Chi-square tests, pairwise comparison and multivariate logistic regression were performed in data analysis. In total, 4039 valid questionnaires were analyzed, with booster hesitancy of 14.81%. Dissatisfaction with previous vaccination experience of primary doses (ORs = 1.771~8.025), less confidence in COVID-19 vaccines , younger age compared to the elderly aged 51–60 years old , lower education level (ORs = 1.707~2.100), weaker awareness of social responsibility of prevention and control of COVID-19 , inconvenience of booster vaccination , complacency regarding vaccine efficacy as well as self-health status  and excessive trade-offs before vaccination  were positively associated with booster hesitancy. Therefore, intelligent means should be strengthened to optimize vaccination services. More influential experts and other significant figures should be supported to promote timely evidence-based information via various media platforms to reduce public hesitancy and increase booster uptake.Following the rollout of a booster campaign to promote immunity against COVID-19 in China, this study aimed to assess booster hesitancy among adults who were fully vaccinated with primary doses across Zhejiang Province. Firstly, the modified 5C scale developed by a German research team was assessed for reliability and validity via a pre-survey in Zhejiang Province. Then, a 30-item questionnaire was established to conduct online and offline surveys during 10 November to 15 December 2021. Demographic characteristics and information on previous vaccination experience, vaccine type of primary doses, attitudes towards booster doses and awareness of SARS-CoV-2 infection were collected.  As of December 2022, over 650 million cases of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection had been recorded, including over 6.65 million deaths globally . The WHOChina began its initial vaccine campaign free of charge in September 2020 with a three-step strategy, firstly covering people at high risk, then expanding to the general adults, and lastly enrolling teenagers under 18 years old and children above 2 years old. As of October 2021, a total of 2.27 billion primary doses  had beenGlobally, Israel , the UniSo far, little is known about the attitudes towards the booster among the public in Zhejiang Province, a developed eastern coastal area of China which had frequent international trade and economic transactions before the pandemic. Thus, this cross-sectional study was primarily conducted to assess COVID-19 vaccine booster hesitancy among the adults in Zhejiang Province using a modified and validated 5C scale developed by a German research team . The secThis phase aimed to establish a modified 5C scale on COVID-19 vaccine booster hesitancy among the Chinese population based on the original version developed by a German research team through interviews with immunization program experts and to test the reliability and validity of the modified version through pilot surveys among convenience samples in Zhejiang Province.Calculation” and “Collective responsibility” into account to further explain vaccination behavior from psychological perspectives, which was perfectly compatible with the globalization of the COVID-19 pandemic. In this scale, “Calculation” refers to individuals’ engagement in extensive information-searching prior to vaccination, such as evaluation of risks of COVID-19 infection, adverse reactions caused by vaccine and benefits of vaccination. “Collective responsibility” is defined as the willingness to protect others by one’s own vaccination to build herd immunity. Low values may indicate that a person is unaware of herd immunity or does not want to vaccinate for the benefit of others . The scale was adjusted from a 7-point to 5-point Likert-type  to make it more concise. The intention to take COVID-19 vaccine boosters was measured by a 5-point Likert-type item . We considered respondents who chose the latter three options  as “vaccine hesitators” according to the WHO definition of vaccine hesitancy; others were categorized as “vaccine acceptors” [Based on the “3Cs” model proposed by the WHO EURO Vaccine Communications Working Group in 2011 , as wellceptors” .Six senior experts who majored in Immunization Programs from provincial, municipal and county-level centers for disease control and prevention (CDC), two public health physicians from community healthcare centers, one professor who majored in Mental Health and Psychiatric Epidemiology at Xiamen University and two translators working for an immunization program with a good bilingual background in both Chinese and English were interviewed face-to-face or online to evaluate the assessment dimension, cultural environment, content relevance and language expression and to make corresponding adjustments to ensure that the modified version was suitable for the actual situation in China. It should be noted that our goal was to explore the impact degree from five dimensions to explain vaccination behavior and to encourage targeted interventions for specific populations to decrease hesitancy. Thus, each dimension was treated as an influencing factor, rather than the total score of the whole 5C scale, due to uncertainty in weight allocation.In order to evaluate the reliability, validity and comprehensibility of the modified 5C scale, a pilot survey was conducted from 29 October to 3 November 2021 in Hangzhou city, the provincial capital of Zhejiang Province. At that time, only adults over 17 years old were approved for eligibility for the booster dose in China. Thus, the general public who had completed primary doses and had not yet received the booster was the target group for this booster hesitancy survey. Convenience samples were recruited, and the responses were anonymous, including adults, college students, university teachers and immunocompromised groups (HIV patients and cancer patients) from hospitals as well as people over 60 years old from communities. Considering network accessibility and education level, the first three types of participants were investigated through Wenjuanxing (WJX), a widely used online questionnaire survey platform, and the last two were interviewed face-to-face to ensure the survey was understood. Individual WJX accounts were limited to a single submission, and all the questions were compulsory. As compensation, participants who finished the questionnaire received a monetary incentive to improve cooperation. The sample size was determined by previous recommendations for sufficient power for scale construction  and the Internal consistency reliability was assessed by calculating Cronbach’s α coefficient on both the whole scale and the 5-dimensional subscales. Considering recommendations from the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) Manual, an alpha value ≥ 0.70 suggests good internal consistency reliability ,50.p < 0.05) were conducted to ensure feasibility of factor analysis. Based on the exploratory factor analysis (EFA) conducted by a German research team [ir) and the corresponding square root of AVE [p < 0.05). DV was estimated to be good if |ir| < The Kaiser–Meyer–Olkin (KMO > 0.6) measure of sampling adequacy and Bartlett’s test of sphericity  ,52. ThreI > 0.9) . DV was t of AVE  if the cThis phase aimed to investigate the COVID-19 vaccine booster hesitancy among those who had completed primary doses and had not yet received the booster shot in Zhejiang Province. In total, 30 questions were included in the formal investigation to explore contributing factors and to provide evidence for targeted interventions to improve booster uptake, consisting of four parts: (1) sociodemographic characteristics; (2) previous vaccination experience and vaccine type of primary COVID-19 doses; (3) the validated and modified 5C scale established in Stage 1; (4) the attitudes towards booster doses and reasons for willingness or hesitancy regarding the booster. This study was deemed exempt by the Ethics Review Committee of Zhejiang Provincial Center for Disease Control and Prevention as no personal identifiers were collected in our questionnaire.Formal investigation was conducted from 10 November to 15 December 2021 in 4 cities  in Zhejiang Province based on the economic development and COVID-19 risks. The source of participants, inclusion criteria and investigation method were consistent with pilot survey described in Stage 1. According to the risk of COVID-19 ,55, mediα = 0.05) using the formula below:P = Hesitancy rate in pilot survey (18.35%) and σ = 0.1 × P. Thus, sample of at least 1893 participants should be recruited considering a loss-to-follow-up rate of 10%.The sample size was calculated based on the hesitancy rate from the pilot survey to achieve 90% power for a 10% difference margin with the statistical parameters  < 5. Data management was conducted using Microsoft Excel 365. Statistical analyses were conducted using IBM SPSS Version 26, IBM SPSS AMOS Version 26 and R Statistical Software Version 4.1.3. p ≤ 0.05 was considered statistically significant.Descriptive analyses were used to evaluate the distributions in scale scores by vaccine hesitancy stages and individual characteristics. Confidence (three items), Complacency (three items), Constraint (three items), Calculation (three items) and Collective Responsibility (two items). Each item was assigned scores of 1–5 in sequence according to the degree; for example, a score of 1 = strongly disagree, and a score of 5 = strongly agree. The score for each dimension equals the average score of items included. Three levels, “high”, “middle” and “low”, were defined as average score of the dimension >3, =3, and <3, respectively.After two rounds of expert review and language translation, the modified 5C scale finally included 14 items  with fivCollective Responsibility” subscale.In total, 511 participants were investigated using the modified 5C scale, and 485 valid questionnaires  were retrieved, with the response rate of 94.91%. A total of 89 participants demonstrated COVID-19 vaccine booster hesitancy (18.35%), of whom 68 were not sure (76.40%), 11 refused (12.36%) and 10 strongly refused (11.24%) to get a booster shot. Invalid questionnaires mainly resulted from missing items, mostly due to inability to understand the accurate meaning of “herd immunity” in the “Cronbach’s α coefficients for each subscale and the total modified scale were 0.903, 0.889, 0.897, 0.747, 0.665 and 0.817, indicating good scale reliability.p < 0.001), respectively, indicating that factor analysis was plausible.The KMO measure of sampling adequacy and Bartlett’s test of sphericity for the scale were 0.838 and 3920.712 , and lower scores for “Calculation” and “Collective responsibility” dimensions (p < 0.05). Respondents with a master’s degree showed higher scores for “Complacency” and “Constraint” dimensions (p < 0.01), and those with lower educational level (middle school and below) were less likely to “Calculate” and be “Collective Responsible” (p < 0.01) before getting the booster shot. Medical workers, civil servants and technical personnel and participants who had excellent or good previous vaccination experience of primary COVID-19 doses scored higher for “Confident” and “Collective Responsibility” dimensions (p < 0.05), and significantly lower for “Constraint” and “Complacency” dimensions (p < 0.05). Participants with mediocre experience tended to be less “Calculative” compared to those with a clear inclination (p < 0.05). As to other partial significant factors, male participants scored higher for “Complacency” and “Constraint” dimensions (p < 0.001). Urban participants had higher scores for “Calculation” and “Collective responsibility” dimensions (p < 0.01). Compared to married participants, single respondents scored higher for “Calculation” and “Collective responsibility” dimensions (p < 0.001). Lower annual income  was associated with lower scores for “Confidence” and “Collective responsibility” dimensions (p < 0.001) and higher scores for “Complacency” and “Constraint” dimensions (p < 0.001). High-risk groups were less confident and thoughtful regarding COVID-19 vaccines when compared to the general population (p < 0.001).Significant associations were observed between individual characteristics and scores of subscales . Age, edHosmer–Lemeshow test was conducted and demonstrated a good fit of the logistic model with 2χ = 4.609 (p = 0.798). No collinearity was detected among independent variables with VIF varying between 1.078 and 2.128. Age, education level, previous vaccination experience of primary COVID-19 doses and all the five subdimensions of the modified 5C scale were significant influencing factors associated with booster hesitancy , “Complacency” dimension  and “Calculation” dimension . On the contrary, the “Confidence” dimension had the greatest negative association (lower booster hesitancy) with OR of 0.285 (0.232–0.348), followed by age of 51–60 years old , educational level of master’s degree and above , educational level of bachelor’s degree  and the “Collective Responsibility” dimension . The odds of COVID-19 booster hesitancy among participants who were not sure about the previous vaccination experience of primary COVID-19 doses was 8.025 (5.605–11.586) times the odds of booster hesitancy among participants who were very satisfied. With every point increase in “Confident” dimension score, the odds of being hesitant on the booster decreased by 0.285 (0.232–0.348). However, based on our model results, gender, residence, annual income, marital status, occupation and type of primary COVID-19 doses were not significantly associated with booster hesitancy.A multivariate logistic regression model  was estaesitancy , among wThis cross-sectional survey in Zhejiang Province demonstrated the prevalence of COVID-19 vaccine booster hesitancy during November and December in 2021 using a modified and validated version of the 5C scale based on the “3Cs” model proposed by the WHO and the original 5C scale established by a German research team. A well-fitted multivariate logistic model was conducted to explore influencing factors of booster hesitancy among a large sample of 4039 participants from Zhejiang Province, China.Confidence”, “Complacency”, “Constraint”, “Calculation” and “Collective responsibility”. For “Confidence” and “Collective Responsibility” dimensions, higher levels of agreement indicate lower booster hesitancy, whereas the relationship is reversed for the other three dimensions. With this scale, we found that the COVID-19 vaccine booster hesitancy was 14.81% in Zhejiang Province during November to December 2021, which was roughly consistent with the national investigation in China (16.1%) conducted in late January 2022 with an online sample of 898 [The modified five-point Likert 5C scale used in this study exhibits good reliability and validity, making it available and practical for measuring VH among the Chinese population. In total, 14 items were included, constituting five dimensions: “e of 898 . Other te of 898 ,41 carrie of 898 , both ece of 898 , which mAs a global phenomenon, booster hesitancy changes over time, and effective interventions can be carried out . CompareOur model indicated that most demographic characteristics were not significantly associated with booster hesitancy, including gender, residence, annual income, marital status, occupation and risk level of SARS-CoV-2 infection. However, young adults (18–30 years old) were much more vaccine-hesitant, which was consistent with studies in UK, USA and Japan ,39,61. IConfidence” dimension has the strongest correlation, followed by “Collective responsibility”, “Constraint”, “Complacency” and “Calculation” dimensions. The first two had a positive association with booster acceptance, whereas the remaining three are negatively correlated. Compared to other studies [It is worth mentioning that the five dimensions from the modified 5C scale all provide significant insights into booster hesitancy, making them good psychological predictors. According to the adjusted ORs, the “ studies ,41,42,43 studies ,65 also Although this survey had a large sample with different demographic characteristics across Zhejiang Province through a validated 5C scale to comprehensively evaluate booster hesitancy, it had several limitations: Firstly, this is a cross-sectional survey using convenience sampling with a specific policy background, which could not reflect the dynamic trend of booster hesitancy and was unable to establish causality and generalizability between significantly associated factors and booster hesitancy. Secondly, to improve response rate, identifiable information was not collected, leading to inability to assess test–retest reliability for the modified 5C scale. Thirdly, some lifestyle habits, health behaviors and psychiatric background information, such as smoking and drinking frequency, washing hands, wearing masks, keeping social distance, knowledge and awareness of the COVID-19 pandemic and mental health status, were not measured or evaluated, and these factors might be potential confounders of booster hesitancy. Fourthly, selection bias and recall bias existed due to non-random sampling, non-response and self-reporting. Fifthly, we missed the opportunity to investigate vaccine hesitancy before the first implementation of COVID-19 primary doses in Zhejiang Province, which may provide clues to early targeted interventions through dynamic monitoring. Lastly, considering the COVID-19 pandemic situation in Zhejiang Province, we did not collect previous history of SARS-CoV-2 infection from participants, especially breakthrough infection, which impacted booster hesitancy in other studies . HoweverConfidence” dimension, age, education level and the remaining four dimensions from the modified 5C scale. More intelligent technology should be applied in the process of vaccination services to avoid crowds and long waits. More influential scientific professors and experts from various fields as well as other significant figures  should be supported to promote timely, credible and updated vaccine messages not only via traditional news media  but also from network platforms , so as to comprehensively cover residents of all age groups and educational levels. Regular public opinion monitoring online and offline is a helpful means of increasing public awareness. To summarize, taking advantage of intelligent means to optimize vaccination services, gaining people’s confidence in the vaccine, making the public aware of the social responsibility of preventing and controlling COVID-19, reducing daily vaccination restrictions and underestimation of dangers of SARS-CoV-2 infection and relying more on experts and influential figures through various media platforms are essential in future public health interventions to increase booster acceptance as well as vaccine uptake.This study demonstrated that the COVID-19 vaccine booster hesitancy in Zhejiang Province during November–December 2021 was 14.81% among adults who had completed the primary series. Previous experience of primary doses had the strongest association with booster hesitancy from our multivariate logistic regression model, followed by the “


Wetting highly compliantsurfaces can cause them to deform. Atomicallythin materials, such as graphene, can have exceptionally small bendingrigidities, leading to elasto-capillary lengths of a few nanometers.Using large-scale molecular dynamics (MD), we have studied the wettingand deformation of graphene due to nanometer-sized water droplets,focusing on the wetting angle near the vesicle transition. Recentcontinuum theories for wetting of flexible membranes reproduce ourMD results qualitatively well. However, we find that when the curvatureis large at the triple-phase contact line, the wetting angle increaseswith decreasing tension. This is in contrast to existing macroscopictheories but can be amended by allowing for a variable wetting angle. Y of liquid droplets resting on rigid flat substrates.It can be derived via balancing in-plane surface tensions at the triple-phasecontact line (TCL)2 2 see 1a. It ha8 This requires balancing the tension componentsnormal to the surface. The development and interest in elasto-capillaryphenomena have increased in recent years. Thanks to advances in imagingtechniques, our understanding of elasto-capillary effects in, forinstance, soft bulk matter has matured.11 Recently, it has alsofound applications in coating of polymers, adhesives, and bioinspireddesigns,15 micro- and nanofabrication,17 bending of fibers,18 or nanowire arrays.19 It has further been shown to be beneficial for capillary origami,i.e., wrapping thin solids around droplets21 or bubbles.23The first to shed lighton wetting-induced deformations of thesubstrate, i.e., elasto-capillarity, was probably Lester.L, should be comparable to the elasto-capillarylength. For bulk solids, this length is set by LEC = γ/E, where E isthe substrate Young’s modulus and γ the liquid–vaporsurface tension. This definition has been successfully applied tobiomembranes and soft matter systems.27 However, in thin membranes, LEC can be significantly smaller than the interatomicdistances.30 Despite having a large E, thesemay still bend easily. The relevant length scale is then LBC = (B/γ)1/2, where B is the bending rigidity of the membrane. Physically, LBC can be interpreted in terms of the inversecurvature of the membrane at the TCL24 , one obtains LBC ≈ 2 nm, making graphene an optimalchoice. Imaging of droplets on suspended graphene has already beenreported,31 the most recent with dropletdiameters down to a micron.33 In fact, in ref  variational approach, and Schulmanand Dalnoki-Veress34 showed that in a suspendedmembrane, Y in refs  treatment. Second,it avoids problems arising from contact-line curvature.53 Finally, a 2D model allows us to isolate the effects of tensionand focus on the behavior of the wetting angle, as the Gaussian curvatureis zero. This is in contrast to 3D models, where wrinkling can obscurethe underlying physics. For the graphene–water system, existingMD studies mostly treat graphene to be rigid and flat  provides a uniquetool to probe the TCLon the atomistic scale.cf. refs ,50 and  than what is common in the literature(≲104). Importantly, this size also prevents thermalagitation by flexural modes from dominating the dynamics.56 To ensure that we reach a stable thermal equilibrium state, oursimulations were relaxed for 4–20 ns. This is considerablylonger than used for studying wetting of rigid graphene (0.1–1ns).Using MD puts demands on dropletsize, relaxation times, and choiceof interatomic force fields.57 (http://lammps.sandia.gov/). To avoid manually tuning solid–liquidinteractions,54 we employ all atomic-optimized potentials for liquid simulations(OPLS-AA)59 for graphene. For the water molecules, weuse the rigid extended-point-charge (SPC/E) model,59 which is compatible with OPLS-AA for large organic molecules.60 Postprocessing of data and graphical representationswere done using the OVITO package (http://ovito.org/).61Classical MD simulationswere performed using LAMMPS–5 kcal/(mol Å) for the production runs. We did not observe anyconsiderable differences using increased accuracy.Within OPLS-AA, the graphene–waterinteraction is treatedvia a Lennard-Jones (LJ) potential. There have been many efforts tofind LJ parameters for the interaction between water and rigid graphene.These studies reached values that are close to that of OPLS-AA withSPC/E (cf. refs  and 51)51)). In 62 algorithm wasemployed for integrating the equations of motion, using a time stepof 2 fs. A Langevin thermostat (T = 300 K) was usedfor temperature control of water with 2 ps damping. Thus, we treatwater in the canonical (NVT) ensemble. The graphene was thermostatedevery 2 ps using a Nosé–Hoover thermostat, where weadjusted the box dimension in the transverse direction along the width(z), in order to relax stresses, as measured viaa Nosé–Hoover barostat. This produces samples from theisothermal–isobaric ensemble (NPT) for graphene. The initialvelocities of all atoms were drawn from a Gaussian distribution correspondingto T = 300 K. This way of thermostatting avoids the“flying ice-cube” artifact,63 where the thermostat gradually removes energy from high-frequencymodes and adds it to low-frequency ones. For small