis and summarize the significant altered canonical pathways.To study the biological function of stage specific upregulated genes, we utilized WebGestalt web server  to perfo Direct-zol™ RNA MiniPrep kit  followed by reverse transcriptase using iScript cDNA synthesis kit . Target genes of primers were assessed by 2-step quantitative RT-PCR with SybrGreen Supermix  to label the amplified products. mRNA expression was quantified using the iCycler iQTM5 multicolor RT-qPCR detection system. Positive control was set with RNA alone and negative control without input of any templates. The relative expression levels of target genes were normalized against the expression level of GAPDH.To validate the expression levels in sequencing data, total RNA samples from the intestines of rats were analyzed for mRNA expression by quantitative real-time RT-PCR (RT-qPCR). The 44 most significantly up- and downregulated genes were selected as candidate genes for validation. Primers for the target genes Suppl.  were des P value of less than 0.05 was considered statistically significant.All data were expressed as the mean with standard deviation. The nonparametric Wilcoxon rank-sum test was used to test the difference among individual groups. All statistical analyses were performed using Statistical Package for Social Science version 17.0 . ATo understand the underlying molecular mechanisms between “A” and “N” conditions over time, we performed RNA next generation sequencing at weeks 0, 1, 4, and 12 after surgery of both “A” and “N” conditions using Illumina HiSeq 2500 platform. First, we performed principle component analysis (PCA) across the whole transcriptome on each sample . The x-aκB signaling, Wnt/β-catenin, sonic hedgehog are all upregulated, indicating a state of inflammatory condition , we then proposed another strategy to compare differential gene expression between these two conditions across three time points . When statistical analysis was performed comparing “A” and “N” conditions at week 1, we found 1686 genes significantly upregulated and 1655 genes significantly downregulated in “N” . For more comprehensive understanding of the expressions at each time point, we use heatmap to demonstrate differential expressed genes with unadjusted P value < 0.05 at the same time point. The upper panel of P < 0.001), TGFB1, GDF1, CD320 (P < 0.01) and VEGFB, MDK1 (P < 0.05). The differential expressed growth factor at week 4 and week 12 time points were also demonstrated  and REG1A (P < 0.05), whereas KITLG, JAG2, PDGFC, PDFGB, and FBRS are significantly upregulated in week 12 “N” (P < 0.05).We therefore hypothesized that at week 1 “A”, critical growth factor combinations are required for proper tissue regeneration. We compared growth factor expressions among three different conditions at three time points, demonstrated as a tile chart in  Figures  and confPadj < 0.05.) The heatmap in the upper panel of Figures  P value < 0.05, detail data in Suppl. P < 0.01), FGF13 and BMP3 (P < 0.05) are upregulated in “A” and in week 4 PDGFC (P < 0.01) and VEGFA (P < 0.05) . After qPCR confirmation on different cohorts of rats, we identified highly expressed genes in week 1 “N”, including RABEP2, VEGFB, GDF1, IL-11, and CD320. These growth factors are correlated with inflammation after tissue injury , granulaβ1 is one of the most important regulators of the ECM, regulating not only the production of multiple ECM components, but also collagen and fibronectin [α]   , chemokictin [α] , growth r (EGF)] , componer (EGF)] , angioger (EGF)] , peroxisr (EGF)] , mammalir (EGF)] , and pror (EGF)] , 68. Prer (EGF)] . Most noEGF)] [β . Besidescytokine , 72. Intcytokine . Communi


Knowledge of mechanisms promoting disruption though mechanical forces is essential in optimizing biofilm control strategies which rely on fluid shear. Our results provide insight into how biofilm disruption dynamics is governed by applied forces and fluid properties, revealing a mechanism for ripple formation and fluid-biofilm mixing. These findings have important implications for the rational design of new biofilm cleaning strategies with fluid jets, such as determining optimal parameters  to remove the biofilm from a certain zone  or using antimicrobial agents which could increase the interfacial area available for exchange, as well as causing internal mixing within the biofilm matrix, thus disrupting the localized microenvironment which is associated with antimicrobial tolerance. The developed model also has potential application in predicting drag and pressure drop caused by biofilms on bioreactor, pipeline, and ship hull surfaces. Streptococcus mutans biofilms, a dental pathogen causing caries, captured by high-speed imaging. The numerical model involved a two-phase flow of air over a non-Newtonian biofilm, whose viscosity as a function of shear rate was estimated using the Herschel-Bulkley model. The simulation suggested that inertial, shear, and interfacial tension forces governed biofilm disruption by the air jet. Additionally, the high shear rates generated by the jet impacts coupled with shear-thinning biofilm property resulted in rapid liquefaction (within milliseconds) of the biofilm, followed by surface instability and traveling waves from the impact site. Our findings suggest that rapid shear thinning under very high shear flows causes the biofilm to behave like a fluid and elasticity can be neglected. A parametric sensitivity study confirmed that both applied force intensity  and biofilm properties  intensify biofilm disruption by generating large interfacial instabilities.Experimental data showed that high-speed microsprays can effectively disrupt biofilms on their support substratum, producing a variety of dynamic reactions such as elongation, displacement, ripple formation, and fluidization. However, the mechanics underlying the impact of high-speed turbulent flows on biofilm structure is complex under such extreme conditions, since direct measurements of viscosity at these high shear rates are not possible using dynamic testing instruments. Here, we used computational fluid dynamics simulations to assess the complex fluid interactions of ripple patterning produced by high-speed turbulent air jets impacting perpendicular to the surface of  During recent years, close attention has been paid to the mechanical behavior of biofilms when subjected to high shear turbulent flows, because of direct applications in utilizing such methods for biofilm removal. Fluid-biofilm patterns, such as formation of migratory ripples and surface instabilities, transition to fluid-like behavior, and stretching to finally break off the biofilm, have been described –5. Furth–6–Several biofilm “constitutive” models, mathematical models which describe the mechanical properties of material and how a material will respond to mechanical forces, have been developed with consideration of viscoelastic properties and biofilm-fluid interaction, as reviewed by different authors –14. Biof–23–Streptococcus mutans biofilms exposed to high-velocity air jet perpendicular impingements. To this goal, two-dimensional (2D) axisymmetric CFD simulations were performed to describe the behavior of air-impinged biofilms, with consideration of turbulence and near-wall treatment. The non-Newtonian biofilm was examined under high shear rates to reveal the mechanisms driving its disruption under air jet impacts. The model was used to study the influence of parameters such as biofilm viscosity at rest and at predicted high shear rates, biofilm thickness, jet nozzle velocity, and air-biofilm surface tension on biofilm cohesiveness, deformation, and disruption.The present study was aimed at developing a CFD model to characterize the observed dynamic rippling patterns of 10.1128/mBio.02813-19.6VIDEO S1S. mutans biofilm attached to a glass surface, at an air velocity of 41.7 m·s−1, with a nozzle diameter of 2 mm. The biofilm consisted or larger clusters (lighter patches) distributed heterogeneously and separated by a base biofilm (gray). In the first few frames, the shutter can be seen being lifted out of the way, exposing the biofilm to the air jet. Biofilm ripples are forming radially away from the central impact area. After approximately 200 ms, the biofilm had been “pushed” from the central area to from a cleared area (darker) of approximately 1.5 cm. This video was used to generate data for the computational analysis. Download Movie S1, AVI file, 13.0 MB.High-speed  recording of the air jet impingement experiment on an Copyright © 2020 Prades et al.2020Prades et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the (i) Biofilm viscosity assessments. The shear-thinning character of S. mutans biofilms has been previously demonstrated by measuring the complex viscosity (S. mutans biofilms at much higher shear rates (104 to 106 s−1) than commonly reported for other biofilms . Shear rate measurements in this range are impracticable with normal rheometric systems, which can achieve shear rates of around 104 s−1. Microchannels containing microelectromechanical systems (MEMS) pressure sensors have been used to achieve a rate of almost 105 s−1  were represented together with the experimental reference values in 1 corresponded to the highest dynamic viscosity, while EVC4 was the lowest viscosity curve. To reproduce the observed liquefaction behavior  Computed results. For the experimental air inlet velocity (j u= 41.7 m·s−1), the biofilm response was simulated with different viscosity curves (γ) of the air-biofilm interface (γ = 72 mN·m−1 and γ = 36 mN·m−1). The former is for air-water γ at 20°C, considering the biofilm matrix to be more than 90% water  or γ = 36 mN·m−1 (right) from the early cavity formation , from a biofilm thickness of 55 μm with viscosity model EVC3 and surface