Gm relaxation edges for biotic microcosms were lower, indicating less remaining free energy for Fe(II) oxidation than abiotic systems, with the implication that biologically-catalyzed reactions are likely more energetically efficient than abiotic reactions. These results strengthen the case for employing novel DFA techniques to distinguish in situ microbial metabolic activity from abiotic processes, as well as to potentially differentiate metabolisms among different chemoautotrophs.Differentiating biotic and abiotic processes in nature remains a persistent challenge, specifically in evaluating microbial contributions to geochemical processes through time. Building on previous work reporting that biologically-influenced systems exhibit stronger long-range correlation than abiotic systems, this study evaluated the relationship between long-range correlation of redox potential and oxidation rates of circumneutral microaerophilic bacterial Fe(II) oxidation using a series of batch microcosms with bacteriogenic iron oxides (BIOS). Initial detrended fluctuation analysis (DFA) scaling exponents of the abiotic microcosms were lower (ca. 1.20) than those of the biotic microcosms (ca. 1.80). As Fe(II) oxidation proceeded, correlation strength decayed as a logistic function of elapsed reaction time, exhibiting direct dependence on the free energy of reaction. Correlation strength for all microcosms decayed sharply from strong correlation to uncorrelated fluctuations. The decay rates are greater for abiotic microcosms than biotic microcosms. The Δ Among the multitude of redox reactions catalyzed by microorganisms, bacterial oxidation of Fe(II) is of considerable interest as Fe(II)/Fe(III) redox transformations are strongly coupled to the cycling of C, N, O, and S, as well as the environmental fate of trace elements, nutrients, and contaminants6. There is also a growing body of evidence implicating Fe(II) oxidizing bacteria (FeOB) as both causative agents in the formation of ancient banded iron formations on Earth and plausible ecophysiological analogues for life on other planets10. At the same time, the potential significance of these findings is tempered by a cautious appreciation of the fact that Fe(II) oxidation pathways in nature are exceptionally diverse and multifaceted, as concurrent abiotic and microbially-mediated reactions are apt to proceed in tandem with microaerobic, photochemical, and nitrate-dependent oxidation processes12. This reactivity extends additionally to nanoscale heterogenous oxidation processes occurring on bacterial cell and mineral surfaces13, further complicating these systems. Overall, distinguishing between biotic and abiotic processes in situ remains a persistent challenge in evaluating microbial contributions to biogeochemical processes10.Deciphering the complex interplay between microbially mediated and abiotic reactions in the biogeochemical cycling of redox active chemical species is crucial to understanding the planetary impact of microbial life2/H2O half-cell  promotes high rates of spontaneous abiotic oxidation of Fe(II) at atmospheric pO2 values and circumneutral pH range of most natural waters14. This constrains neutrophilic FeOB to low pO2 microaerophilic environments where the metabolic oxidation of Fe(II) can outcompete the kinetics of homogenous and heterogenous abiotic reactions16. Subsequent chemical hydrolysis and precipitation of dissolved Fe(III) often gives rise to voluminous mats of flocculent rust-coloured bacteriogenic iron oxides (BIOS) consisting of nanoparticulate hydrous ferric oxides (HFO) intermixed with live and dead bacterial cells19. The mineral precipitates occur as poorly ordered ferrihydrite21. Over time, the accumulation of BIOS serves to increase the relative proportion of heterogeneous surface-mediated abiotic reactions contributing to the abiotic oxidation of Fe(II), thereby confounding the contributions of FeOB11.The high standard potential of the O24. Inhibition of FeOB using sodium azide as a poison or sterilization by autoclaving clearly demonstrate that rates of bacterial Fe(II) oxidation are faster than abiotic homogenous and heterogenous rates under similar geochemical and low pO2 conditions. While informative, these results only hint at the underlying mechanisms and bioenergetic capacity of FeOB to catalyze otherwise spontaneous abiotic Fe(II) oxidation rates. More recently, the statistical dependence  of temporal fluctuations in electrochemical measurements of redox potential on a time scale of minutes to seconds were found to distinguish bacterial and abiotic Fe(II) oxidation25. Long-range