amounts of water,an alternative is to only thermostat the graphene and not the droplet(cf. ref (The velocityVerlet(cf. ref ).x-direction) and 3 to 12 nm in width (z-direction),with a C–C distance of 1.4148 Å prior to relaxation. Periodicboundary conditions were used in the transverse direction (z-direction). To impose an external tension x-directionalong the ribbon were uniformly distributed over two small regions,one at each edge. These regions extended a distance of 2 nm from theedges into the ribbon. The rest of the ribbon was allowed to interactfreely with the water. Normal to graphene, we inserted enough vacuumto make self-interactions negligible. These boundary conditions allowthe formation of a stable hemicylindrical water droplet with an axisof symmetry parallel to the z-axis.49 All simulations used the same ratio between the water volume andgraphene surface area. For 100, 150, 200, and 250 nm long and 12 nmwide ribbons, we consequently had hemicylindrical droplets approximately20, 24, 28, and 31 nm in diameter, respectively. This amounts to between53 × 103 and 133 × 103 water molecules.Our samples ranged from 100 to 250 nm in length (5 atoms), no time averaging is done, allowing also dynamicwetting angles64 to be determined.There are two common approaches for determining the wetting anglein MD. Conventionally, the positions of the atoms and molecules inthe liquid phase are averaged over a specific time to obtain the averagedensity distribution. This is followed by fitting the outer waterlayer, or a contour of constant average density close to the surface,to a circular contour. The main reason for this averaging is to minimizeerrors associated with the change of instantaneous angles. These canarise due to the high mobility of small droplets, where one must correctfor the drift of the center of mass during averaging. In the secondapproach, which works for relatively large droplets (≥10xy-plane (integrating over z) and averaged over 1 ns to obtain the water density profile (To determine the wetting angle at the triple-phase contact line(TCL), we relaxed the structures for at least 4 ns. However, mostsystems required much longer relaxation times, occasionally well over20 ns, depending on size and tension. In particular, close to thevesicle transition see 3d,e, lon profile 2. Least  profile 2. Assee profile 2, slightY = 68.5° for a flat water–grapheneinterface (65 forthe surface tension (γ ≈ 70.2 ± 2.1 mN/m), it canunderpredict γ when using PPPM,66 giving a value as low as γ ≈ 55.4 mN/m. To benchmarkour results for the wetting angle, we extensively verified differentaspects of our simulation with the existing literature. In particular,with regard to the wetting angle, we accurately reproduced the resultsof ref (Using SPC/E with OPLS-AA resulted in a wettingangle of θnterface 2a. Whilenterface −33), we sof ref .Y is less importantas we study the applicability of continuum theories on the nanoscaleand the variation of the wetting angle under deformation.Here, the exact value of θ2 graphene ribbon. The overall behavior, includingorders of magnitude for parameters (see below), confirms the theoreticalpredictions of the continuum theory of Kozyreff et al.37 In particular, our simulations clearly reproducethe transition to a vesicle-like state below a critical value of theapplied external tension D involves averaging overseveral hundreds of snapshots, with subsequent fitting , may also lead to bond breaking. Being a nonreactive forcefield, such phenomena cannot be studied using OPLS-AA.Note that ng e.g., 2. For ve2, with errorbars three times the standard deviation in the mean. From the dataset, we have excluded some points very close to the vesicle transitionwhere it was not possible to extract the angles due to the very narrowconstriction  when looking at length scales ∼ L. For this special case, Schulman and Dalnoki-Veress34 found that using a macroscopic constant wettingangle θYm, and assuming a sharp kink at the TCL,experiments could be fitted using force balancing provided that theparallel traction n∥ at the TCLobeyed the boundary conditionm, assuming that θYm remains constant, obeys the relationm asa function of the scaled tension m and θYm are macroscopic angles. They arethus not necessarily the same as β and θY,which we here define on a microscopic level, where the membrane curvaturechanges smoothly across the TCL. The result of using For a droplet of linear size 34 were later derived using variational approaches,38 accounting also for finite B. However, also thetheory of Kozyreff et al.37 lying in the xy-plane. In terms of r(s), the elastic free energy fora sheet of width W is, in the inextensible case,given by67B is the membrane bendingrigidity and μ is a Lagrange-multiplier ensuring that s is the proper arc length. The parametrization is chosensuch that s = 0 corresponds to the symmetry axisof the deformed membrane and s = ± D at the TCL  in V = WA, where the cross-sectional area A depends on r(s), D, and β. In accordance with the MD simulations,we here assume the droplet to be surrounded by vacuum and that allwater molecules are contained in the droplet.The free energy of the water droplet, containing r(s), minimizing F with respectto D and β, and makinguse of p = −∂V|N,TF0, reproduces the Laplace pressure p = γ/R for a hemicylindrical droplet and the Young–Dupréequation γcos θY = γ cos(β+ θD) = Δγ for the wettingangle at the TCL. Hence, unless γ/Δγ changes with tension or local curvature, it follows that θY should remain constant.For a (symmetric)partial wetting state given by a fixed F with respect to r(s) to obtain the pressurized elasticap(s) = γ/R for |s| ≤ D and zero outside the droplet. The vector n̂ is the unit normal to the graphenesurface.Using the Frenet–Serret formulas, and projecting (s) can be solved for in termsof the curvature κ(s) and the exernal tension To obtain the differentialequations for the shape of the sheet,we vary ojecting 5 onto thn⊥ ≡ – B∂sκ, i.e.,x-axis  = n⊥(s = 0) = 0, and vary the remaining free parametersto match the boundaryconditions at the TCL (s = D)A remains constant. x-axis. A amounts to treating the fluid as incompressible. Finally,we use p = γ/R and treat R as an adjustable parameter.We solve the system 7 numericA, B, γ, and θY.While we have established values of γ and θY  within the OPLS-AA, as well as the renormalizedbending rigidity for nearly flat sheets by calculating the height–heightcorrelation function. Also here, we find excellent agreement withthe literature .A/B. For a constant value of θY,smaller values of Γ will push the vesicle transition to lowervalues of B and lower bounds on γ and A, resultingin a lower bound of Γ = 29. Still, the continuum theory predictsa transition to the vesicle state at The red curvein 2. Thus, it may seem surprising thatmost data points seem to fall on the same curve as this changes Γby a factor of 2.5. This robustness to changes in Γ is alsopresent in continuum theory, which predicts that Γ must changeby an order of magnitude for the shift in the curve to be comparableto the uncertainty in the angles β and θY.Hence, the discrepancy between MD and continuum theory cannot be attributedto uncertainties in the parameters A, γ, or B. As we observe a nonconstant wetting angle ,which could influence our measured θY. This is incontrast to the continuum theory, where the surface tension exertsa force acting along a line of zero width, predicting a constant wettingangle.For a flexible membrane,the deformation is directly connectedto the local values of γ and Y to differing dimensionality. On the contrary,in three dimensions, static wrinkles, or creases, can drasticallyincrease the local bending rigidity due nonzero Gaussian curvature.For some of the wider samples, such creases formed during the relaxation.An example is shown in Y even when The final difference between the continuum theory andMD simulationsis that the former is 2D, while the ribbons in our MD simulationsare three-dimensional. As we observe a changing wetting angle alsowhen making the ribbons more narrow, we cannot attribute the changesin θY. We stress that different sensitivities of the results withrespect to Γ and θY imply that uncertaintiesin material parameters cannot account for the observed nonconstantwetting angle. As continuum theory still applies, albeit modified,we expect that for larger systems, one will recover agreement withtheories predicting constant wetting angles. However, to further increasesystem sizes, coarse-grained models will be needed.Inconclusion, using large-scale MD simulations of droplets onsuspended graphene nanoribbons under external tension, we have foundthat existing macroscopic continuum theories work qualitatively wellalso at the nanoscale. In particular, for large deformations (lowtensions), one obtains a good agreement between atomistic simulationsand continuum theory provided one allows for a nonconstant θWhile thewetting angle itself is of fundamental interest, ourresults also have a more direct consequence, which may be easier todetect. This concerns the magnitude of the external tension at whichvesicles begin to form. Our simulations and modified model show thatthis occurs at a tension that can be considerably lower than predictedby macroscopic continuum theory. This is important in situations inwhich large deformations are of interest. Potential areas of impactare design of nanoscale biomimetic actuators that respond to moisture,capillary origami, vesicle formation, budding, etc. , and other phenomena involvinghighly flexible membranes.


As well as having affiliation 2, they should also have “1 College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China.”In the published article, there was an error regarding the affiliations for Yining Di. As well as having affiliations 2 and 3, they should also have “3 Sugarcane Research Institute, Yunnan Agricultural University, Kunming, China.”In the published article, there was an error regarding the affiliations for Lilian He. As well as having affiliation 1, they should also have “3 Sugarcane Research Institute, Yunnan Agricultural University, Kunming, China.”In the published article, there was an error regarding the affiliations for Fusheng Li. As well as having affiliation 1, they should also have “Results, “3.1. Genomic features and annotation of Bacillus velezensis YC89”, paragraph two. The sentence previously stated:A correction has been made to Supplementary Figure S3).”“Among these pathways, the most represented pathways included carbohydrate metabolism , followed by amino acid metabolism, and metabolism of cofactors and vitamin pathways .”“Among these pathways, the most represented pathways included carbohydrate metabolism , followed by amino acid metabolism, and metabolism of cofactors and vitamin pathways . For an in-depth analysis of the taxonomic status of YC89 strain, a genomewide phylogenomic tree was constructed. The results revealed that strain YC89 formed a close genetic relationship with strain B. velezensis GS-1 .”“These results indicate that YC89 is closely related to The corrected sentence appears below:B. velezensis FZB42 and B. amyloliquefaciens 6B . For an in-depth analysis of the taxonomic status of YC89 strain, a genome-wide phylogenomic tree was constructed. The results revealed that strain YC89 formed a close genetic relationship with strain B. velezensis GS-1 .”“These results indicate that YC89 is closely related to Results, “3.8.2. Evaluation of Bacillus velezensis YC89 for biocontrol potential and growth promotion under greenhouse conditions”, paragraph one. The sentence previously stated:A correction has been made to Supplementary Figure S5).”“Collectively, the potted results indicated that the YC89 strain could control the red rot disease of sugarcane and promote the growth of sugarcane plants .”“Collectively, the potted results indicated that the YC89 strain could control the red rot disease of sugarcane and promote the growth of sugarcane plants (In the published article, there were errors in The authors apologize for these errors and state that they do not change the scientific conclusions of the article in any way. The original article has been updated.