tension γ = 36 mN·m−1, subjected to a perpendicular air jet with velocity of 41.7 m·s−1. Download Movie S2, AVI file, 12.1 MB.Simulated biofilm ripple formation in time  for viscosity model parameter EVC3 and surface tension γ = 36 mN·m−1. Download Movie S3, AVI file, 14.6 MB.Simulated changes of biofilm thickness in time (0 to 20 ms) over the radial direction  and better fit of the experimental data, but the differences were still too small to be considered significant.By tracking the position of the advancing front of the ripples, the biofilm displacement was determined as defined as the maximum distance traveled by the advancing front of the ripples over the underlying biofilm support at a given time . From thA simulated sequence of the air jet impingement over the biofilm in the lateral view showing fluid-biofilm interaction is presented in 10.1128/mBio.02813-19.9VIDEO S4x-z lateral view), from a biofilm thickness of 55 μm with viscosity model EVC3 and surface tension γ = 36 mN·m−1, subjected to a perpendicular air jet with velocity of 41.7 m·s−1. Colored surface, velocity magnitude (meters per second); gray surface, biofilm area. In this simulation, the jet is coming from the bottom of the screen, and the biofilm is on the top. Download Movie S4, AVI file, 2.3 MB.Simulated biofilm ripple formation over time , which directly produced high shear stresses (∼104 Pa) and relative pressure  in the air jet impact zone during air jet exposure. Significantly, the high shear rates generated on the air-biofilm interface rapidly reduced the biofilm dynamic viscosity from the initial value  to that of water (0.001 Pa⋅s), suggesting that complete liquefaction of the biofilm had occurred.A sequence of the air shear rate and the biofilm dynamic viscosity distributions is presented in 10.1128/mBio.02813-19.10VIDEO S5x-z lateral view), from a biofilm thickness of 55 μm with viscosity model parameter EVC3 and surface tension γ = 36 mN·m−1, subjected to a perpendicular air jet with velocity of 41.7 m·s−1. Gray-scale surface, shear rate (per second); color scale, biofilm dynamic viscosity (Pa⋅s). In this simulation, the jet is coming from the bottom of the screen, and the biofilm is on the top. Download Movie S5, AVI file, 4.3 MB.Simulated biofilm ripple formation over time  in the disrupted region  was dominant over the component in the axial direction (zu), which indicated the drag direction on the biofilm surface . The values averaged in time are listed in The velocity component in the radial direction .(i) Biofilm viscosity  underwent smaller biofilm displacement due to the higher resistance to flow (η = EVC3 (runs 1 and 2) meant a softer structure, disrupting the full biofilm length after only a few milliseconds of jet exposure, while experimentally, the biofilm displacement was less than 6 mm for 2 ms. The biofilm with the lowest viscosity  was disrupted over the largest radius, while biofilm residues remained unremoved in the cavity center for the more viscous biofilm   . Thus, eγ).(ii) Surface tension  allowed for quicker biofilm displacement initially (γ = 72 mN·m−1 (runs 2 and 4), until arriving at similar steady-state displacement, possibly explained by the stabilization effect of surface tension. Moreover, the lower surface tension produced ripples with higher frequency and higher amplitude, i.e., the biofilm surface was less unstable .(iii) Jet velocity ( The largest and fastest displacements were achieved with high velocity (runs 1 and 3) due to the higher shear rates produced .(iv) Biofilm thickness  was moved faster by the air jet and, consequently, reached stationary state sooner, i.e., in less than 8 ms, compared with >20 ms for the thicker film   . The thi, run 1)  and reacS. mutans bacteria exposed to air jets applied parallel to the surface  models. The difficulty, however, is not only the definition of 3D geometry and properties into the computational model but, more significantly, a large increase in computing time.Our model suggests that the air jet exposures generating high shear rates, coupled with the biofilm shear-thinning behavior, produced rapid (within milliseconds) biofilm disruption, thus affecting both the biofilm properties and the applied force intensity. The greatest interfacial instabilities are produced by the largest forces  and the lowest values of biofilm properties .S. mutans biofilms. Biofilm liquefaction, i.e., the complete breakdown of polymer network interactions in the biofilm matrix, is a mechanism that can explain the extremely quick disruptive effect induced by high shear airflows on the biofilms. The Herschel-Bulkley parameters of the estimated biofilm viscosity curve EVC3 described the required shear-thinning behavior, with the fitted yield stress (yσ = 0.745 Pa) in accordance with the viscoelastic linearity limit (σ = 3.5 Pa) previously determined by creep analysis  than for water-air (γ = 72 mN·m−1), as measurements for Bacillus subtilis, Pseudomonas fluorescens, and P. aeruginosa biofilms indicated values within the range of 25 to 50 mN·m−1  to remove or predict the spread of the biofilm in specific applications . The developed model also has potential application in predicting drag and pressure drop caused by biofilms in bioreactors, industrial pipelines, and ship hull surfaces, as well as predicting how shear might influence the removal or spread of biofilms associated with medical devices such as orthopedic implants, voice prostheses, catheters, and vascular stents.S. mutans UA159 (ATCC 700610) biofilms were grown for 72 h on glass microscope slides at 37°C and 5% CO2 in brain heart infusion  supplemented with 2% (wt/vol) sucrose (Sigma-Aldrich) and 1% (wt/vol) porcine gastric mucin . After the growth period, the biofilm-covered slides were gently rinsed in 1% (wt/vol) phosphate-buffered saline solution (Sigma-Aldrich) and placed in petri dishes (bL) of 55 μm in the simulations.i dishes . Biofilmju = 41.7 ± 1.5 m·s−1) was measured with a variable area flow meter. To estimate the Reynolds number for the biofilm (Reb) flowing along the substratum, the biofilm thickness (bL) was used as the characteristic length, and the biofilm density (bρ) was assumed to be equal to that of water. The variation of biofilm displacement velocity (bu) and biofilm viscosity (η) variables had greater effect in the Reb. At the highest shear stresses, the biofilm behaved as if it was completely liquefied to water, with η = 0.001 Pa⋅s  was 5,600, which is in the turbulent regime  impinged on biofilm samples at a 90° angle. Experiments were performed in triplicate. The compressor tip  was held at a 5 mm distance from the biofilm. The air jet impingement was recorded at 2,000 frames per second with a high-speed camera  placed to record the back view of the biofilm-covered microscope slide. The average air jet velocity exiting the nozzle  was used to determine the dominant period (T) and dominant frequency (f) of the ripple patterns in the R), defined as the distance between two reverse peaks, was measured using NIH ImageJ, as previously described  was calculated as uR = f × λR = λR/T.For the experimental high-speed videos, the ripple wavelength (λescribed . Brieflya); (v) the initial biofilm is a thin layer with constant thickness; and (vi) the biofilm is characterized by non-Newtonian fluid shear thinning (Herschel-Bulkley model) and density equal to that of water.The general assumptions made in the development of the numerical model were (i) the gas (air) and liquid (biofilm) phases are incompressible (Mach number below 0.3 for the gas phase); (ii) a uniform velocity profile, constant in time, leaves the compressor nozzle; (iii) the flow is symmetric with respect to the vertical axis (around the nozzle middle); (iv) the free gas jets are in the turbulent regime . The interface between fluids  was tracked with a robust coupling between level set and VOF methods, as implemented in the ANSYS Fluent software (Momentum conservation  is couplsoftware .An examination of Reynolds-averaged Navier-Stokes (RANS) modeling techniques recommends the shear-stress transport (SST) k-ω model instead of the standard k-ε model since it can better describe fluid flow in impinging jets within reasonable computational effort . The SSTThe turbulence kinetic energy, k, and the specific dissipation rate, ω, are obtained from the transport equations including the convection and viscous terms, together with terms for production and dissipation of k and ω and cross-diffusion of ω. A user-defined source term for ω, representing the turbulence damping correction, was added to correctly model the flows in the interfacial area. Turbulence damping was needed because otherwise, the large difference in physical properties of biofilm and air phases would create a large velocity gradient at the interface, resulting in unrealistically high turbulence generation . See secS. mutans biofilms, representing previously observed shear-thinning non-Newtonian behavior. The dynamic viscosity η (Pa·s) is inversely related to the shear rate σ  as shown in yσ representing the yield stress , K representing the consistency index (Pa·s), and n representing the flow behavior index.The Herschel-Bulkley model  was usedS. mutans biofilms , we extrapolated using data from dynamic viscosity sweeps to determine the complex viscosity of biofilms  and the y axis was used on the boundary AD, with radial velocity component and normal gradients equal to zero. The boundary EF was open to the atmosphere, depending on the mass balance. A zero gauge pressure outlet condition was set on FI. The air inlet was on DE , with a fully developed velocity profile. The inlet turbulent energy, k, was computed as shown in U indicating the mean flow velocity and I the turbulence intensity defined as I = 0.16 × Re−1/8 , the linear and logarithmic law-of-the-wall formulations were blended. To resolve the viscous sublayer, the first grid cell needed to be at about y+ ≈ 1, also near the free surface , with near-wall formulation to represent precisely the wall-bounded turbulent flow in the region, including the buffer layer and viscous sublayer. The reatment  was used surface . In the k and ω were computed using U equal to the inlet velocity (ju) and characteristic length l = 1 mm, and the volume fraction of the biofilm phase was set to 1 in the region ABHI  Meshing. A uniform mesh of prism cells was defined in the domain, with maximum size hx by hy of 50 μm by 17 μm, with a refined mesh in the region ACGI  to satisfy the requirement y+ ≈ 1 near walls and in the free surface. A mesh growth rate no higher than 1.2  Solvers. The mathematical model was implemented into the commercial fluid dynamics software ANSYS Fluent . The governing equations were discretized using a second-order upwind scheme in space and first-order implicit in time, with pressure staggering option interpolation (PRESTO) and pressure-implicit splitting of operators (PISO) for the pressure-velocity coupling. The free surface deformation was tracked with the georeconstructed scheme. Transient simulations ran with a maximum time step set to 10−7 s for stable transient solutions. A total time of 20 ms was simulated in each run to reach a quasistationary solution.