correlation in a time series implies that any measured value is statistically dependent on preceding values, and can take the form of positive correlation (a past increasing trend will continue to increase into the future) or negative correlation (an increasing trend is likely to be followed by a decreasing trend)27. Such correlative dependence has proven to be consistently more pronounced for bacterial Fe(II) oxidation than abiotic Fe(II) oxidation25; thus, the parameter of correlation strength, measured by detrended fluctuation analysis (DFA) scaling exponents (α), can feasibly be used to distinguish microbially-catalyzed Fe(II) oxidation from homogenous and heterogeneous chemical oxidation. This new approach to investigating bacterial Fe(II) oxidation is compelling in light of the fact that the Gibbs free energy (ΔGr) of an oxidation-reduction reaction is an explicit thermodynamic function of redox potential. The implication is that ΔGr reveals not only which reactions are favored thermodynamically and exist as realistic bioenergetic options, but also constrains essential geochemical conditions, including pH, ionic strength, and concentrations of electron donors and acceptors, that influence the amount of available metabolic energy.The kinetics of Fe(II) oxidation by neutrophilic bacteria have been examined in a number of studiesGr) and correlation strength, we conducted a series of batch microcosm experiments in which Fe(II) oxidation rates and relative concentrations of Fe(II)/Fe(III) changed systematically over time, and experimentally determined the strength of long-range temporal correlation in redox potential fluctuations at several stages of elapsed reaction time. Our results confirm that long-range correlation of redox potential fluctuations in biologically-influenced systems is stronger than long-range correlation in abiotic systems. Furthermore, we discovered that correlation strength dissipates as a logistic function of time and exhibits direct electrochemical dependence on free energies of reaction.To investigate the relationship between  oxidation kinetics. Estimates for kox ranged from 0.004 to 0.012 min−1 for the Control and Autoclaved microcosms and 0.039 to 0.141 min−1 for the Low BIOS and High BIOS microcosms, respectively. The temperature of the Autoclaved microcosm at 20.95 °C was slightly warmer than the others (ca. 16.00 °C), which is a result of cooling this sample to ambient temperature after autoclaving. Relative to the other microcosms, it is possible that the higher temperature of the Autoclaved microcosm contributed to a kox value higher by a factor of approximately 3, based on the Arrhenius relationship for the temperature dependence of reaction rates, and a reported activation energy of 134 kJ mol−1 for Fe(II) oxidation24.The oxidation of Fe(II) in the microcosms followed pseudo-first order reaction kinetics, wherein relative Fe(II) concentrations decrease exponentially over time according tot = 0 in the Low and High BIOS microcosms (ca. 1.80) were evidently higher than those for the Control and Autoclaved BIOS microcosms (ca. 1.20) .The initial scaling exponents calculated for DFA start times of ms ca. 1.0  than those determined for the Low and High BIOS microcosms (ca. 0.75 min−1), indicating that long-range correlations in redox potential may persist longer in bacterial mediated Fe(II) oxidation in comparison to homogenous and heterogenous abiotic reactions. Crossover threshold times were shortest for slow rates of Fe(II) oxidation in the Control and Autoclaved microcosms, as well as high rates of Fe(II) oxidation in the High BIOS microcosm. In remarkable contrast, the longest crossover threshold time was obtained in the Low BIOS microcosm with intermediate Fe(II) oxidation rates.As the oxidation of Fe(II) advanced with time, α value as,G0 = −46.0 kJ mol−1 for the combined Fe(II)/Fe(III) and O2/H20 half-cell reactions corrected from 25 °C to the measured temperature of the Control, Low BIOS, and High BIOS microcosms using the van’t Hoff equation29; at the temperature of the Autoclaved microcosm, ΔG0 = −45.1 kJ mol−1. Concentrations of Fe(II) were determined using Eq. Gibbs energies for the oxidation of Fe(II) in the microcosms were calculated at elapsed reaction times corresponding to each mentclass2pt{minim29.Concentrations were corrected to activities using the Davies equationGr values for Fe(II) oxidation as the independent variable instead of time are shown in Fig. Gm is the change in free energy at the inflection midpoint. Unlike the time course results, this representation of the microcosm data