Metabolic dysfunction-associated fatty liver disease (MAFLD) represents a new classification system for fatty liver disease. In this study, we investigated the clinical characteristics of patients with MAFLD-hepatocellular carcinoma (HCC) in comparison with those with nonalcoholic fatty liver disease (NAFLD) and considered the validity and challenges of the new criteria.This study included 237 untreated non-B, non-C HCC patients with hepatic steatosis. We examined the profile and laboratory findings of patients with MAFLD-HCC and NAFLD-HCC. We also classified MAFLD-HCC patients according to the factors on which the diagnosis was based and compared their clinical characteristics.A total of 222 (94%) and 101 (43%) patients were diagnosed with MAFLD and NAFLD, respectively. MAFLD-HCC patients were more likely to be male than NAFLD-HCC, but there were no significant differences in metabolic indices, noninvasive liver fibrosis score or HCC status. In a study of MAFLD-HCC patients by diagnostic factor, those with overweight only were younger and had advanced liver fibrosis histologically, and when limited to patients younger than 70 years, the majority were overweight. Redefinition of overweight as BMI ≥ 25 reduced the number of MAFLD-HCC patients by only 5, from 222 to 217.MAFLD accounted for the majority of non-B, non-C HCC cases with hepatic steatosis. Examination of additional cases and revision of the detailed criteria is needed so that it can be used to efficiently select patients with fatty liver who are at high risk of developing HCC.The online version contains supplementary material available at 10.1186/s12876-023-02851-y. Nonalcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases, estimated to affect about 25% of the world population –3. It isThe term NAFLD was first coined by Jurgen Ludwig et al. in 1980 . Now it On the other hand, the inaccuracy and divergence from clinical practice of the term NAFLD have long been noted , 28. ThuThus, the superiority of MAFLD to NAFLD is still unclear, and further studies are needed. Furthermore, there is an overwhelming lack of studies comparing the two criteria in patients with hepatocellular carcinoma (HCC). HCC is a poor prognosis complication of fatty liver disease, and is not only due to viral or alcoholic hepatitis  as in thn = 1,661), autoimmune hepatitis and/or primary biliary cholangitis (n = 31), or glycogen storage disease (n = 1)  ≥ 126 mg/dl, 75 g oral glucose tolerance test (OGTT) 2-h value ≥ 200 mg/dl, or casual blood glucose ≥ 200 mg/dl plus glycated hemoglobin (HbA1c) ≥ 6.5%. MRA included the following six items, and MAFLD was diagnosed in lean or normal weight patients if two or more of them were met; (a) waist circumference ≥ 90 cm in men and 88 cm in women, (b) blood pressure ≥ 130/85 mmHg or on specific drug treatment, (c) plasma triglycerides (TG) ≥ 150 mg/dl or on specific drug treatment, (d) plasma high-density lipoprotein cholesterol (HDL-C) < 40 mg/dl for men and 50 mg/dl for women or on specific drug treatment, (e) prediabetes (FBS 100 to 125 mg/dl or HbA1c 5.7 to 6.4%), (f) homeostasis model assessment of insulin resistance (HOMA-IR) score ≥ 2.5. Although the original criteria for MRA included an item referring to C-reactive protein (CRP) level, we did not include it since CRP levels of the patients in this study were considered to be affected by the status of HCC. NAFLD was diagnosed in patients without habitual drinking or light drinkers .All patients in this study were screened for the criteria for MAFLD and NAFLD based on their physical and laboratory findings prior to treatment for HCC. Hepatic steatosis was determined pathologically by fatty accumulation in more than 5% of the hepatocytes in a specimen or by the presence of findings on ultrasonography, i.e., increased hepatic echogenicity, intrahepatic vascular blurring, ultrasound attenuation or controlled attenuation parameter value of 230 dB/m or higher. Diagnostic criteria for MAFLD were largely in accordance with those proposed by the international expert consensus statement [th edition were listed as indicators of HCC progression. All of these data were taken before the initial treatment for HCC.Clinical background measurements included the year of HCC diagnosis, age, gender, BMI, waist circumference, alcohol consumption and incidence of type 2 DM, hypertension, and dyslipidemia. Regarding alcohol consumption, all patients were classified into four categories: none, light, moderate, and heavy drinkers. Those with no habitual drinking were classified as “none”. The definition of light drinkers is described above. Heavy drinkers were defined as those who drank 60 g of ethanol equivalent or more per day, and moderate drinkers as those who drank between light and heavy drinkers. Hypertension was defined as blood pressure ≥ 130/85 mmHg or on specific drug treatment. Dyslipidemia was defined as HDL-C < 40 mg/dl for men and 50 mg/dl for women or TG ≥ 150 mg/dl or on specific drug treatment. Laboratory measurements included platelet count, prothrombin activity (PT), total bilirubin (T-Bil), aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyl transferase (γ-GTP), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), choline esterase (ChE), albumin (ALB), ammonia, TG, HDL-C, low-density lipoprotein cholesterol (LDL-C), FBS, HbA1c, alpha-fetoprotein (AFP), and des-γ-carboxy prothrombin (DCP). Non-invasive liver fibrosis assessment included fibrosis-4 index (Fib-4 index) and NAFLD fibrosis score (NFS). The number and maximum diameter of tumors and clinical stage assessed by general rules for the clinical and pathological study of primary liver cancer 6p value < 0.05 were considered to be statistically significant. All analysis was conducted using EZR 4.0.3.Continuous variables were expressed as median with range. Categorical variables were expressed as total numbers and percentages. Differences between the groups were investigated using Fisher’s exact test for categorial variables and Mann–Whitney U test or Kruskal–Wallis test for continuous variables. Results with a Of the 237 patients enrolled in this study, 198 (84%) were male, with a median age of 73 years and a median BMI level of 24.9  and 101 (43%) were diagnosed with MAFLD and NAFLD, respectively; 94 (40%) met both of the two criteria, 128 (54%) met only MAFLD, 7 (3%) met only NAFLD  by the year of diagnosis. The number of all HCC patients diagnosed in the year and MAFLD-HCC and NAFLD-HCC patients among them are shown at the bottom. While the proportions of both were less than 5% prior to 2010, they have been on the rise with MAFLD-HCC in particular increasing significantly, accounting for around 25% of the total cases in recent years. NAFLD-HCC, on the other hand, has increased to just under 15%, and the difference between the two is larger than before.Figure n = 222). The number of patients with DM, overweight, and MRA were 135 (61%), 168 (76%) and 171 (77%), respectively. 11 (5%), 12 (5%) and 19 (9%) patients had only DM, overweight and MRA, respectively, and 28 (13%) had DM and overweight, 24 (11%) had DM and MRA, 56 (25%) had overweight and MRA, and 72 (32%) had all three of the components for the diagnosis of MAFLD. Figure n = 195), the percentage of advanced liver fibrosis cases with fibrosis stage 3 or 4 was 80% in overweight-only group, higher than the overall population (44%), DM-only group (11%), MRA-only group (29%), and the group with all three (37%) . 2 (3%), 10 (14%) and 3 (4%) patients had only DM, overweight and MRA, respectively. 13 (18%) had DM and overweight, 4 (5%) had DM and MRA, 16 (22%) had overweight and MRA, and 25 (34%) had all of the three.Figure 2. Although the number of overweight patients decreased by 56, from 168 to 112, most of them still met the MAFLD criteria due to DM and/or MRA comorbidity; only 5 patients were omitted.Figure We examined the clinical characteristics of non-B, non-C HCC cases with hepatic steatosis and considered the validity of MAFLD. MAFLD is a new classification for fatty liver disease that focuses on the presence of metabolic dysfunction. There have been many reports published on the relationship between hepatic carcinogenesis and metabolic dysfunction. Accumulated visceral fat and type 2 DM contribute to the development of HCC through various mechanisms, including inflammatory cytokines, several endocrine pathways, insulin resistance, hepatic lipotoxicity, hepatic and peripheral insulin resistance, and oxidative stress –37.Although MAFLD criteria originally allow for viral hepatitis complication, in this study we focused on HCC with fatty liver disease associated with metabolic abnormalities as the primary cause, and excluded viral HCC cases. Actually, the target population was characterized by complication of metabolic dysfunction with a higher incidence of lifestyle-related diseases than the excluded hepatitis C cases. The difference in tumor progression at diagnosis between the target cases and the hepatitis C cases shown in Table  included more than 60% of total cases, and therefore were considered to be essential in terms of distinguishing HCC patients Fig. a. Huang ications . In our  [2 Fig. . This alThis study has two limitations. First, it was conducted at a single institution, and therefore, it is not certain whether similar results would be obtained in other geographic or ethnic cohorts. In addition, since this is a retrospective study, we cannot evaluate the difference in the risk of HCC development between MAFLD and NAFLD patients; prospective studies need to be conducted to more accurately assess the value of the disease concept of MAFLD.2 instead of ≥ 23 kg/m2. Further accumulation of cases and revision of the detailed criteria are needed so that it can be used to efficiently select patients with fatty liver who are at high risk of developing HCC.In conclusion, MAFLD is a new disease concept that is superior to NAFLD in the inclusion of non-B, non-C HCC cases. In diagnosing MAFLD, we propose to define overweight as BMI ≥ 25 kg/mAdditional file 1: Table S1.  Alcohol consumption for each group.