(iii) Simulation plan. Two sets of simulations were carried out with the two-phase model. The first set (runs 1 to 7) was performed for model calibration, where the biofilm viscous properties were evaluated according to the experimental data. Experimental parameters, such as the measured jet velocity (ju) and biofilm thickness (bL), were used in this set with different non-Newtonian viscosities (η)  and two surface tensions (γ). The second set (runs 8 to 12) was performed for sensitivity analysis, evaluating the effects of the inlet jet velocity and the biofilm thickness on the biofilm rippling response. 10.1128/mBio.02813-19.1FILE S1Text S1, DOCX file, 0.1 MB.Computational and experimental investigation of biofilm disruption dynamics induced by high-velocity gas jet impingement. Download Copyright © 2020 Prades et al.2020Prades et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


Chronic Lymphocytic Leukemia (CLL) is the most frequent lymphoproliferative disorder in western countries and is characterized by a remarkable clinical heterogeneity. During the last decade, multiple genomic studies have identified a myriad of somatic events driving CLL proliferation and aggressivity. Nevertheless, and despite the mounting evidence of inherited risk for CLL development, the existence of germline variants associated with clinical outcomes has not been addressed in depth.Exome sequencing data from control leukocytes of CLL patients involved in the International Cancer Genome Consortium (ICGC) was used for genotyping. Cox regression was used to detect variants associated with clinical outcomes. Gene and pathways level associations were also calculated.PPP4R2 and MAP3K4 were associated with earlier treatment need. A gene-level analysis evidenced a significant association of RIPK3 with both treatment need and survival. Furthermore, germline variability in pathways such as apoptosis, cell-cycle, pentose phosphate, GNα13 and Nitric oxide was associated with overall survival.Single nucleotide polymorphisms in Our results support the existence of inherited conditionants of CLL evolution and points towards genes and pathways that may results useful as biomarkers of disease outcome. More research is needed to validate these findings.The online version of this article (10.1186/s12885-019-5628-y) contains supplementary material, which is available to authorized users. NOTCH1, SF3B1, ATM, TP53 and POT1, and the absence of somatic hypermutation in the IGHV locus.Chronic Lymphocytic Leukemia (CLL) is the most frequent lymphoproliferative disease in western countries, and it shows remarkable clinical heterogeneity . RecentlBCL2, EOMES, CASP10 and POT1 have been associated with significant risk of CLL development [Inherited predisposition to the development of CLL has been addressed by various genome wide association studies (GWAS) during the last years. In this regard, dozens of common variants at genes such as elopment –5. Similelopment , 7. DespPPP4R2 and MAP3K4) significantly associated with TTT. Moreover, we found significant associations with TTT and OS both at the gene and pathway-level, which could shed new light about CLL biology and its mechanisms of progression.In this paper, we addressed for the first time to our knowledge the association of common genomic variants with time to treatment (TTT) and OS of CLL patients participating in the Spanish ICGC cohort. Our results suggest the existence of polymorphisms at some genes  CLL sequencing data [European Genome-Phenome Database (EGA). The Data Access Committee approved access to this data under DACO-1040945. We downloaded exome-seq data from control non-tumoral samples from patients with CLL under the accession code EGAD00001001464.We applied for access to the ing data  depositebwa [Puente et al [SureSelect Human All Exon 50 Mb v4 kit or the SureSelect Human All Exon 50 Mb + UTR kits (Agilent Technologies). Next, DNA was pulled down using magnetic beads with streptavidin, followed by 18 cycles of amplification. Sequencing was performed on an Illumina GAIIx or on a HiSeq2000 sequencer (2x76bp). Duplicate read removal, sorting and indexing was done using samtools [BamUtil [Exome-seq data were previously aligned to the reference genome (GRCh37.75) using bwa  as descrte et al . Brieflysamtools . Base qu[BamUtil  using a minVarFreq = 0.02”, “minReads = 2”, “maxReads = 8000”, “assemble = 1”, “minBaseQual = 20”, “trimSoftClipped = 1”, “minPosterior = 20”, “sbThreshold = 0.01”, “badReadsWindow = 15” and “badReadsThreshold = 15”, at least 10 reads covering a position and 2 reads covering a variant, a minimum genotype quality (GQ) of 20 Phred, genotype likelihood (GL) below − 3, maximum homopolymer run (HP) below 11, minimum variant quality adjusted per read depth (QD) above 2 and minimum median minimum base quality for bases around variant (MMLQ) above 10. Variants labeled by platypus as “HapScore”, “SC”, “strandBias” and “MQ” were discarded. Heterozygous loci with variant allele frequency (VAF) < 35% or > 70% were also discarded.Platypus2  was run Bioconductor [SNPRelate [IGHV status, there were 146 unmutated cases and 273 mutated cases; and by clinical staging, the data contained 47 monoclonal B-cell lymphocytosis (MBL), 332 Binet A, 37 Binet B and 8 Binet C cases. Information about clinical staging and IGHV mutation status was not available for 2 and 7 cases, respectively.We used principal component analysis (PCA) to detect outliers in our study cohort. Similarly, identity-by-descent (IBD) was used to discard all individuals with a degree of relatedness equivalent to third degree or higher. PCAs and IBD data were computed on a linkage disequilibrium (LD) pruned dataset (LD upper threshold of 0.2) using the onductor  package NPRelate . Our finsurvival R package [p-value < 0.2 in a univariate model were selected as covariates for the GWAS. In the cases of TTT these were “donor sex”, “IGHV mutation status” and “Binet stage”; whilst in the case of overall survival we used “IGHV mutation status”, “Binet stage” and “donor age at diagnosis” as covariates. Three association models were computed: an additive model, a dominant model and a recessive model. P-values were adjusted using the Benjamini-Hochberg (BH) method.Cox regression and assumption of proportional hazards was performed with the  package , 18. VarDue to the heterogeneity of exome-seq coverage and quality metrics, many variables had incomplete data. We included in the analysis variables with at least 25% call rate, a minimum of 10 events (progression or death). A minimum allele frequency of 1% was selected as the lowest threshold. Furthermore, we only analyzed polymorphisms where Platypus called at least 10 minor alleles (additive model) or genotypes (dominant and recessive models).QQperm [p-values was created by randomly permuting phenotype variables. Then, the association p-values are compared with the null. This method doesn’t consider the null distribution to be distributed uniformly.Inflation values were estimated with the R package QQperm . BrieflyVEGAS2 [VEGAS2 takes GWAS p-values, and then uses a simulation-based approach using information from population variant reference panels to adjust for LD effects. We used the 1000 Genomes phase 3 data from the iberian population in Spain as our reference population, since all patients of this cohort were of Spanish origin [P-values were adjusted for multiple testing using the BH method.VEGAS2  was usedh origin . Only vaGSEA4GWAS version 1.1 [“Canonical Pathways” and “Gene Ontology Biological Process”. Maximum distance was set to 20 Kb, and the major histocompatibility complex region was masked from the analysis. P-values were adjusted with the BH method.sion 1.1  was usedp-value < 0.05), and other 6 showed association with BH adjusted p-value in the range of 0.05–0.1 , PPP4R2 (2 variants), TTLL12/TSPO, TXNRD2, ZCCHC7, MKI67IP and MARCH10. Notably, rs537453728 at MAP3K4 broke the genome-wide association p-value (4.53 × 10− 8). Other 391 variants were suggestively associated with TTT .We created three models to analyze variant association with time to first treatment: an additive, a dominant and a regressive model. PCA plots . Variants at GPR98, MAP3K4 and TTLL12 were also significantly associated with TTT   and in lymphoblastoid cells . Similarly, the polymorphisms rs361807, rs361946, rs5992169 and rs1043278 in PEX36 are associated with PEX36 