displays a clear distinction between the controls and BIOS experiments. Overall, there is good agreement between αmin and αmax estimates obtained from nonlinear regressions of time course and free energy data in Eq. βGrΔ decay constants for the Control and Autoclaved microcosms were slightly greater (ca. 2.25 mol kJ−1) than the Low and High BIOS microcosms (ca. 1.40 mol kJ−1). A more striking feature is the similarity in the ΔGm crossover thresholds of −20.12 and −14.90 kJ mol−1 for the Control and Autoclaved microcosms, and particularly the ΔGm values of −11.84 and −11.24 kJ mol−1 for the Low and High BIOS microcosms, respectively. The ΔGm values are virtually the same as ΔGr calculated on the basis of corresponding crossover threshold times  oxidation redox potential fluctuations and reaction free energy, but also the clear distinction between chemical and biological Fe-based redox reactions.Plots for the logistic decay of microcosm DFA scaling exponents in Eq. es Table , which ckox) estimates in this study (0.004 to 0.141 min−1) are in concordance with those determined from previous microcosm investigations, which span from 0.016 to 0.249 min−1 23. Additionally, kox estimates of 0.141 min−1 for the High BIOS microcosm are about three times that of the Low BIOS microcosm  and Autoclaved (0.012 min−1) microcosms. These differences re-emphasize the ability of FeOB to outcompete the kinetics of abiotic homogeneous oxidation of Fe(II) in solution, as well as heterogenous Fe(II) oxidation on HFO precipitates23.The range of Fe(II) oxidation rate constant (α = 0.5) as rates of Fe(II) oxidation declined over time towards equilibrium in each of the closed batch microcosm. The logistic decay rate constants of scaling exponents for redox potential fluctuations were also more than twice as fast in the Control and Autoclaved microcosms than in their BIOS counterparts, demonstrating that long-range correlations in redox potential fluctuations tend to be more persistent for bacterial Fe(II) oxidation than abiotic reactions. This was particularly evident in the case of the Low BIOS microcosm, which exhibited the longest crossover threshold time of 8.28 min for the transition in from long-range correlation to random uncorrelated fluctuations in redox potential. On the other hand, the High BIOS microcosm displayed not only the shortest crossover threshold time, but also the highest rates of Fe(II) oxidation with consumption of Fe(II) and production of Fe(III) driving towards equilibrium faster than in the Low BIOS microcosm. These counterintuitive results are, in fact, consistent with the dependence of redox reactions on the mutual diffusion and collision frequency of reactants to form close-encounter complexes, with short separation distances more favorable to electron transfers30. If the redox reaction rate is high, as is expected under conditions far from equilibrium, then diffusion may become limiting. This situation is consistent with the initial stages of the microcosm experiments when fast Fe(II) oxidation rates yielded DFA scaling exponents symptomatic of long-range correlation in redox potential fluctuations . Conversely, diffusion constraints are relieved by slower reaction rates, which diminished over time in the microcosms with a shift to random uncorrelated fluctuations in redox potential as Fe(II) concentrations decreased. Specifically, at the comparable crossover threshold times and sluggish rates of abiotic Fe(II) oxidation in the Control and Autoclaved microcosms, Fe(II) concentrations calculated from Eq. 30.In keeping with previous work, the initial elevated DFA scaling exponents in the Low and High BIOS microcosms confirm that higher rates of bacterial catalyzed Fe(II) oxidation gives rise to stronger long-range correlations in redox potential fluctuations than slower abiotic reactions. As anticipated from these observations, the long-range correlations dissipated to a random uncorrelated state  oxidation. The existence of long-range correlations in redox potential fluctuations under conditions far from electrochemical equilibrium  and terminal electron acceptors in natural systems38. In this regard, the potential for using DFA to detect long-range correlation strength of redox potential fluctuations associated with bacterial metabolisms other than chemoautotrophic Fe(II) oxidation remains virtually unexplored and ripe for investigation. Recent estimates of the number of undiscovered microbial species living in refugia and ephemerally on Earth underscore the importance of developing new research methods which are not reliant on physical access