This study explores the predictive power of macro-structural characteristics on quality rating and improvement system (QRIS) outcomes of Family Day Care (FDC) services in Australia.The dataset consisted of 441 FDC National Quality Standard (NQS) ratings from all Australian states and territories, with overall ratings of Exceeding NQS, Meeting NQS, Working Towards NQS, or Significant Improvement Required.Multinomial logistic regressions confirmed that management type, community socioeconomic status (SES), level of urbanization, and government jurisdiction explained 6.9 to 19.3% of the variation in QRIS outcomes. Results indicated that lower FDC NQS ratings were more likely for (1) private for-profit vs. not-for-profit; (2) low-SES vs. high-SES area; and (3) regional or remote area vs. metropolitan. State/territory jurisdiction also influenced NQS ratings.These findings imply the need for policy attention to inequalities in FDC quality associated with systemic and organizational differences. Greater effort is needed to promote equality and equity in FDC services. Early childhood education and care (ECEC) attendance can positively affect young children’s early learning and development, academic success, and socioeconomic mobility . Family Multiple layers of structure, deemed “macro-structural” variables by Harrison et al. , may infAustralian FDC educators are self-employed providers of regulated home-based ECEC and are registered with and supported by the coordination units of approved FDC schemes . EducatoEducation and care services have been assessed against the NQS since 2012. The NQS sets a national quality benchmark for publicly funded education and care services in Australia. It is part of the National Quality Framework (NQF), a unified national system for education and care introduced in 2008 . The rolThe importance of ECEC quality has led to a plethora of research into factors that predict quality e.g. , 14,). S,. S14),)For-profit ECEC services are generally reported to be lower quality than not-for- profit services , 17–20. Low SES levels have been theorized to be correlated with thin markets, resulting in lower ECEC quality . SignifiResearch has indicated that rural areas offer lower-quality education and care services than urban areas , 26. ForRegulatory differences between state government jurisdictions have influenced ECEC quality in the United States of America , 29. ForThis study aimed to examine the influences of these four macro-structural characteristics on differences in FDC quality as assessed through Australia’s NQS A&R system. An overall rating of “SIR” or “WT” indicates lower levels of quality. “MEET” means that all standards have been met, while a rating of “EXCEED” indicates higher levels of quality. An overarching question about whether macro-structural factors predict FDC quality was operationalized into three analysis questions:Do management type, SES, level of urbanization, and jurisdiction predict the likelihood of an FDC scheme…attaining a rating of “WT”/“SIR” vs. a rating of “MEET”?attaining a rating of “WT”/“SIR” vs. a rating of “EXCEED”?attaining a rating of “MEET” vs. a rating of “EXCEED”?The study used Australia’s national data repository of NQS ratings to undertake secondary data analysis. Data for 441 FDC schemes in Australia that had completed an NQS assessment by December 2020  were anaThe overall quality of each scheme was assessed following the NQS assessment and rating process as:Significant Improvement Required: Service does not meet 1 of the 7 QAs or a section of the legislation, and there is a significant risk to the safety, health, and wellbeing of children. The regulatory authority will take immediate action;Working Towards NQS: Service provides a safe education and care program. There are one or more QAs identified for improvement;Meeting NQS: Service provides quality education and care in all seven QAs;Exceeding NQS: Service goes beyond the requirements of the NQS in at least four of the seven QAs.Frequency analysis for the assessed 441 schemes showed that only 4 (0.9%) schemes had received an overall NQS rating of “SIR,” 209 (47.4%) schemes received a rating of “WT,” 170 (38.5%) schemes received a rating of “MEET,” and 58 (13.1%) schemes received a rating of “EXCEED.”Of the 441 FDC schemes, 205 (46.5%) had been assessed under the original 2012 version of the NQS, and 236 (53.5%) had been assessed with the 2018 version.n = 248, 56.2%), followed by local government FDC schemes , community-based FDC schemes , and FDC schemes run by other not-for-profit organizations .Management type for the FDC schemes was identified by four categories: (1) for-profit; (2) not- for-profit – community-based organizations; (3) not-for-profit – other organizations; and (4) not-for-profit - local government. Frequency analysis showed that for-profit FDC schemes were the largest management type , followed by quintile 2 , quintiles 3 and 4 , and quintile 5 . The remaining 25 FDC schemes were not matched to a SEIFA level.Community SES was measured by the Socioeconomic Indexes for Areas (SEIFA) which is a composite of four indexes, including two indexes of advantage, one of education and occupation, and one of economic resources . Higher n = 300, 68.0%), followed by inner regional , outer regional , remote , and very remote  areas of Australia.The level of urbanization was measured by the Accessibility and Remoteness Index of Australia (ARIA+) which represents a general access model covering education, health, shopping, public transport, and financial/postal services . ARIA+ ln = 147, 33.3%), followed by Victoria , Queensland , Western Australia , South Australia , Tasmania , ACT , and the Northern Territory .There are eight government jurisdictions in Australia: six states, New South Wales, Victoria, Queensland, Western Australia, South Australia, Tasmania, and two territories, Australia Capital Territory (ACT) and Northern Territory. New South Wales had the most FDC schemes . Regression analysis was employed to examine the influences of each macro-structural characteristic on FDC quality. Because the outcome variable was categorical, we chose multinomial logistic regression (MLR), which predicts the probability that an observation falls into one of three or more categories of a dependent variable (DV) based on one or more independent variables (IVs) . The IVsR2 (a pseudo R2) was used to estimate the variation in overall NQS rating scores explained by the model  MEET and (2) EXCEED. The second set of MLRs used “MEET” as the reference category to test the predictive effects of each of the four macro-structural characteristics predicting a rating of “MEET” versus (1) “EXCEED” and (2) “WT”/ “SIR.” The validity of the models was tested with a chi-square test and confirmed with Pearson’s goodness of fit test. The nagelkerke he model . Finallyhe model . To fullThe version of the NQS (2012/2018) was included as a covariate. This increased the number of statistical tests, with a corresponding increase in the potential for a Type I error . A Type I error risk was accounted for by dividing the standard critical value of 0.05 by two, the total number of predictor variables, to yield a critical value of 0.025 .The distribution of FDC schemes across the four NQS quality ratings by each macro-structural characteristic and the NQS version are presented in The pattern of distributions of NQS ratings across the five SEIFA levels suggested an association between quality and community SES. A lower proportion of FDC schemes located in more disadvantaged communities (SEIFA quintiles 1 and 2) were rated as “EXCEED”  compared to FDC schemes located in SEIFA quintiles 4 and 5 (22.4 and 25.8%). However, there was inconsistent evidence of a linear relationship: for example, only 5.2% of FDC schemes located in SEIFA quintile 3 were rated as Exceeding NQS.Results for the level of urbanization indicated that FDC schemes located in major cities were more likely to be rated as “WT” (53.0%) than FDC schemes in inner regional and outer regional areas . The converse pattern was evident for ratings of “MEET,” which were more evident in regional areas  than in cities (34.3%).The results for jurisdiction suggested some differences between the states. FDC schemes in Queensland had a lower proportion of “WT” ratings (35.0%) than FDC schemes in New South Wales (46.9%), Victoria (52.6%), Western Australia (73.3%) and South Australia (66.7%), and a higher proportion of “Exceeding NQS” .Results also showed differences for FDC schemes assessed under the 2012 and 2018 versions of the NQS. While similar proportions of schemes received a “WT” rating , “EXCEED” ratings were less likely (7.6 vs. 19.5%), and “MEET” ratings were more likely (43.6 vs. 32.7%) for 2018 versus the 2012 version.A multiple regression model may be invalid due to the existence of categorical variables in the study. A number of preliminary analyses were conducted to confirm the viability of a multiple regression analysis.2(2) = 15.416, p < 0.001. The version of the NQS was subsequently entered as a covariate in the MLRs for each of the four macro-structural characteristics. Next, we tested for model validity. For an MLR model to be valid: (1) there should be few covariate patterns with expected cell frequencies of zero; and (2) the proportion of expected cell frequencies greater than 5 should be 80% or more , remote Australia (n = 4), and very remote Australia (n = 2), were combined into a single category. For jurisdiction, the five states and territories with the lowest numbers of FDC schemes – Western Australia (n = 30), South Australia (n = 12), Tasmania (n = 10), ACT (n = 6), and NT (n = 3), were combined into a single sub-category. When the MLR was repeated with the new sub-categories, the models were valid. MLR was thus an appropriate method of analysis.Our preliminary analyses also showed that entering more than one macro-structural predictor (IV) in the regression model resulted in invalid models. To create more trustworthy models, we reduced the number of categories for level of urbanization and jurisdiction. The three levels of urbanization with the highest remoteness levels – outer regional Australia (R2) of the variance in overall NQS ratings, respectively  = 80.31, p < 0.001, Pearson’s χ2(6) = 8.89, p = 0.180, R2 = 0.193. Significant Odds Ratios were evident for all three MLR comparisons. These are summarized using for-profit FDC as the reference category in p < 0.001), FDC schemes managed by other not-for-profit organizations , and local government managed FDC . For-profit FDC schemes were also more likely to be rated “WT”/“SIR” compared to “EXCEED” than community-based not-for-profit FDC schemes , FDC schemes managed by other not-for-profit organizations , and local government FDC schemes . For- profit FDC schemes were more likely to be rated “MEET” compared to “EXCEED” than community-based not-for-profit FDC schemes , and local government FDC schemes .Management type had a moderate effect on FDC quality,χ2(10) = 34.29, p < 0.001; Pearson’s χ2(8) = 6.74, p = 0.565, R2 = 0.092. All five levels of SES (SEIFA quintiles 1 to 5) were tested as the reference category in a series of MLR tests to explore the overall effect. Results are summarized for Odds Ratio comparisons for quintile 3, the mid-point, in p = 0.011) and more likely to be classified as “MEET” compared to “EXCEED’ than FDC schemes in quintile 4 , and SEIFA quintile 5 .Community SES had a small but significant effect on FDC quality ratings, χ2(6) = 27.19, p < 0.001, Pearson’s χ2(4) =7.70, p = 0.103, R2 = 0.069. Results are summarized using major cities of Australia as the reference category. FDC schemes in major cities of Australia were significantly more likely to be (1) rated “WT”/“SIR” compared to “MEET” and (2) rated “WT”/“SIR” compared to “EXCEED” than schemes in inner regional Australia , respectively.Level of urbanization had a small effect on FDC quality ratings, χ2(8) = 30.66, p < 0.001; Pearson’s χ2(6) =8.66, p = 0.193, R2 = 0.078] based on our four-level categorization that compared New South Wales, Victoria, Queensland and combined group of five jurisdictions. While all categories were tested as the reference category in a series of MLR tests, Odds Ratios results are summarized for Queensland as the comparison state. FDC schemes managed by Queensland were less likely to be rated “WT”/“SIR” compared to “MEET” than FDC schemes managed by the combined group , and less likely to be rated “WT”/“SIR” compared to “EXCEED” than FDC schemes managed by Victoria . In addition, MLR results using Victoria as the reference category showed that FDC schemes managed by Victoria were more likely to be rated “WT”/“SIR” compared to “MEET” than the combined FDC schemes managed by the combined group . MLR results for New South Wales as the reference category indicated no differences in quality ratings for jurisdiction.Government jurisdiction showed a small but significant difference in FDC quality ratings . Th. Th21)].The present study found that FDC schemes in regional areas of Australia attained higher levels of quality than services in metropolitan areas. This finding contrasts with previous studies reporting higher quality ECEC in metropolitan areas for kindergartens and LDC centers , 25, 41.Finally, our findings also identified jurisdiction as a predictor of FDC quality. FDC schemes in Queensland were generally found to be of higher quality than FDC schemes other jurisdictions, with the exception of NSW. Since 2012, all states and territories have been expected to adhere to national guidelines for quality; however, before the reforms introduced in 2012, there were disparities in standards and administrative processes for the child-to-staff ratios and educators’ qualifications. It may be that these prior distinctions continue to affect current levels of quality.Overlap between the NQS Quality Areas (QAs) and standardized assessments of quality has been reported by Siraj et al.  who founWhile our use of a publicly available, large, national data set to examine macro-structural differences in FDC quality has made a unique contribution, these data have inherent limitations. FDC assessment and rating is conducted in a sample of FDC homes that are expected to represent the large number of individual FDC homes in each scheme. A further limitation is that, unlike in the U.S., the assessment process does not use of standardized observation instruments and, while accepted as conceptually sound , 46, onlSecond, the small numbers of schemes in Western Australia, South Australia, Tasmania, the ACT, and Northern Territory were combined into one group to ensure valid regression models, but this was not a coherent group. Our initial analyses (summarized in Given that FDC is a widely utilized yet seldomly researched form of ECEC, the potential offered by Australia’s NQS dataset has realized important findings. The Australian NQS is unique in applying a universal quality improvement and rating system across all jurisdictions. It is also tied to standardized regulations for child-educator ratios, educator qualifications, and the Australian government’s childcare fee subsidy system. The NQS is a relatively novel measure of quality in FDC, about which relatively little is known from a research perspective. This study contributes to ongoing research about macro-structural characteristics that predict FDC quality and research about predictors of NQS quality indicators. Findings imply that policy attention should be paid to the variations and inequalities linked to macro-structural characteristics to promote equality and equity in FDC services.acecqa.gov.au.Publicly available datasets were analyzed in this study. This data can be found at: This article reports VC’s postgraduate study, co-supervised by LH and HL, at Macquarie School of Education, Macquarie University. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This cohort study assesses the memory clinic prevalence and prognosis of individuals with a β-amyloid–negative, tau-positive cerebrospinal fluid biomarker profile. What is the memory clinic prevalence and prognosis of individuals who have a β-amyloid–negative, tau-positive (A−T+) cerebrospinal fluid (CSF) profile?In a clinical cohort of 7679 individuals, prevalence of A−T+ CSF was 4.1%. Longitudinally, cognitively unimpaired individuals or individuals with mild cognitive impairment and an A−T+ CSF profile exhibited similar trajectories to individuals with an A−T− CSF profile in relation to cognitive deterioration, brain atrophy, or cerebral glucose metabolism and β-amyloid burden indexed by positron emission tomography.A CSF profile of A−T+ appears to be benign despite being classified as a pathologic change by guidelines; compared with individuals with biomarker-negative CSF, individuals with A−T+ CSF do not have higher rates of cognitive decline or faster disease progression. Knowledge is lacking on the prevalence and prognosis of individuals with a β-amyloid–negative, tau-positive (A−T+) cerebrospinal fluid (CSF) biomarker profile.To estimate the prevalence of a CSF A−T+ biomarker profile and investigate its clinical implications.This was a retrospective cohort study of the cross-sectional multicenter University of Gothenburg (UGOT) cohort (November 2019-January 2021), the longitudinal multicenter Alzheimer Disease Neuroimaging Initiative (ADNI) cohort , and 2 Wisconsin cohorts, Wisconsin Alzheimer Disease Research Center and Wisconsin Registry for Alzheimer Prevention . This was a multicenter study, with data collected from referral centers in clinical routine (UGOT) and research settings (ADNI and WISC). Eligible individuals had 1 lumbar puncture , 2 or more cognitive assessments (ADNI and WISC), and imaging (ADNI only) performed on 2 separate occasions. Data were analyzed on August 2022 to April 2023.18F]-florbetapir amyloid positron emission tomography (PET), magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose PET (FDG-PET), and cross-sectional tau-PET .Baseline CSF Aβ42/40 and phosphorylated tau (p-tau)181; cognitive tests . Exposures in the ADNI cohort included [Primary outcomes were the prevalence of CSF AT biomarker profiles and continuous longitudinal global cognitive outcome and imaging biomarker trajectories in A−T+ vs A−T− groups. Secondary outcomes included cross-sectional tau-PET.A total of 7679 individuals  were included in the UGOT cohort, 970 individuals  were included in the ADNI cohort, and 519 individuals  were included in the WISC cohort. The prevalence of an A−T+ profile in the UGOT cohort was 4.1% , being less common than the other patterns. Longitudinally, no significant differences in rates of worsening were observed between A−T+ and A−T− profiles for cognition or imaging biomarkers. Cross-sectionally, A−T+ had similar tau-PET uptake to individuals with an A−T− biomarker profile.Results suggest that the CSF A−T+ biomarker profile was found in approximately 5% of lumbar punctures and was not associated with a higher rate of cognitive decline or biomarker signs of disease progression compared with biomarker-negative individuals. In recent research criteria, low Aβ42 or Aβ42/40 ratio and increased concentration of p-tau have been proposed to support the diagnosis of AD,2 Because p-tau increases are suggested to be AD specific,5 and increases would not be expected in Aβ-negative individuals, this finding is challenging to interpret. Guidelines vary in the terminology assigned to this finding, mostly encompassing the concept of suspected non-Alzheimer pathology.6 For example, studies evaluating the prevalence of CSF biomarker abnormalities suggest that such an A−T+ CSF profile can be found in up to 20% of cognitively unimpaired (CU) older adults.10 In addition, available studies show mixed results in the progression of disease-related changes in individuals with this biomarker profile.11 Previously, various propositions have been made to explain its underlying cause, including non-AD tauopathies  and altered CSF dynamics, among others.12 Importantly, these explanations likely vary depending on the biomarker modality being used. Unlike Aβ biomarkers, which seem to provide similar information in terms of “A” status when determined by positron emission tomography (PET) or CSF, tau biomarkers present pronounced differences across imaging and fluid modalities. Although fibrillar tau aggregates targeted by tau-PET radiotracers are likely a more direct proxy of tangle pathology,14 soluble p-tau measured by immunoassays may reflect a neuronal response to Aβ pathology,18 although p-tau is also present in dystrophic neurites, suggesting that it also reflects tau pathology.20 Although evidence is accumulating that individuals with an A+T− biomarker profile  are at risk for cognitive decline,10 less is known about the clinical implications of the CSF A−T+ pattern. Because CSF biomarkers are frequently used in clinical practice, it is important to further understand the implications of patterns not expected within the currently proposed model of AD biomarker progression.Normal CSF Aβ in combination with increased p-tau is a finding sometimes seen in clinical settings.Given the lack of studies directly evaluating the clinical relevance and prevalence of an isolated increase in CSF p-tau, we aimed to assess the prevalence, as well as cognitive and biomarker progression rates, associated with the CSF A−T+ profile, using 4 large data sets.STROBE) reporting guidelines.Four cohorts were used in the present study: University of Gothenburg (UGOT), the Alzheimer Disease Neuroimaging Initiative (ADNI), Wisconsin Registry for Alzheimer Prevention (WRAP), and Wisconsin Alzheimer Disease Research Center (WI-ADRC). For each cohort, a local institutional review board for human research or ethics committee approved of the study. The UGOT cohort consisted of cross-sectional data and was retrospectively compiled using anonymized data from the clinical routine laboratory database at Sahlgrenska University Hospital, Gothenburg, Sweden, based on CSF analyses performed between November 2019 and January 2021. ADNI is a longitudinal multicenter observational study with data being collected between September 2005 and May 2022. WRAP and the Wisconsin Alzheimer Disease Research Center (WI-ADRC) are single-center, longitudinal, observational, cohort studies that collected data between February 2007 and November 2020. The present study followed the Strengthening the Reporting of Observational Studies in Epidemiology , we included only individuals who were CU or had mild cognitive impairment  at the first cognitive evaluation. In the UGOT cohort, no data on participant race and ethnicity were available, as they are not routinely collected by health care services. In the ADNI cohort, race categories included were American Indian or Alaska Native, Asian, Black, Hawaiian, multiracial, and White. In the WISC cohort, individuals self-identifying as White were reported, whereas American Indian or Alaska Native, Asian, Black or African American, or those not identifying as any of the stated races (here indicated as other), were not reported to maintain anonymity for groups with less than 3 individuals. More information on all cohorts used can be found in eMethods 1 in 23 In both the ADNI and WISC cohorts, p-tau was quantified using a fully automated Elecsys assay (Roche), also recently approved by the US Food and Drug Administration. Aβ42 and Aβ40 were measured with mass spectrometry and Elecsys assays in the ADNI and WISC cohorts, respectively. Elecsys Aβ42 is an in vitro diagnostic test approved for use in the European Union and US; Aβ40 was measured using a research use−only Elecsys assay. CSF status determined at baseline lumbar puncture was tested in the WISC analyses. In all cohorts, previously established cohort-specific cutoffs were used. In ADNI, cutoffs of 0.0138 or less and 24 pg/mL or greater were used for Aβ42/40 and p-tau, respectively. In WISC, abnormal biomarker results were determined to be 0.046 or less and 24.8 pg/mL or greater for Aβ42/40 and p-tau, respectively. More information on the CSF collection and quantification of and cutoff determination for Aβ42, Aβ40, and p-tau can be found in eMethods 2 in CSF Aβ42/40 ratio and p-tau181 (hereafter referred to as p-tau) were quantified in all participants and were used to define AT status in all 3 cohorts. In the UGOT cohort, CSF concentrations of Aβ42, Aβ40, and p-tau were quantified on the Lumipulse G1200 platform (Fujirebio), a fully automated assay recently approved by the US Food and Drug Administration.18F]-florbetapir, [18F]-fluorodeoxyglucose (FDG) PET, and hippocampal volume magnetic resonance imaging (MRI). A brief description of imaging methods can be found in eMethods 3 in 25 As a secondary analysis, we evaluated summary tau-PET measures between CSF AT groups, to investigate whether elevated p-tau in the absence of Aβ could represent an abnormal tau-PET phenotype. In the WISC sample, a subset of participants underwent [18F]-MK6240 tau-PET and were evaluated with an entorhinal cortex region of interest (ROI), as further detailed in eMethods 3 in 26 In the ADNI cohort, a subsample of the individuals herein included had available [18F]-flortaucipir tau-PET data up to 6 years after the baseline lumbar puncture, and the previously described ADNI meta-temporal ROI was used as the composite region.27In longitudinal imaging analyses in ADNI participants, we included those who had at least a baseline and a follow-up scan. We evaluated longitudinal trajectories of Aβ -MK6240 tau-PET SUVr than the A−T− group . In both the ADNI and WISC cohorts, the A+T+ group had higher tau PET SUVr  compared with the A−T+ group. Differences were less clear for the CSF A+T−group , we evaluated whether isolated p-tau positivity was associated with the deposition of fibrillar tau . In the In this cohort study, we found that the prevalence of CSF A−T+ in a clinical neurochemistry laboratory, likely to be representative of a general memory clinic population, was 4.1%, which is far lower than the prevalence of other CSF profiles. Further, compared with the A−T− biomarker profile, we found that the A−T+ profile was not associated with higher rates of cognitive deterioration, Aβ accumulation, tau PET pathology, or neurodegeneration.33 This slight discrepancy may be explained by the presence of other disorders causing cognitive symptoms in our cohort and likely reflects more similarly what may be expected in a real-world memory clinic, considering most CSF analyses are ordered by memory clinics or general neurology centers across Sweden. However, fewer studies have performed in-depth evaluations of the clinical meaning of CSF results not consistent with the current view of the chain of AD pathophysiological events. Previous studies evaluating the prevalence of CSF AT abnormalities report mixed results. Although many studies report higher figures (above approximately 15%) for the prevalence of an A−T+ profile, most of these have relied on CSF Aβ42 alone to define “A” status.35 When defining “A” with the widely recommended Aβ42/40 ratio,36 this prevalence is substantially reduced to figures like the one reported here for this large, unselected clinical chemistry cohort (approximately 4%).8 This suggests that the absence of correction for Aβ40 concentrations may lead to artificially deflated frequencies for CSF Aβ abnormalities, and, consequently, artificially inflated frequencies for a CSF A−T+ profile. This highlights the need for using CSF Aβ42/40 in clinical settings when separately assessing “A” and “T” CSF biomarkers.Several studies have attempted to determine the prevalence of AD pathophysiology in varying scenarios. We found that in the UGOT cohort, CSF Aβ abnormalities ranged from approximately 25% in the youngest individuals to roughly 60% in the oldest individuals. These estimates are lower than those found in a recently published, large, multicenter study evaluating the prevalence of Aβ positivity as determined by either PET or CSF.6 More specifically, during the early phases of AD CSF biomarker research, p-tau was considered by some studies as a biomarker of neuronal injury.10 However, it is now known that biomarker abnormalities, especially in “T” and “N” (for neurodegeneration) categories, do not provide the same information within and between modalities.35 Some authors argue that a CSF A−T+ pattern may reflect tau pathophysiology not attributable to Aβ accumulation, such as primary age-related taupathy, but this theory has become increasingly untenable due to the accumulation of fluid biomarker evidence that tangle pathology in the absence of concurrent Aβ pathology is not well reflected by p-tau,38 although data specifically addressing this question are still scarce. Importantly, we found that the A−T+ group had similar tracer retention in temporal areas as the A−T− group, suggesting that an isolated elevation of CSF p-tau is not due to fibrillar tau pathology in the absence of Aβ pathology. However, it is important to note that to date, no convincing evidence has shown that tau-PET tracers are capable of capturing PART, which remains a lingering question for the field.Furthermore, discussing the etiology underlying a CSF A−T+ profile is highly needed. For instance, individuals presenting with this profile are sometimes referred to as suspected non-Alzheimers pathology. This construct is most often biomarker defined as Aβ negativity in the presence of neurodegeneration and a clinical picture not clearly consistent with a recognizable non-AD phenotype.39 Interestingly, in the UGOT and ADNI cohorts, concentrations of Aβ40 and Aβ42 were clearly higher in the A−T+ group, compared with the A−T− group. This could potentially suggest that the CSF A−T+ profile is indeed associated altered CSF dynamics, given that within 2 Aβ-negative groups as defined by the Aβ42/Aβ40 ratio, it would not be expected that Aβ42 and Aβ40 values alone would differ between groups.In this challenging context of determining the underpinnings of a CSF A−T+ profile, a potentially better-suited explanation is that the finding of increased p-tau in the absence of brain amyloidosis reflects slower CSF turnover. As an example of how alterations in CSF dynamics can impact biomarker readings, low concentrations of all core biomarkers  are commonly seen in cases of idiopathic normal pressure hydrocephalus, not necessarily associated with AD pathophysiology.40 However, when defining A−T+ with both Aβ− and tau-PET, individuals with this profile show slightly greater risk of cognitive decline than biomarker-negative individuals.41Data from both longitudinal cohorts suggest that individuals with an A−T+ pattern had similar cognitive and biomarker trajectories as compared with participants with a CSF A−T− profile. This aligns with recent findings in individuals with subjective cognitive decline that showed that the A−T+ group was not more likely to progress to MCI or dementia compared with the biomarker negative group when defining “A” using Aβ PET or Aβ42 and “T” using p-tau.23 and concordance with amyloid PET usually reaches over 90%,42 which in turn makes cutoff determination and interpretation uncomplicated. However, p-tau typically shows no such distribution and is less clearly linked with tau-PET status.43 Here, we show that both p-tau cutoff values that optimize sensitivity and specificity, as well as higher, less sensitive cutoff values presented very similar results in our study.Lastly, the choice of cutoff values may influence the interpretation of abnormal results. For Aβ42/40, this is likely of limited relevance because it usually presents a bimodal distributionStrengths of this study include the large sample size of participants with core CSF biomarkers of Aβ and soluble tau pathology, allowing us to derive estimated prevalence of these pathologies in a real-world, unselected clinical routine sample, for whom memory clinic physicians order most CSF tests. Further, our consistent finding that CSF A−T+ individuals do not show significant cognitive decline suggests that the findings are generalizable across different populations. Limitations include limited clinical information from the UGOT cohort, as the data are derived from a database used in laboratory practice. This precludes us from defining strict inclusion/exclusion criteria and describing the clinical features of the patients included. Another potential limitation is that the longitudinal cohorts consisted primarily of self-identified White individuals. Finally, AT status in the WISC sample was based on CSF tests not necessarily coinciding with the cognitive baseline visit.In this cohort study, we found the estimated prevalence of a CSF A−T+ profile to be 4.1% in a large, real-world, clinical routine data set. Further, individuals with this profile did not present with significantly higher rates of cognitive decline or AD biomarker progression than those of individuals with an A−T− profile. We suggest that practitioners encountering this pattern in daily clinical practice should interpret such a finding similarly to CSF biomarker-negative results during the diagnostic workup of patients with cognitive complaints, and unusually high CSF Aβ42 and Aβ40 concentrations could be a recognizable feature of such a profile.


Astragalus sinicus) (CMV), a globally important legume used as forage, green manure, and a rotation crop. Pot experiments were conducted to investigate the seedling emergence performance of 12 CMV seed lots under control, water stress, salinity stress, deep sowing, and cold stress conditions. Meanwhile, the field emergence performance was evaluated on two sowing dates in June and October 2022. Correlation and regression analyses were implemented to explore the relationships between hydrotime model parameters and seedling emergence performance under various environmental conditions. The seed germination percentage did not differ significantly between seed lots when seeds were incubated at 0.0 MPa, whereas it did differ significantly between seed lots at water potentials of −0.2, −0.4, and −0.6 MPa. The emergence percentage, seedling dry weight, and simplified vigor index also differed significantly between the 12 seed lots under various environmental conditions. Ψb(50) showed a significant correlation with germination and emergence performance under various environmental conditions; however, little correlation was observed between θH or σφb and germination and emergence. These results indicate that Ψb(50) can be used to estimate seed vigor and predict seedling emergence performance under diverse environmental conditions for CMV and similar forage legumes. This study will enable seed researchers, plant breeders, and government program directors to target higher seed vigor more effectively for forage legumes.Seed vigor is an important aspect of seed quality. High-vigor seeds show rapid and uniform germination and emerge well, especially under adverse environmental conditions. Here, we determined hydrotime model parameters by incubating seeds at different water potentials  in the laboratory, for 12 seed lots of Chinese milk vetch ( Astragalus sinicus L.) (CMV) is a globally important forage legume and is especially widely cultivated in East Asia [Chinese milk vetch  [Trifolium repens L.) [Lotus corniculatus L.) [The selection of high-quality CMV seeds is an efficient approach to optimizing seedling establishment and thus achieving a better livelihood and environment. Seed vigor is an important aspect of seed quality ; it is dtiva L.) , white cpens L.) , and biratus L.) . Thus, mThe standard germination (SG) experiment, performed according to International Seed Testing Association, is always used to estimate seed quality . NeverthθH), which is the hydrotime (MPa-hours) to germination; (2) the base water potential (Ψb(g)), which is the base or threshold water potential (MPa) defined for a specific germination fraction, g (%); and (3) germination uniformity (σφb), which is the standard deviation of Ψb(g) [Gossypium hirsutum L.) [Brassica napus L.) [Beat vulgaris L.) [θH or Ψb(g) [There are many ways to test seed vigor, including electrical conductivity , DNA repof Ψb(g) ,36. The of Ψb(g) ,38. In pof Ψb(g) , cotton utum L.) , rapeseeapus L.) , and sugaris L.) . It has or Ψb(g) ,44,45,46The seed germination percentages of 12 seed lots of CMV in response to different water potentials in a laboratory germination test are shown in In the non-water stress treatment (0.0 MPa), the germination percentages (standard germination) were high and similar, ranging from 85.6% (lot 2) to 95.6% (lot 12), for the 12 seed lots. In the −0.8 MPa treatment, the SGP also differed little between the 12 seed lots and all lots had values lower than 7%. With the exception of the 0.0 and −0.8 MPa treatments, the SGP differed significantly across the 12 seed lots. For example, in the −0.6 MPa treatment, the germination percentage was significantly affected by the seed lot and ranged from 17.8% (lot 2) to 56.7 (lot 12) .The germination rate and germination index were also significantly influenced by the seed lot in all water potential treatments except for −0.8 MPa, in which low and similar germination rates and germination indexes were observed .2r values of 0.783 to 0.917 , and σφb differed significantly among seed lots. Seed lot 2 had the highest estimated Ψb(50) (−0.278 MPa), and lot 12 had the lowest Ψb(50) (−0.522 MPa). The estimated value of θH of the 12 lots of CMV ranged from 8.799 to 14.770 MPa·h, and σφb varied from 0.267 to 0.333 . Meanwhile, SDW and SVI were significantly affected by the interaction effect between the seed lot and environmental conditions (p < 0.001)  .The dynamics of soil water content for control and water stress conditions during the pot experiment are shown in Overall, PEP, SDW, and SVI decreased significantly with water, salinity, and cold stresses and the deep-sowing treatment as compared to the control conditions and differed significantly across the 12 seed lots of CMV under each environmental condition . For ins−1 (lot 7), and 2318.6 (lot 2) to 5123.9 (lot 12), respectively, whereas the corresponding FEP, SDW, and SVI for the 12 seed lots in the second sowing ranged from 25.3 (lot 2) to 63.3% (lot 3), 21.4 (lot 2) to 48.6 mg plant−1 (lot 9), and 539.6 (lot 2) to 2946.4 (lot 12), respectively (There were significant differences in the field emergence percentage (FEP), SDW, and SVI in the two field sowings between the 12 CMV seed lots with similar SG . The obsectively .