expression in lymphoblastoid cells . rs9463 in TTLL12 strongly correlates with the expression of the adjacent gene TSPO , and to a lower extent, with TTLL12  in blood cells. Other polymorphisms suggestively associated with TTT such as those in TXNRD2 and ZCCHC7 are also significantly associated with the expression of their respective genes in blood cells. On the contrary, no functional information exists about the intronic variant rs537453728 within the MAP3K4 gene.Some of this polymorphisms are associated with functional changes in their corresponding genes. According to dbSNP , rs22478database  points tp-values below 0.05 in any model . Overall, the number of variants with BH adjusted p-values below 0.1 was 25 in the additive model, 4 in the dominant model and 18 in the recessive model.We created an additive, a dominant and a recessive model to investigate variant association with OS. No significant inflation was observed . No variant achieved BH-adjusted p-values for the TTT model using VEGAS2 evidenced the association of 5 genes with BH adjusted p-values < 0.05 and 10 genes with BH adjusted p-values in the range of 0.05–0.1  were “Pentose Phosphate pathway”, “Gluconeogenesis”, “Glycolysis”, “GNα13 pathway”, “Nitric Oxide pathway”, “Apoptosis”, “Glycolysis and Gluconeogenesis”, “Tumor Necrosis Factor pathway”, “Ovarian Infertility genes”, “Bile Acid Biosynthesis”, “Keratinocyte pathway” and “Glycine Serine and Threonine pathway” , such as “Notch Signalling pathway”, “EGF pathway”, “JNK MAPK pathway” and “PDGF pathway” among others. Among “GO Biological Processes” terms, the following were associated with OS with a BH-adjusted p-value < 0.05: “Induction of Apoptosis by Extracellular Signals”, “Mitosis”, “M phase”, “M phase of mitotic cell cycle” and “Negative regulation of Developmental Process”. Terms with BH-adjusted p-value < 0.25 were “Anti Apoptosis”, “Chromosome Segregation”, “Vasculature Development” and “Negative Regulation of Apoptosis”, among others , which regulates DNA repair through non-homologous end joining [MAP3K4, PEX26 and TTLL12. MAP3K4 participates in the TRAIL/MAP3K4/p38/HSP27/Akt pathway, thereby modulating processes such as autophagy and cell migration. Indeed, MAP3K4 is affected by recurrent loss-of-function mutations in different types of cancers [PEX26 [GPR98; and TTLL12, which participates in chromosome stability and mitosis-related processes [TTC32/WDR35 and CLIP1 loci, the last of which is overexpressed in Reed-Sternberg cells of Hodgkin lymphoma [In this work we present evidence that suggest the existence of germline variation modulating CLL’s clinical aggressivity. The most remarkable finding was the strong and recessive association of rs7620924 near  joining . Concord joining ; and its joining . Other p cancers –32. Convs [PEX26 ; the G-processes . On the lymphoma .RIP3K with both time to treatment and overall survival. RIP3K encodes a protein that regulates necroptosis, a form of regulated cell death characterized by cell membrane permeabilization [NIFK [SIK1 [ZCCHC7, which is encoded near the B-cell specific PAX5 super-enhancer locus [CLUAP1, which participates in tumor growth and cytoskeleton regulation [GAMT, which converts S-adenosylmethionine to creatine in order to foster high energy demands [BNIPL [GNA13 have been described in B cell lymphomas [In a similar fashion, we detected variation on different genes associated with CLL evolution. The most relevant was the association of lization . Other ron [NIFK , the tumFK [SIK1 , 40 and er locus , 42. On gulation –45; and  demands . Moreoves [BNIPL , 48 was s [BNIPL . Other pymphomas –53, and ymphomas , 55.The main limitation of this study is the lack of an independent cohort for validation of these findings. Furthermore, although inflation values were low, we assume that treatment heterogeneity could have an impact on overall survival associations. Nevertheless, the global results are not only statistical significant but also biologically plausible. Thus, we believe that this report will motivate further studies in order to confirm the effect of these variants and to determine their mechanisms of action in lymphoproliferative disorders.Our results point towards the existence of germline variability as a determinant of CLL clinical aggressivity. Future studies to validate and characterize the activity of these variants in CLL are needed.Additional file 1:Figure S1. Principal component plots for the subjects included in the final analysis. (JPG 154 kb)Additional file 2:Figure S2. Manhattan plot of the additive OS model results. (JPG 199 kb)Additional file 3:Figure S3. Manhattan plot of the dominant OS model results. (JPG 222 kb)Additional file 4:Figure S4. Manhattan plot of the recessive TTT model results. (JPG 193 kb)Additional file 5:Figure S5. Manhattan plot for the Pentose Phosphate pathway. (JPG 56 kb)Additional file 6:Figure S6. Manhattan plot for the GNα13 pathway. (JPG 54 kb)Additional file 7:Figure S7. Manhattan plot for the “Induction of apoptosis by extracellular signal” biological process. (JPG 59 kb)Additional file 8:Figure S8. Manhattan plot for the “Mitosis” biological process. (JPG 54 kb)Additional file 9:Table S1. GWAS results of the additive Cox regression model for TTT. Polymorphisms with a BH-adjusted P-value < 0.5 are shown. Table S2. GWAS results of the dominant Cox regression model for TTT. Polymorphisms with a BH-adjusted P-value < 0.5 are shown. Table S3. GWAS results of the recessive Cox regression model for TTT. Polymorphisms with a BH-adjusted P-value < 0.5 are shown. Table S4. GWAS results of the additive Cox regression model for OS. Polymorphisms with a BH-adjusted P-value < 0.5 are shown. Table S5. GWAS results of the dominant Cox regression model for OS. Polymorphisms with a BH-adjusted P-value < 0.5 are shown. Table S6. GWAS results of the recessive Cox regression model for OS. Polymorphisms with a BH-adjusted P-value < 0.5 are shown. Table S7.VEGAS2 gene-level results for association with TTT. Table S8.VEGAS2 gene-level results for association with OS. (XLSX 3081 kb)


Participants (N = 7058) were randomly allocated to one of the 36 groups. The primary outcome was acceptability of the policy. Acceptability differed across policy, behaviour and evidence communication . Labelling was the most acceptable policy (supported by 78%) and Availability the least (47%). Tobacco use was the most acceptable behaviour to be targeted by policies (73%) compared with policies targeting Alcohol (55%) and Food (54%). Relative to the control group (60%), asserting evidence of effectiveness increased acceptability (63%); adding a quantification to this assertion did not significantly increase this further (65%). Public acceptability for nudges and taxes to improve population health varies with the behaviour targeted and the type of intervention but is generally favourable. Communicating that these policies are effective can increase support by a small but significant amount, suggesting that highlighting effectiveness could contribute to mobilising public demand for policies. While uncertainty remains about the strength of public support needed, this may help overcome political inertia and enable action on behaviours that damage population and planetary health.There is growing evidence for the effectiveness of choice architecture or ‘nudge’ interventions to change a range of behaviours including the consumption of alcohol, tobacco and food. Public acceptability is key to implementing these and other interventions. However, few studies have assessed public acceptability of these interventions, including the extent to which acceptability varies with the type of intervention, the target behaviour and with evidence of intervention effectiveness. These were assessed in an online study using a between-participants full factorial design with three factors: Policy (availability  •Public acceptability varies across nudges and taxes and behaviour targeted.•Labelling was the most acceptable policy, then Size, Tax, and Availability.•Targeting tobacco use was more acceptable than targeting alcohol or food.•Asserting effectiveness increased acceptability; quantifying it had no added impact.•The more participants engage in the targeted behaviour, the lower their support. Such bei.e. how individuals feel and think about the implementation or continued existence of policies , targeting three sets of behaviour  to improve health outcomes. A further aim is to test whether communicating evidence of policy effectiveness  increases the public acceptability of these policies across these behaviours.2The study was preregistered with the Open Science Framework .2.1www.yougov.co.uk) recruited 7058 participants from their online panel representative of the English population based on age, gender, socioeconomic status, region, and education. This sample size provided 80% power to explore the 3-way interaction term (policy x behaviour x effectiveness communication) with α = 0.05 and an effect size of f = 0.05. Data were collected between 9th and 16th October 2018.A research agency  x Behaviour  x Evidence communication (no message vs assertion of policy effectiveness vs assertion and quantification of policy effectiveness). Participants were randomly allocated to one of the 36 groups, using the research agency's software.An online study using a between-participants full factorial design with three factors resulting in 36 groups: Policy ; ii. a reduction in the size of the product (Size); iii. a graphic warning label on the product (Labelling), and iv. a tax to increase the price of the product (Tax).The four policies were: i. Alcohol, ii. Tobacco, and iii. Food . Descriptions of all twelve policies - four for each of the three behaviours - are presented in Box 1Alt-text: Box 1The three behaviours that were