and sampling38. The in situ nature of the electrochemical DFA method makes it a potentially revolutionary tool in furthering our understanding of microbial contributions to important biogeochemical processes such as Fe(II) oxidation that mediate the flow of energy and matter across our planet’s complex interconnected systems40.Altogether these findings constitute a significant breakthrough in deciphering the complex dynamics of microbial Fe(II) oxidizing systems in natural systems through the fluctuation analysis-based differentiation of biotic and abiotic reaction pathways. While Fe(II) oxidation is an important redox transformation that is tied closely to the biogeochemical cycling of many key elements including C, N, O, and S23. The discharge zone is characterized by a thick, rust-coloured flocculent BIOS mat, consisting of copious amounts of filamentous Leptothrix ochracea bacterial sheaths and less abundant helical Gallionella ferruginea stalks intermixed with Fe-oxyhydroxide mineral precipitates23.This investigation was conducted at Ogilvie Creek , approximately 10 km west of Deep River in the municipality of Laurentian Hills, Ontario, Canada. The creek is fed by surface outflow of a small lake, as well as discharge from an anoxic chalybeate groundwater spring that drains over a shallow 3.0 m reach into the main stream channel23, a series of four individual 0.72 L batch microcosms were set up in 1.5 L plastic bottles as follows: (i) a chemical control consisting of 0.22 μm filtered creek water, measuring homogenous, abiotic Fe(II) oxidation (Control); (ii) a sterilized control with 310 mg/L of autoclaved BIOS (Autoclaved) and 540 mL of 0.22 μm filtered creek water, measuring autocatalytic Fe(II) oxidation; and two live microcosms, (iii) 330 mg L−1 (Low BIOS); and (iv) 1000 mg L−1 (High BIOS). The dry weight BIOS concentrations were determined at the end of each experiment by drying microcosm contents at 70 °C to a constant weight. Microcosms were filled gently, with syringes emptied slowly and below the liquid surface to minimize the exposure of the BIOS and water to the atmosphere.Samples of BIOS and water were collected separately in several sterile 60 mL syringes approximately 2.0 m downgradient from the spring source. Following the same protocol to replicate previous experiments on kinetics of bacterial Fe(II) oxidation2 (DO) were recorded using a YSI 600 QS sonde. Dissolved Fe(II) concentrations were measured in triplicate samples taken at 10- to 30-minute intervals on 0.22 μm filtered aliquots using a HACH DR/890 colorimeter with HACH Ferrous Iron reagent. The standard errors of average Fe(II) concentrations for the triplicate samples were typically <±10.0%. To assess redox potential fluctuations, open-circuit voltage across a Pt | Ag-AgCl combination electrode was measured continuously at a sampling frequency of 10 Hz  for the entire duration of each microcosm. The voltage measurements were recorded using a National Instruments data acquisition device (DAQ) controlled by a laptop running LabVIEW 6.0.Microcosm temperatures, pH, and dissolved Ot = 0 Fe(II) concentrations to account for variations between the microcosms, then fit to Eq. kox. The same fitting routine was used to derive parameter estimates and standard errors for logistic decay of characteristic power law scaling exponents  in Eq. Measured microcosm Fe(II) concentrations over time were normalized to starting (i.e., 25. The original 10 Hz time series for each DFA start time were down-sampled from 10 Hz to 1 Hz realizations and truncated to a standard window lengths of 1000 s before analysis. The set of scaling exponent estimates was subsequently averaged over all 10 realizations for the same standardized time interval. In order to compare changes in correlation strength with changes in oxidation rate over elapsed time in the microcosms, scaling exponents were calculated sequentially at one-minute intervals with index, i, for the first 27 minutes of elapsed time in each microcosm corresponding to DFA start times DFA was implemented in MatLAB, as described previously