Ψb(50)  was significantly negatively correlated with the above vigor variables, with the correlation coefficients (r) and determination coefficients (2r) ranging from −0.600 to −0.972 and 0.36 to 0.95, respectively (Ψb(50) have higher seed germination and seedling emergence performance under a wide range of environmental conditions. θH (constant hydrotime) was significantly negatively correlated only with the germination rate (r = −0.702), germination index (r = −0.623), and PEP under cold stress (r = −0.630) (p < 0.05). In addition, σφb (standard deviation of Ψb(50)) was significantly positively associated only with SDW (r = 0.666) and SVI (r = 0.616) under salinity stress conditions, and SDW (r = 0.700) and SVI (r = 0.617) in the second sowing under field conditions (p < 0.05)  .Medicago polymorpha, Medicago truncatula, Medicago littoralis, Trifolium alexandrinum, and Trifolium michelianum [Vicia benghalensis L.).Seed germination and seedling emergence are important stages in the establishment of a plant species and the most sensitive ones in the plant’s life cycle under environmental conditions . In the helianum . In addihelianum . On the helianum  also repFestuca sinensis and Poa crymophila seed lots [In this study, we used 12 CMV seed lots collected from different commercial sources, and our results indicate wide variations in seedling emergence performance among these 12 seed lots under diverse environmental conditions, which effectively rank seed lots according to their actual vigor level. All seed lots used in the present experiment were from a single cultivar; thus, any differences in their germination and emergence patterns and stress tolerance were not due to their genotypes. Therefore, from a seed physiology perspective, the difference in the above traits between seed lots was most likely due to different vigor levels caused by seed aging and deterioration . From theed lots ,62. In oStipa spp. [Ceratonia siliqua [Jatropha curcas [Elymus trachycaulus (Link) Gould subsp. trachycaulus) [2r values ranging from 0.78 to 0.92 , and σφb [Ψb(50) generally had higher seed germination and seedling emergence performance under different environmental conditions than those with higher values of Ψb(50); for example, seed lot 12, with the lowest Ψb(50) (−0.522 MPa), also had the highest emergence performance in most cases. In contrast, seed lot 2, with the biggest Ψb(50) (−0.278 MPa), had the poorest seedling emergence under a wide range of environmental conditions (Ψb(g) generally had higher vigor than those with high values, and this allowed seeds to germinate rapidly in adverse environments. Furthermore, our study revealed very significant correlations between Ψb(50) and seed germination characteristics and seedling emergence performance under diverse conditions (Ψb(50). Therefore, Ψb(50) could be considered an ideal indicator when determining seed vigor and predicting emergence performance under various conditions. Accordingly, in the present study, the close association of Ψb(50) with seed vigor and seedling emergence performance is in agreement with the previous findings of Chen et al. [Lactuca sativa L.). Farzane and Soltani [Ψb(50) is significantly negatively related to the seed vigor of sugar beet.The hydrotime model has been widely used to describe germination dynamics in response to reduced water potential ,64,65, aipa spp. , Ceraton siliqua , Jatropha curcas , and sleycaulus) . Consist to 0.92 , Table 2 and σφb ,69. In onditions . This renditions , Table 4n et al.  in alfaln et al.  in cotton et al.  in rapesn et al.  in lettu Soltani  also sugθH and σφb and seed vigor. One probable reason is that a lower θH favors rapid and uniform germination, and this happens only when seeds are exposed to favorable conditions. According to the hydrotime definition, when the external water potential approaches or is lower than Ψb(50), seeds will accumulate hydrotime units very slowly, which overrides the advantage of low θH [Ψb(50) and θH was observed, which implies that these two parameters may act independently in regulating seed germination and seedling emergence under diverse conditions. Accordingly, Farzane and Soltani also indicated that Ψb(50) was the only parameter that was significantly related to seedling emergence and seed vigor [However, in the present experiment, a low correlation was observed between f low θH . In addied vigor .Samples of 12 seed lots of CMV were obtained from various commercial sources in China. For each lot, sufficient seeds were collected (in excess of 1000 g) to ensure the implementation of subsequent experiments. These seed lots had been produced over several years  from div−1) was calculated as previously described by Fallahi et al. [−1), Si is daily seed germination, Di is the number of days to n computations, and n is the number of computation days. The germination index was calculated using the following formula [Gt represents the number of germinated seeds on day t, and Dt represents germination days.In order to calculate hydrotime model parameters for each seed lot, a germination test for each CMV seed lot was carried out by incubating seeds at 20 °C in an 8 h/16 h light/dark cycle at water potentials of 0.0, −0.2, −0.4, −0.6, and −0.8 MPa. Water potentials were maintained with solutions of polyethylene glycol 6000 (PEG-6000) prepared according to Michel and Kaufmann . For eaci et al. :GR=∑i=1n formula :GI=∑GtDt−1) used in pot experiments was collected from local farmland and sieved through a 2 mm sieve to remove debris. The soil was dried at 120 °C for 24 h and then spread out for drying, followed by heating and homogenization. CMV seeds in 12 seed lots were sown in various environmental conditions, including control (irrigated with distilled water every four days), water stress (irrigated with distilled water every seven days), salinity stress (irrigated with 125 mM NaCl solution every four days), deep sowing  (5 cm), and cold stress (10 °C). The abiotic treatments for the control, water stress, salinity stress, and deep sowing were conducted in a greenhouse  on the campus of Qingdao Agricultural University , Shandong Province, China. The cold stress treatment was performed in a growth chamber. For each seed lot and treatment, three replicates of 30 seeds were sown at a depth of 1 cm (except for the deep-sowing treatment) in pots that contained 950 g of dry soil, which had been treated as described above. After sowing, 200 mL of distilled water or NaCl solution was used to irrigate each pot immediately and every four days during the period of the pot experiment . The pots containing soil and seeds were placed in a random arrangement on a table in the greenhouse or in the growth chamber, and the locations of pots were changed daily to avoid edge effects.Cylindrical plastic pots  were used in this part of the experiment. The clay loam soil  of wet soil weighed every day.To monitor the soil water content for the control and water stress treatments, two extra pots without seeds were irrigated under the same conditions as those in each irrigation regime of the experiment. These two pots were weighed daily using an electronic balance, and the soil water content was calculated according to the following formula :SWC%=Ww−The pot experiments lasted 30 days. At the end of the experiment, the number of emerged seedlings was counted in each pot, and the emergence percentage was calculated. All emerged seedlings in each pot were cut at the soil level and combined before being dried at 65 °C for 48 h and then weighed; the mean SDW was then calculated. The SVI was derived arithmetically by multiplying the emergence percentage with the SDW .The field emergence experiments were implemented at the Jiaozhou Experimental Base of Qingdao Agricultural University , Jiaozhou, Shandong Province, China. The mean annual temperature and mean annual precipitation were 13.8 °C and 686.0 mm, respectively. The field emergence experiment was conducted twice, once in June 2022 and once in October 2022. During each sowing, a randomized complete block design (RCBD) with three replications was used, and each replication contained 50 seeds. The seeds from each seed lot and each replication were hand-planted in a 1.5 m row, with 50 cm spaces between adjacent rows (seed lots). The seeds were sown at a depth of approximately 1 cm.The daily mean temperature and daily precipitation data during each experimental period were collected from local meteorological stations ; no irriAfter each field experiment, all emerged seedlings from each block and each seed lot were harvested at the ground level and amalgamated before being dried at 65 °C for 48 h and then weighed, and the mean SDW was calculated; the SVI was also calculated as described above.p < 0.05 probability level. Proportional data were arcsine-transformed before statistical analysis, and non-transformed data are reported in all tables and figures. All measurements reported are the mean of three replications. Pearson correlation and regression analyses were used to detect the relationships between hydrotime model parameters and the germination percentage, germination rate, germination index, and seedling emergence performance under various environmental conditions.All statistical analyses were performed using SPSS 26.0 software . Data for germination and emergence percentages in response to the seed lot and water potential were subjected to ANOVA. Significant differences between treatments or seed lots were compared using Duncan’s multiple comparison test at the Ψ is the actual water potential (MPa), θH is the hydrotime constant (MPa·h), Ψb(g) is the base water potential (MPa) defined for a specific germination fraction (g), and tg is the time (h) to germination of fraction g (%) of the seed lot.The hydrotime model is described by the following equation ,34,77:θHΨb(g) values among seeds in a population is characterized by its median Ψb(50) and standard deviation (σφb), which can be estimated using repeated probit analyses, varying θH until the best fit is reached [The normal distribution of  reached ,69,78 asθH, Ψb(50), and σφb). Additionally, different water potentials and environmental conditions also had a significant influence on seed germination and seedling emergence for each CMV seed lot. Seed germination and emergence characteristics significantly decreased under reduced water potential and abiotic stresses. In addition, the 12 seed lots also significantly differed in their seedling emergence under field conditions. Among the three hydrotime model parameters, correlation and regression analyses indicated that Ψb(50) was the most highly correlated with germination characteristics and seedling emergence performance under different environmental conditions. The lower the Ψb(50) of a lot, the higher its vigor. Thus, it can be concluded that Ψb(50) is an ideal trait to estimate CMV seed vigor and predict seedling emergence under diverse environmental conditions. These findings provide important parameters for seed vigor testing and early-warning signs for CMV seed storage. Future seed treatment and breeding programs for seed vigor could pay attention to reducing Ψb(50) to increase seed vigor and stress tolerance. Moreover, the evaluation of the relationship between stress tolerance characteristics and Ψb(50) might reveal similar significant associations in other plants.Overall, the present study clearly shows that the hydrotime model can describe the germination time course of CMV seed lots in response to different water potentials very well. The 12 seed lots of CMV differed significantly in their hydrotime model parameters (