targeted were the use or consumption of: i. no message (control group) ii. a message that asserted that the policy was effective at changing the targeted behaviour, without any description of the magnitude of the effectiveness, and iii. a message that asserted that the policy was effective and quantified the magnitude of the effectiveness.The three evidence communication messages were i.e. a graphic warning label on cigarette packs. To address this the wording used for all groups was “… a new graphic warning label …”.Within the asserting and quantifying condition, the effectiveness was expressed as a 10% reduction for all behaviours and policies to ensure consistency and comparability across the groups. This size of effect was selected as it is credible across conditions given existing evidence. For example, Cochrane reviews suggest that energy labelling on menus in restaurants could reduce energy purchased per meal by 7.8% , that smBox 2Alt-text: Box 2Text provided to three of the 36 groups is presented in 2.4Primary outcome: Acceptability of the policy was assessed using three items (α = 0.97) ( = 0.97) : “How acSecondary outcome: The perceived effectiveness of the policy was measured with two items (α = 0.90)  : ”The neDemographic measures: The research agency provided participant demographic data including age, gender, socio-economic status, education, and region. Educational achievement was recoded into three categories: low education ; medium education ; and, high education (degree awards or higher). Socio-economic status was also recoded into three categories: low (DE), medium (C1C2), and high (AB). Indices of Multiple Deprivation (IMD) (on (IMD)  were calBehaviour and BMI: Weekly alcohol consumption in units was measured with two items (α = 0.91) on a six-point scale . Daily tobacco use (α = 0.96) and daily vaping (α = 0.95) were measured with two items on a five-point scale . High-calorie snacking was assessed with three items (α = 0.97) on an eight-point scale  (4 a day) . For an 2.5The main analyses used full factorial ANOVAs to test the main effects and interactions between policy, behaviour, and evidence communication on acceptability and perceived effectiveness. Bonferroni adjusted pairwise comparisons were used to indicate if acceptability and perceived effectiveness significantly differed across the levels of each factor.α = 0.05/7 = 0.007. The model diagnostics were all acceptable.The predictors of acceptability were analysed in seven regression models (three behaviours and four policies). The ordinal variables that assessed frequency of consumption were not used in the regression analyses as few people reported smoking or vaping. These ordinal variables were replaced by dichotomous variables indicating whether or not participants currently smoked or vaped. A Bonferroni adjustment was applied to the seven regression models, Potential confounding variables  were judged to be matched across groups using a percentage method to assess chance imbalances following the randomisation . The larTo aid interpretation of the results, data in 33.13.1.1i.e. for those who did not receive any evidence of effectiveness for the different interventions. This is to provide baseline data, from participants not influenced by evidence of policy effectiveness.A majority of participants found nudges and taxes to be acceptable overall . These dPolicies that limited the availability of a product were the least acceptable, and the use of graphic labels on products were the most acceptable.F = 124.57, p < .001, partial η2 = 0.045. Acceptability varied significantly across the four policy types, 3.1.2Policies that targeted tobacco use were the most acceptable. Policies that targeted alcohol use and high-calorie snack food consumption were less acceptable, and comparably acceptable.F = 201.93, p < .001, partial η2 = 0.048  = 9.24, p < .001, partial η2 = 0.002  of communicating evidence was small.Relative to the control group, asserting evidence of effectiveness increased acceptability; adding a quantification to this assertion did not significantly increase this further. There was a significant main effect of communicating evidence of policy effectiveness on acceptability, .002 see . In comp3.1.4F = 12.97, p < .001, partial η2 = 0.010. Exploration of this interaction using plots  = 0.77, p = .544, partial η2 = 0.000, or between evidence communication and policy type on acceptability, F = 1.40, p = .213, partial η2 = 0.001. The three-way interaction between evidence communication, behaviour and policy type on acceptability was not significant, F = 0.26, p = .995, partial η2 = 0.000.There were no significant two-way interactions between behaviour and evidence communication on acceptability, 3.2F = 36.54, p < .001, partial η2 = 0.014, and across the three behaviours, F = 121.18, p < .001, partial η2 = 0.030. There was also a significant main effect of communicating evidence of policy effectiveness on beliefs about the effectiveness of the policy, F = 21.76, p < .001, partial η2 = 0.005. Beliefs about policy effectiveness varied significantly across the policies, F = 15.69, p < .001, partial η2 = 0.012. Exploration of this interaction using plots  = 1.53, p = .190, partial η2 = 0.001, or between evidence communication and policy type on beliefs about the effectiveness of the policy, F = 1.21, p = .299, partial η2 = 0.001. The three-way interaction between evidence communication, behaviour and policy type on beliefs about the effectiveness of the policy was not significant, F = 1.24, p = .249, partial η2 = 0.002.There were no significant two-way interactions between behaviour and evidence communication on beliefs about the effectiveness of the policy, 3.3B = 0.25, p = .006, and participants living in the most deprived areas had less support for policies targeting unhealthy snack foods  or vaped  judged policies that aimed to reduce tobacco use less acceptable. People who ate more high-calorie snacks were less accepting of policies to reduce high-calorie snacking, B = −0.02, p = .006, and people who drank more alcohol were less accepting of policies to reduce alcohol use, B = −0.02, p < .001.The perceived effectiveness of a policy was the strongest predictor of its acceptability across all seven regression models  and quantifying effectiveness . Quantifying the effectiveness did not clearly confer any additional benefit over simply asserting that the policy was effective. There was no difference in perceptions of policy effectiveness or support for a policy between these two groups. There are several possible explanations for these findings. First, people may be insensitive to numbers indicating magnitude of an effect. Evidence against this explanation comes from a discrete choice experiment which found acceptability of interventions was sensitive to numbers denoting magnitude of effects . Second,The regression models suggest that people who engage in a behaviour reliably oppose policies to reduce that behaviour. Tobacco users and vapers were less accepting of policies targeting tobacco use, and acceptability of policies to targeting both alcohol use and snack foods decreased as weekly use of the respective product increased. These regression analyses also show that women were more likely to support the policies than were men, consistent with previous studies . DiepeveSocio-economic status, as assessed by educational achievement, social grade, and the Index of Multiple Deprivation had little association with acceptability. One exception to this was the acceptability of policies to change the size of products: those with high levels of education supported these policies more so than those with low levels of education. A second exception to this was stronger support for policies to reduce unhealthy snack consumption among those living in less deprived areas . It is not clear why these two results were significant, however the main pattern of results in the current study – an absence of an association between socio-economic status and acceptability of policies to change behaviour to improve health – is reported in other studies . In a reA strength of the current study is the full between-participants design. This ensures that each estimate of acceptability is not influenced by prior exposure to questions about the acceptability of other policies, and participants are also less likely to be affected by fatigue effects. A limitation of this design is that, despite the large overall sample size, the sample size per cell is smaller than in some similar studies and therefore each estimate has larger confidence intervals . A furthThe current study estimated acceptability in England only and although some countries have similar attitudes toward nudges - such as France or the USA - others do differ . Future In conclusion, the majority of the study sample perceived nudging and taxing to be acceptable for changing unhealthy behaviours to improve population health. There was, however, considerable variation by policy and behaviour. All four policies targeting tobacco, including banning tobacco sales in corner shops, received majority support. Three policies targeting snacking and two targeting alcohol consumption received majority support. This highlights that there is already public will for stronger government action to improve population health. Furthermore, communicating evidence of their effectiveness increased support for policies that can reduce overconsumption of alcohol, cigarettes, and unhealthy foods. This suggests that highlighting effectiveness could contribute to mobilising public demand for policies. While uncertainty remains about the strength of public support needed, this may help overcome political inertia and enable action on behaviours that damage population and planetary health.