Data shows quantification of a total of sixty-five elements and is reported for a total of eight digested samples. Quantification of 6Li/7Li isotopic ratio is reported for a total of eight digested samples.This article shows the elemental analysis of a batch of FLiBe prepared from LiF and BeF Specifications tableValue of the data•Data can be used as a baseline for elemental composition of purified FLiBe, relevant to groups studying chemical or thermophysical properties of FLiBe•Data can be used as partial evidence indicating the effectiveness of hydro-fluorination as a purification step of FLiBe.•Data presented here can be relevant to groups investigating aqueous digestion methods for FLiBe.•Data presented here can be relevant to design of nuclear reactors that use FLiBe as a coolant or fuel solvent.•Data presented here can serve as a benchmark for ICP-MS measurement sensitivity for the set of elements that were analyzed here.1The ICP-MS data is reported in 22.1A 1 kg batch of FLiBe was transferred in liquid form to a nickel vessel , from a 57 kg FLiBe batch purified by hydrofluorination and reduced with hydrogen sparging. The detailed purification procedure was reported by Kelleher et al. 2.2The solid salt sample of ~0.5 g was ground using a small agate mortar and pestle within a fume-hood, with the goal of relatively uniform particle size; very fine powdering is not necessary for the microwave digestion. The duration of the grinding process is on the order of 10 min. Samples of < 100 mg were taken from this powder for digestion; the weight of each digested powder sample is reported in Table 1•Acidic solution with HF: 7.00 mL 16 M Nitric Acid, 3.00 mL 12 M Hydrochloric Acid, 2.00 mL 28 M Hydrofluoric Acid and 3.00 mL MQ.•Acidic solution without HF: 8.00 mL 16 M Nitric Acid, 4.00 mL 12 M Hydrochloric Acid, and 3.00 mL MQ.Each powder sample of FLiBe was dissolved in 2.00 mL of Millipore high-purity water and digested with one of the following two acidic solutions:This digestion was topped up to 50.00 mL with water. Microwave-aided Teflon-bomb digestion was performed using Milestone Ethos EZ instrument with SK12 and PRO24 microwave rotors. Details on the methodology can be found in Samples for each ICP-MS batch comprised of: NIST SRM Marine Sediment 2.3Samples were run on a Thermo-Fisher Scientific Element 2 instrument (formerly Thermo-Finnigan). To eliminate spectral interferences, ICP-MS instrument settings were adjusted among low resolution (LR), medium resolution (MR), and high resolution (HR). The instrument setting used, and the isotope analyzed for each element are indicated in The ICP-MS measures ion counts per second. Each specific isotope has slightly different transmission characteristics in the mass spectrometer (i.e. sensitivity), therefore a mass bias correction is applied. This bias also changes slightly during a run due to cone aging and other effects. The absolute bias and relative bias are corrected by a "standard-sample-standard-sample" approach: known standards are run after each sample; the measured ratios in these standards are used to correct the bias in the samples. The standard deviation for the concentration values is based on three measured values, from three ion count data blocks. Each ion count data block is typically based on 30 ion counts. For potassium, acid-matrix matched calibration checks were used, using relevant potassium levels at frequent locations in the analytical sequence.For isotope ratio measurements, the mass spectrometer is run in a specific mode: the magnet is fixed on a mass between the two isotopes of interest and the electrostatic analyzer is rapidly scanned between those isotopes and data for 10 scans are collected. The ion counts at each isotope in each scan are baseline (blank) corrected and then isotope ratio is computed. The average of these 10 ratios is then calculated. This is called a single block of data. This whole process is repeated 9 more times to generate 10 blocks of data, each with an average value. The average (and standard deviation or standard error) of the 10 averages is what is used to generate the final isotope ratio value.3) and is directly traceable to NIST SRM 3129. We note that the HPS and NIST standards are certified for concentration, but they are not certified for isotope ratios. The NIST standard that is certified for Li isotope ratios  has been unavailable for more than a year; it has a true absolute abundance of 0.08215 (mol/mol), identical to the value we measured for the HPS standard, which was 0.0821 ± 0.0002 (mol/mol).The Li standard used to compare isotopic ratios was procured from High Purity Standards (HPS), product number 100029-1 (1000 µg/mL Li in 1% HNO


Hordeum vulgare L.) have been described and several of these are known to have lesions in starch biosynthesis genes. Here we confirm that one type of shrunken endosperm mutant, lys3  has an additional phenotype: as well as shrunken endosperm it also has enlarged embryos. The lys3 embryos have a dry weight that is 50–150% larger than normal. Observations of developing lys3 embryos suggest that they undergo a form of premature germination and the mature lys3 grains show reduced dormancy. In many respects, the phenotype of barley lys3 is similar to that of rice GIANT EMBRYO mutants (affected in the OsGE gene). However, the barley orthologue of OsGE is located on a different chromosome from Lys3. Together these results suggest that the gene underlying Lys3 is unlikely to encode a starch biosynthesis protein but rather a protein influencing grain development.Many shrunken endosperm mutants of barley ( This wo (lys3a) .lys mutants have low-starch content and shrivelled  grains. Numerous other barley mutants  have been identified primarily because of their shrunken endosperm but these also have high-lysine content. Thus, the lys mutants are a sub-group of a larger set of shrunken endosperm mutants that display high-lysine and low-endosperm-starch phenotypes. Several of the shrunken endosperm mutants of barley have been found to carry lesions in starch synthesis genes  , larger-Oryza sativaL.), there is a GIANT EMBRYO (GE) mutant that, like some of the Risø barley mutants, also has enlarged scutellar cells, reduced endosperm size  have reduced dormancy and a predisposition towards preharvest sprouting . It is nlys3a mutant, Risø1508 and three additional barley mutants with lesions at the lys3 locus: Risø18 (lys3b), Risø19 (lys3c) and M1460 (lys3d) (lys3 could be due to disruption of the barley orthologue of CYP78A13 (HvGE). Our results suggest that the Lys3 gene is distinct from HvGE and that it represents a novel gene controlling embryo size and grain development in barley.To investigate these matters further, we studied the relationship between embryo and endosperm size in a collection of shrunken endosperm barley mutants . This le (lys3d) . We also22.1; USDA-ARS National Small Grains Collection, Idaho, USA ; Nordic Gene Bank, Alnarp, Norway (Risø29 and Risø17); Tom Blake, Montana State University, USA (Nubet and Franubet); Birthe Møller Jespersen, University of Copenhagen, Denmark  and Alan Schulman, University of Helsinki, Finland (Shx). All of the mutants in the collection show seed-shrivelling under the control of a single recessive locus.The collection of low-starch barley mutants used here  was asse2.2Barley was germinated by placing individual seeds directly into the wells of a 24-well seed-tray containing a peat and sand mix. Once the seeds had germinated and the seedlings were established (two to three weeks), each seedling was transplanted into a 1 L pot containing barley compost mix , 100 L perlite, 200 L 4-mm grit, 1.6 kg Osmocote® .−1 m−2 at ear height.Barley was grown either in a glasshouse or a controlled environment room (CER). In the glasshouse in winter, additional lighting was provided by sodium lamps for 16 h per day and temperatures were maintained between 15 °C (night) and 20 °C (day). Barley grown in CER had conditions of 16 h light (15 °C) and 8 h (12 °C) darkness, 80 percent humidity and quantum irradiance of 300–350 μmol s2.3Time after flowering was used to determine the age of the grains. Anthesis occurs while the ear is enveloped in the flag leaf so the exact day of anthesis is difficult to determine without damaging the plant. Accordingly, flowering time was defined as the day on which the awns of the developing ear protruded more than 1 cm above the leaf sheath.2.4To remove embryos from mature grains they were first imbibed overnight in sterile water at 4 °C in the dark before the embryos were removed, dried to constant weight and weighed (dry weight).2.5Mature, fully-dried primary spikes of barley were subjected to X-ray μCT scanning and analysis as described in 2.6Samples were extracted by grinding thoroughly for 90 s at 1500 rpm in 0.5 mL 0.77 M ice-cold perchloric acid in a Genogrinder®  using 1.5 mL polyvinyl tubes and stainless steel balls 3/8 inches in diameter. Following grinding, a further 1 mL of perchloric acid was added to the homogenate which was allowed to fully-acidify for 30 min on ice. The insoluble material, including starch, was separated from the soluble metabolites, including sugars, by centrifugation at 20 000 g for 10 min at 4 °C.For embryo carbohydrate extraction, the supernatant was removed and the pellet re-suspended in 100 μL of sterile water. Following another centrifugation step as above, the second supernatant was added to the first.The supernatants were used to measure glucose, fructose and sucrose. The pellets were assayed for starch as follows. Embryo samples: pellets were re-suspended in 250 μL of sterile water from which four representative aliquots were taken (two were digested and two were