Perception of the time interval between one’s own action (a finger tapping) and the associated sensory feedback  is critical for precise and flexible control of action and behavioral decision. Previous studies have examined temporal averaging for multiple time intervals and its role for perceptual organization and crossmodal integration. In the present study, we extended the temporal averaging from sensory stimuli to the coupling of action and its sensory feedback. We investigated whether and how temporal averaging could be achieved with respect to the multiple intervals in a sequence of action-sensory feedback events, and hence affect the subsequent timing behavior. In unimodal task, participants voluntarily tapped their index finger at a constant pace while receiving auditory feedback (beeps) with varied intervals as well as variances throughout the sequence. In crossmodal task, for a given sequence, each tap was accompanied randomly with either visual flash or auditory beep as sensory feedback. When the sequence was over, observers produced a subsequent tap with either auditory or visual stimulus, which enclose a probe interval. In both tasks, participants were required to make a two alternative forced choice (2AFC), to indicate whether the target interval is shorter or longer than the mean interval between taps and their associated sensory events in the preceding sequence. In both scenarios, participants’ judgments of the probe interval suggested that they had internalized the mean interval associated with specific bindings of action and sensation, showing a robust temporal averaging process for the interval between action and sensation. Time perception upon the interval between one’s action and its sensory feedback , i.e., sensorimotor timing, is critical for daily perception, behavioral decision and even human living . Two proIn a typical sensorimotor synchronization task, observers are usually tapping according to the pacing signals with regular rhythm. However, it is often the case that the pacing rhythm is not regular, wherein observers have to calculate the “mean” rhythm  for making the subsequent prompted action decision and execution, whether by adopting the temporal estimation or (re)production tasks. The ability to extract the average time interval information in the action-sensory feedback sequence demonstrates the individual timing sensitivity  and help us adapt to the environmental changes . The comThe perception of the time interval between an action and its sensory feedback, in which the perception of time will be biased to the concurrent actions, is different to the perception of time intervals within pure sensory events. A recent study showed that motor timing during rhythmic tapping influences the visual timing. Temporal averaging entails the empirical inquiries with regards to the distribution of irregular  time intervals , selectiIn current study, we examine the mechanisms of temporal averaging of the time intervals between action and its sensory feedback . Specifically, we investigated how the mean and irregularity (variances) in the distribution of time intervals affect the perception of target interval in the loop of action and its sensory feedback. Secondly, we examined how human observers can selectively average the sensory-specific time intervals in two sequences in which the actions were bound with either visual flashes or auditory beeps . Lastly,We implemented four experiments to address these issues. In Experiment 1, we examined the ability of extracting the mean interval from a sound sequence and replicated the central tendency effect. In Experiments 2 and 3, we studied the selective temporal averaging in which the actions were bound with two types of events: beeps of two types of pitches, or two types of sensory stimuli . In Experiment 2, we investigated whether observers could selectively separate the different mean action-auditory feedback intervals and hence make the comparisons between the produced interval and the preceding duration-specific mean auditory intervals. To examine whether the ability of temporal averaging is dependent on the individual modalities (events) or not, in Experiment 3, we used both auditory beeps and visual flashes as sensory feedbacks and examined the selectivity of temporal assimilations to either short or long mean intervals . By averaging, human observers could take both the mean interval information and the variance of the intervals into account . In ExpeAuditory stimuli in a sound sequence were pure tones , with 65 dB SPL. Two pure tones of 2000 Hz were used as cueing signals. The starting cue (duration of 500 ms) prompted the beginning of a trial. The testing cue  indicated the coming of the probe interval for discrimination (see the following procedure for more details).2 in luminance) appearing at the center of the screen, with a gray background (16.8 cd/m2 in luminance), presented on a 27-inch screen . The viewing distance from the participants to the center of the monitor was 60 cm. Auditory stimuli were delivered through NIVIDIA High Definition Audio. Participants wore headset of Sennheiser Momentum 2 to receive the sounds. We used RTBox v6  to collect responses. The experimental program was written with Matlab (Mathworks Inc.) and the Psychophysics Toolbox  was 800 ms. The eight sequential intervals were in the time range of 600 to 1000 ms, and were drawn from a Gaussian distribution of N. Using customized codes, we composed each trial(sequence) to ensure the coefficient of variance  of all intervals was between 0.1 and 0.15, thus to largely randomize the temporal information as well as within the human observers’ perceptual expertise to perform the tasks. In Experiment 2, two mean intervals were used. The short interval (mean of 400 ms) was associated with low-pitch tone (500 Hz) and the long interval (mean of 800 ms) was associated with high-pitch tone (1000 Hz). The short sequential intervals were in the range from 200 to 600 ms, and were drawn from a Gaussian distribution of N. The CV of the intervals was between 0.1 and 0.15. The mapping between tone pitch and mean interval was reversed in the other condition. In Experiment 3, the similar configurations were used as in Experiment 2 except that both auditory and visual feedbacks were used. In Experiment 4, we designed two types of tap-tone sequences in which the mean tap-tone interval was kept at 800 ms. However, for one sequence, the taps were followed with tones (500 Hz) with low CV (between 0.1 and 0.15) of the intervals. For the other sequence, the taps were associated with tones with high-pitch tones (1000 Hz) and with high CV (between 0.3 and 0.35). The CVs were determined by previous evidence so that in this range human observers could well perform the relevant tasks ,b. For aIn the formal experiments, the preceding sequence contained two different intermixed durations, with the two different durations each cued by different pitches or by different sensory events . Under this context, people can extract and maintain a standard for each duration. The two standards might interact and may interfere a bit in memory references. To examine whether there are perceptual shifts and response biases due to the mixing of the two sequences (standards), we further implemented control tests with the same tasks as in formal experiments, but obtained the baseline data for mean 400 and 800 ms interval conditions from another groups of participants.The experiments were performed in compliance with the institutional guidelines set by the Academic Affairs Committee, School of Psychological and Cognitive Sciences, Peking University. The protocol was approved by the Committee for Protecting Human and Animal Subjects, School of Psychological and Cognitive Sciences, Peking University. All participants gave written informed consent in accordance with the Declaration of Helsinki, and were paid for their time on a basis of 40 CNY/hour, i.e., 6.3 United States dollars/hour.In a preceding action-sensation sequence, participants did voluntary taps that triggered either auditory beeps or visual flashes as sensory feedbacks. This loop with multiple tap-sensation intervals  served as a temporal reference for the subsequent comparison of target interval (in a single action-sensation loop). The target interval was defined by a tap with its associated sensory feedback . The target interval was 200, 400, 600, 800, 1000, 1200, or 1400 ms for the long mean duration (800 ms) condition and 100, 200, 300, 400, 500, 600, or 700 ms for the short mean duration (400 ms) condition. A typical trial started with a black fixation (“cross” on the monitor screen) which appeared 500 ms before the first signaling tone and lasted until the second cueing tone was over. The first cueing beep  indicated the start of the action-sensory feedback sequence and prompted the participants to issue the tappings within 3 s. The tap was accompanied with either visual flash or auditory beep, with the repetition of eight action-sensation intervals (mean 400 ms or 800 ms). When the last sensation feedback was over, after a blank interval of 300 ms, participants heard a 2000 Hz beep (200 ms) which indicated the issuing of a last tap for generating target interval  . We usedThirteen participants  took part in experiment 1. In Experiment 1, we used 500 Hz tones as sensory feedbacks for participants’ voluntary taps. Participants consecutively tapped eight times first, in which each tap was followed by a 500 Hz auditory beep as sensory feedback. The time