used as undigested controls). Endosperm samples: to remove excess soluble carbohydrate contaminants from the insoluble pellet, the pellet was washed by re-suspension in 1 mL of sterile water, followed by two re-suspensions in 1 mL of 80 percent ethanol solution. Finally, the pellet was re-suspended in 1 mL of sterile water from which four representative aliquots were taken for starch assay (two were digested and two were used as undigested controls).2.7Aspergillus oryzae, manufactured by Megazyme Ltd., Co. Wicklow, Ireland) and the glucose measured enzymatically as described in Samples were autoclaved for 20 min at 120 °C to solubilise the starch. Starch was digested to glucose using α-amylase and amyloglucosidase , centrifuged at 12 000 g to remove the insoluble potassium perchlorate and glucose, fructose and sucrose were measured enzymatically as described in 2.9A single freeze-dried embryo (1.0–6.0 mg) was thoroughly ground, for 90 s at 1500 bpm, in 500 μL of sterile water in the Genogrinder® . Then 500 μL of protein extraction buffer  SDS, 2 mM DTT) was added and proteins were allowed to dissolve for 30 min at room temperature with continuous rocking. Samples were then centrifuged for 5 min at 12 000 g before the supernatant was assayed using a BCA (bicinchoninic acid) protein assay kit  according to the manufacturer's instructions and using BSA standards.2.10To study dormancy and after-ripening, plants grown in controlled environmental conditions were allowed to dry for several weeks without watering prior to harvest. Grain from the primary tillers were stored at room temperature in the dark prior to germination trials. To assess dormancy, germination rate was measured each week for four weeks after harvest. Germination trials were as follows: grains were allowed to imbibe/germinate on moist filter paper at 17 °C in the dark. After five days, the number of grains with a visible radicle was scored.Moisture content of grains during the after-ripening period was measured as follows: samples of 20 grains were weighed, incubated at 80 °C for >48 h and re-weighed. The loss of weight during incubation was taken to indicate moisture content.Germination rate was also measured in grains more than 5 weeks after harvest, when the grains were assumed to be no longer dormant. Grains were allowed to imbibe/germinate on moist filter paper at 17 °C in the dark. The germination rate was measured at intervals for 22 h after imbibition. Germinated grains were scored when the radicle was first visible outside the hull. Root and shoot length were measured five days after imbibition.2.11DNA was prepared from seedling leaves using a DNeasy Plant Mini Kit (QIAGEN) according to the manufacturer's instructions.2.12GE orthologue and to discriminate against the wheat (cv. Mardler) orthologous homoeologues. The primers were HvGE forward 5′-AGGCGTACGTCGGTAACATC-3'and HvGE reverse 5′- TCGGTATGCTTGTAGAATGTAGAG-3’. All PCR reactions were performed using Phusion Hot Start II high-fidelity DNA polymerase (Thermo Scientific) according to the manufacturer's instructions. The PCR thermal cycler was programmed as follows: initial denaturing step of 2 min at 94 °C; 35 cycles of 30 s at 94 °C, annealing for 30 s at 45–60 °C and extension at 72 °C for 30 s kb−1; and final extension step of 72 °C for 10 min. PCR products were separated on a 1.5% agarose gel and visualised by ethidium-bromide staining under UV light.PCR primers were designed to amplify the barley (cv. Betzes) 33.1lys3a) has larger-than-normal embryos. To test whether the other lys3 mutants also have large embryos, all four mutants and their wild-type controls were grown together and the mature grains were visually examined . Given the previously reported recessive nature of the lys3mutants investigated here, this showed that lesions at the Lys3 locus are almost certainly responsible for the large-embryo phenotype. The identity and origin of the Risø19NE grains is not known. The grain size of Risø19NE is lower than normal , and those in which it is determined by the maternal genotype (seg mutants: shrunken endosperm genetic). So far, the genes responsible for only five of the twenty mutant lines in Nst1 - plastidial ADPglucose transporter, Agps1 – small subunit of ADPglucose pyrophosphorylase, Isa1 – isoamylase, Amo1 – starch synthase IIIa, Flo6 – controlling starch granule size and number) (We investigated whether shrunken endosperm barley mutants commonly have enlarged embryos by examining a selection of twenty shrunken endosperm mutants . As well number) .lys3 mutants. The embryo weights of the lys3 mutants ranged from 