intervals between action and sensory feedback were not equal . The target interval was 200, 400, 600, 800, 1000, 1200, or 1400 ms. Participants took two blocks of tests, each block having seven trials for each given target interval. Participants received 14 trials, twice for each target interval, to get familiar with the task.The data from Experiment 1 served as a subset of baseline data, in which only one type of auditory signals were used. Three further control experiments were implemented to provide baseline data in which only a single type of stimuli was presented eight times, i.e., 500 Hz tones with short intervals, visual flashes with long intervals (mean 800 ms) and visual flashes with short intervals (mean 400 ms). The control experiments were modified after Experiment 1. In addition to the specific mappings of sensory feedbacks and intervals, in each control experiment participants received practices  until their accuracies were above 75%. The number of practice blocks were identical to the formal experiments. Thirteen participants  took parts in control experiment (CE1). In CE1 (baseline corresponding to Experiment 2 and Experiment 3), sensory feedbacks were 500 Hz auditory beeps, but the mean tap-beep interval was 400 ms. Thirteen participants  attended in CE2. In CE2 (baseline for Experiment 3), we used visual flashes as sensory feedbacks to associate with the taps. The mean tap-flash interval was 800 ms. Thirteen participants  attended in CE3. In CE3 (baseline for Experiment 3), the tap-visual flash sequence was adopted with the mean tap-flash interval of 400 ms. For all the control experiments, after the preceding sequence was over, the probe interval was given and was always demarcated with the sensory event of the same properties as shown in the sequence. The probe interval was 200, 400, 600, 800, 1000, 1200, or 1400 ms for the long mean duration (800 ms) condition, and 100, 200, 300, 400, 500, 600, or 700 ms for the short mean duration (400 ms) condition.Seventeen participants  took part in Experiment 2. We used two kinds of auditory feedbacks (500 or 1000 Hz) and two sets of tap-sensation intervals . In one condition, short intervals were marked by 500 Hz tones and long intervals were marked by 1000 Hz tones. Nine participants took the test in this condition. In the other condition, eight participants joined the test in which the associations between intervals and tones were reversed . In a tap-sensation sequence, the short and long intervals were mixed. Participants issued eight taps in which the ratio of the short to long intervals was selected from one of the given sets . Participants were prompted to compare the target interval with the preceding mean interval of action-sensory feedbacks in four blocks, in which both the target interval and the preceding intervals between action and sensation were marked by the tones with the same pitches. In each block, one target interval (from seven levels) was presented four times. Prior to formal experiment, participants received two tasks for practice. In the first task, they received the practice with both short and long mean intervals (but in one sequence only either 500 or 1000 Hz tones were given). Each target interval was presented three times, resulting in 42 trials. Participants could take another session for practice until their accuracies were above 75%. In the second task, they received another 14 trials . Both practice tasks were implemented with visual feedback of “correct” or “wrong” responses. When the practice session was over, participants took the formal test.Sixteen participants  took part in Experiment 3. The stimuli configurations and timing parameters were similar to those in Experiment 2, except that the 1000 Hz tones were replaced by visual black disks as sensory feedback. The practice protocol was the same as the one in Experiment 2.Twelve participants  took part in Experiment 4. The stimuli setting and timing parameters were similar to those in Experiment 2, except that the two sets of action-sensation intervals were same (mean 800 ms) but with different CVs. In one configuration, the intervals marked with 500 Hz tones were associated with CVs of 0.1 to 0.15 , and those intervals marked with 1000 Hz were associated with CVs of 0.3 to 0.35 . In the other configuration, the mappings between tone pitches and CVs were reversed. Prior to the formal experiment, participants took 14-trial practice with feedback of “correct” or “wrong” responses as did in Experiment 2.In all four experiments, the proportions of reporting the target duration as longer across seven intervals were fitted to the psychometric curve using a logistic function . The trat-test showed that participants underestimated the target interval, compared with 800 ms, t(12) = 10.368, p < 0.001 (The mean PSE and JND were 869.3 ± 24.1 ms (standard deviation) and 194.4 ± 29.4 ms. All the mean PSEs and JNDs were ploted in  < 0.001 , left.ps < 0.001), which suggested at least one of the predictor variables was statistically significant in contributing the discrimination of probe interval. The results of Hosmer and Lemeshow Tests of models were not significant (ps > 0.143), implying good fitness of the models. We then implemented one-sample t-tests comparing parameter estimates of the eight sequential intervals of all participants with “0.” None of these sequential intervals reached significant level (ps > 0.521). Finally, a repeated-measure ANOVA test was implemented with positions of sequential intervals as within-subject variables on parameter estimates of sequential intervals of all participants. The difference between sequential intervals was partially significant  and the effect of intercept was not significant . The detailed values were given in To evaluate whether certain intervals in the action-sensation sequences play a significant role in determining the estimation of the probe interval, e.g., the potential recency effect stemming from the last interval , we perft-test revealed a significant bias of perceived “compression” of the probe intervals (compared with the reference of 400 ms) . Binary logistic regression, the same as in Exp1 was applied. Ominibus Tests of Model Coefficients of all models reached significant level (ps < 0.001). The results of Hosmer and Lemeshow Tests of models were not significant (ps > 0.196) for eleven participants except for two participants (which means their models were not good fitted). Thus we implemented one-sample t-tests with the two participants excluded. None of these sequential intervals reached significant level (ps > 0.055). The repeated measures ANOVA test revealed a partially significant effect of intercept  but no significant effect of sequential intervals .In this separate control experiment with 500 Hz auditory beeps and short mean durations, the mean PSE and JND were 470.8 ± 19.5 ms and 119.1 ± 24.5 ms. One sample t-test of this condition showed participants’ tendency of “compressing” probe intervals as above  (ps < 0.001) and Hosmer and Lemeshow Tests of models were not significant [ps > 0.579] for the binary logistic regression. One-sample t-tests showed that none of the effects of these sequential intervals were significant (ps > 0.345). Both the effects of sequential intervals  and intercept  were not significant by repeated-measure ANOVA test.The mean PSE and JND of the control experiment with visual flashes and long mean duration (800 ms) were 832.7 ± 27.6 and 138.2 ± 7.5 ms. One sample = 0.001] . Ominibut(12) = 5.128, p < 0.001]. For binary logistic regressions, Ominibus Tests of Model reached significant (ps < 0.001) and Hosmer and Lemeshow Tests of models were not significant (ps > 0.364). One-sample t-tests showed none of these sequential intervals was significant in contributing the perceived probe intervals (ps > 0.277). The repeated-measure ANOVA test showed neither effect of sequential intervals , nor effect of intercept .For the control experiment with visual flashes and short mean duration (400 ms), the mean PSE and JND were 418.5 ± 13.0 and 75.7 ± 10.3 ms. Participants had biases to “compress” the probe intervals  and a significant main effect of mean duration . The interaction of modality and mean duration was not significant . To be more specific, PSEs and JNDs of auditory modality were significantly larger than those in visual modality. PSEs and JNDs in short mean duration condition was significantly smaller than those in long mean duration condition.A 2 × 2 ANOVA test that took modality  and mean duration (short/long) as between-subject factors showed, for both PSEs and JNDs, a significant main effect of modality  but interval means had a significant main effect on PSEs . For JNDs, context also did not make a difference . However, the main effect of the mean interval duration was significant . This result pattern indicated that participants had selectively extracted different “mean” intervals to make prompt perceptual decision of the probe intervals. Therefore, we collapsed the data across two types of tone pitches for further analysis. The mean PSEs for short and long mean durations (across both pitches) were 464.9 ± 65.9 and 766.7 ± 86.7 ms. The mean JNDs for short and long mean durations were 101.5 ± 43.6 and 145.0 ± 53.6 ms.We performed a repeated measures analysis of variance (ANOVA) test with context from different matchings between tones  and means of intervals  as between-subject variable, and means of sequential intervals as within-subject variable. There was no significant main effect of context . The context also didn’t make a difference on JNDs .We implemented one-way ANOVA to compare the collapsed data and data from corresponding control experiments, i.e., Experiment 2 and CE1. For short mean duration condition, we did one-way ANOVA with context as between-subject variable. The context included three conditons: 500 Hz with short mean duration control , 500 Hz with short mean duration stimuli in the context of 1000 Hz long mean duration stimuli (“500 Hz–400 ms and 1000 Hz–800 ms”), 1000 Hz with short mean duration stimuli in the context of 500 Hz long mean duration stimuli (“500 Hz–800 ms and 1000 Hz–400 ms”). The effect of context was not significant on PSEs . PSEs of “500Hz–800 ms” control was significantly larger than both PSEs of “500 Hz–800 ms” in “1000 Hz–400 ms” context (p = 0.015) and PSEs of “1000 Hz–800 ms” in “500 Hz–400 ms” context (p = 0.009). Also, there was a significant main effect of context on JNDs . However, JND of “500 Hz–800 ms” in “1000 Hz–400 ms” context were marginally significantly different from JND of “500 Hz–800 ms” control [p = 0.061]. JND of “1000 Hz–800 ms” in “500 Hz–400 ms” context was the same as the JND of “500 Hz–800 ms” control (p = 0.110).For long mean duration condition, the same one-way ANOVA test was implemented. The results showed that the context had a significant effect on PSEs  but the effect of intercept was significant . This result pattern indicated that with two standards of references (sequences), participants could have some initial preferences responding to the specific sequence (short vs. long). Moreover, with the increasing complexity of stimuli, participants depended more on the recent intervals to make perceptual decision for the probe interval.Binary logistic regressions analysis was applied to Experiment 2 as in Experiment 1. For all participants, results of Ominibus Tests of Model Coefficients reached significant level  = 0.414, p = 0.530, η2 = 0.029; JND: F = 0.360, p = 0.558, η2 = 0.025]. However, the main effect of the mean intervals was significant on PSEs  and JNDs . Therefore, we collapsed the data across stimuli types . The mean PSEs for short and long mean interval conditions were 447.8 ± 75.2 and 774.3 ± 88.0 ms. The mean JNDs for short and long mean interval conditions were 95.4 ± 44.7 and 125.5 ± 72.3 ms (The mean PSE and mean JND of “A(uditory) – 400 ms” in “V – 800 ms” context were 456.2 ± 64.2 and 86.9 ± 47.8 ms. The mean PSE and mean JND of “V – 400 ms in A – 800 ms” context were 439.5 ± 88.5 and 104.0 ± 42.7 ms. The mean PSE and mean JND of “V – 800 ms” in “A – 400 ms” context were 784.3 ± 108.3 and 117.1 ± 77.6 ms. The mean PSE and mean JND of “A – 800 ms” in “V – 400ms” context were 764.3 ± 68.0 and 133.9 ± 70.9 ms . A repea 72.3 ms .F = 2.434, p = 0.127, η2 = 0.060]. The modality of sensory feedbacks had a significant effect  but the context didn’t have such a significant effect . The PSEs of “A – 400 ms” in “V – 800 ms” context in Exp3 were not different from PSEs of “A – 400 ms” in CE1 (p = 0.225). The PSEs of “V – 400 ms” in “A – 800 ms” context were the same as PSEs of “V – 400 ms” in CE3 [p = 0.336]. For JNDs, there was a significant effect of modality × context . The results also revealed a significant effect of modality  but not of context . The JNDs of “A – 400 ms” in “V – 800 ms” context in Exp3 were smaller than JNDs of “A – 400 ms” in CE1 (p = 0.003) and the JNDs of “V – 400 ms” in “A – 800 ms”in Exp3 were, however, larger than JNDs of “V – 400 ms” in CE3 (p = 0.040).As above, we implemented a two-way ANOVA test on the collapsed data and corresponding control data for short mean duration condition, with modality of feedbacks  and context (context of 500 Hz–400 ms control/context of Exp 3) as between-subject variables. For PSEs, there was no significant interaction effect of modality × context . The modality made no difference for PSEs . But context had a significant effect on PSEs . The PSEs of “A – 800 ms” in “V – 400 ms” context in Exp3 were significantly larger than PSEs of “A – 800 ms” context in Exp1 (p = 0.010) The PSEs of “V – 800 ms” in “A – 400 ms” context in Exp3 were the same as PSEs of “V – 800 ms” context in CE2 (p = 0.100). For JNDs, only modality had a significant effect . There was no significant interaction of modality × context  or effect of context . There were no differences between JNDs of “A – 800 ms” in “V – 400 ms” context in Exp3 and of “A – 800 ms” context in Exp1 (p = 0.116) or between JNDs of “V – 800 ms” in “A – 400 ms” context in Exp3 and the JNDs of “V – 800 ms” context in CE2 (p = 0.365).For long mean duration coditon, the same two-way ANOVA test was implemented. We didn’t find significant interaction effect of modality × context on PSEs [ps < 0.001) but Hosmer and Lemeshow Tests of models were not significant (ps > 0.163). The result showed that seven of eight sequential intervals alone could not predict participants responses [ps > 0.066] but the sixth one contributed to participants’ reponses (p = 0.042). The results of repeated-measure ANOVA test showed no effect of sequential intervals  but a significant effect of intercept .The binary logistic regressions showed good fit for 15 participants: Ominibus Tests of Model Coefficients reached significant level  = 0.988, p = 0.344, η2 = 0.090]. Therefore, we collapsed the data across tone piches as did in Exp2. The mean PSEs for low CV and high CV interval conditions were 900.4 ± 99.1 and 895.8 ± 101.6 ms, and the mean JNDs under the two CVs were 165.0 ± 68.1 and 175.6 ± 87.9 ms.We implemented a two-way repeated measures ANOVA test to examine whether various mappings of tone pitches (500 Hz vs. 1000 Hz) and CVs (0.1–0.15 vs. 0.3–0.35) made a difference. The results indicated that orthogonal mappings did not make a difference  or on JNDs . Again, binary logistic regressions for all participants showed that Ominibus Tests of Model reached significant level (ps < 0.001) and Hosmer and Lemeshow Tests of models were not significant (ps > 0.138). One-sample tests suggested none of these sequential intervals were significant (ps > 0.093). Finally, a repeated- measure ANOVA test was implemented. No differences between sequential intervals were found  and the effect of intercept was not significant .One-way ANOVA test with CV (low/high/control) indicated that there was no significant main effect either on PSEs [In current study we reported that humans are able to use the mean of multiple irregular action-sensation intervals, to compare with the subsequent probe interval which was defined by a single tap and its sensation . However, during this comparison, human observers might use only some of the intervals rather than all of them.This temporal averaging ability has been robustly observed in the loop of action-sensation (sensory feedback) as did in the pure perceptual domian (with a sequence of stimuli) . ImportaThis robust temporal averaging between action and sensation was achieved by a similar mechanism of central tendency effect , in whicAs shown in the literature of timing research, perception of temporal synchrony/asynchrony between one’s own action and the sensory feedback of that action is quite flexible, in which the time order of cause (action) and effect (sensory feedback) could even be reversed due to the repetitious adaptation . This flIn the current study, though the central tendency effect was robustly replicated in the sensorimotor domain, we did not observe a fixed pattern of the potential recency effect, i.e., the potent role of the last interval in action-sensation sequence . And intUsing the mean intervals in action-sensation loop to compare with the subsequent probe interval could be attentional resource- consuming, which constrains the otherwise “advantage” of auditory events . During The control experiments with only one standard (mean duration of 400 or 800 ms), with the comparsion of the corresponding main exepriments, further supported that a robust averaging has been observed, even though there were general biases in which the perceived (mean) time interval was “compressed” with mixed sequences (“standards”) and had been observed obviously with “short” standard. This illusory “compression” of perceived time interval could be elicited by the repetition effect of extended, complex structures of events, which lead to the subjectively “shortened” element interval . AlternaWith that said, we should pay attention to the limitations of current studies. For instance, we did not test empirically how the efficiency of using the mean intervals in sensorimotor domain is constrained by the invidiviual working memory capacity. We are also not informed how the degrees of complexity of the temporal structure (including the more levels of CVs for the durations) would affect the “averaging” processing. Further research evidence is needed to address these considerations.In sum, we revealed a novel and robust temporal averaging process in sensorimotor domain, by employing the action-sensory intervals as building elements in the perception-action sequence. Our findings suggest that human observers can use the mean action-sensation intervals to facilitate and optimize the task-relevant perceptual decision for the subsequent time information in the critial action- sensation loop. The robust averaging of action-sensation intervals suggests that a centralized timing mechanism may subserve this process , though LC designed the study. HZ and LC analyzed the data and wrote the manuscript.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.