160% of wild type for Risø19LE to 247% for M1460. The largest increase in embryo weight amongst the shrunken endosperm mutants other than lys3 was 118% for Notch 2 (an isoamylase mutant), which has the smallest grain size of all the mutants studied. These data suggest that 1) most of the mutants with reduced grain size have both reduced endosperm size and reduced embryo size and 2) all four of the lys3 mutants were clear outliers with respect to increased embryo weight. Thus, the large-embryo phenotype is not a common response to reduction in endosperm growth. It is however, a particular feature of mutants with lesions at the Lys3 locus.Measurements showed that, as expected, all of the selected mutant lines had lower grain weights than their respective controls A. Of the3.3lys3a mutants were studied further by μCT scanning . Additionally, many of the Risø1508 mutant embryos were seen to have a distinct shape and texture  than that of the control at the earliest time-point examined: 20 days after flowering (DAF). In Risø1508, embryo weight was significantly higher than that of the control at 25 DAF . Thus, embryo size is affected earlier and to a greater extent in M1460 than in Risø1508. As Risø1508 and M1460 have differing genetic backgrounds, it is not possible from these data alone to determine whether the differences in phenotype are due to variation between these two lys3 alleles or to effects of genetic background. The ratio of fresh-to-dry embryo weight changed during development but this was not affected by the lys3 mutations, showing that the increase in embryo fresh weight in the lys3 mutants was accompanied by a proportional increase in embryo dry weight (data not shown).To determine when during grain development the large-embryo phenotype becomes apparent, two of the r detail . The frer detail A. However detail B. In M143.5The carbohydrate and protein contents of developing embryos of Risø1508 and/or M1460, and their wild-type controls were measured. The starch content of Risø1508 embryos was slightly higher than that of the wild-type control, Bomi throughout development C–F. The lys3 mutant lines were compared with those of the other shrunken-endosperm mutants and their respective wild-type controls (lys3 mutants Risø1508 (28 percent lower), Risø19 (50 percent lower) and M1460 (36 percent lower), and in the isoamylase mutant, Notch 2 and the ADPglucose transporter mutant, Risø86.The protein contents of mature embryos for all four controls C. None o3.6lys3 mutant, Risø1508 and its wild-type control, Bomi  at five days after imbibition showed that there was no statistically-significant difference in root length between mutant and wild type B. Howevelys3 mutant, Risø1508 was significantly less dormant than normal during the first two weeks after harvest. By four weeks after harvest, dormancy had been broken in both mutant and wild type. Moisture content can modify the imposition of dormancy during grain maturation and the breaking of dormancy during dry after-ripening. However, reduced dormancy in Risø1508 was not associated with altered grain moisture content . At maturity, a range of whole-plant metrics and yield components were measured  and unfavourable (yield depression) traits. This would potentially allow more effective utilisation of the lys3 trait in barley and in other cereals. As a first step towards gene identification, we considered whether the Lys3 gene might be orthologous to the gene responsible for the GIANT EMBRYO (GE) mutant phenotype in rice.Identification of the gene underlying EnsemblPlants (http://plants.ensembl.org), the barley orthologue of the rice Giant Embryo (GE) gene (LOC_Os07g41240/ Os07g0603700) is HORVU2Hr1G032890. Thus, from the latest genome sequence information for barley, HvGE is located on chromosome 2H whereas Lys3 is genetically mapped on chromosome 5H  elongation, normal spike elongation as well as normal grain development, dormancy and germination. Lys3 appears not to be required for normal vegetative growth although a reduction in straw height in Risø1508 compared to Bomi was described previously (The effects of mutations at the eviously .lys3 grains, we observed an alteration in the appearance of the mature embryos using μCT scanning. Compared to the wild-type embryos, the mutant coleoptiles were frequently larger and contained leaves that were well-separated from each other. Together this suggests that in lys3 grains, the normal dormancy mechanism is broken and that the mutant grains may undergo a form of premature germination, as suggested by lys3 could therefore be due to loss of a cell cycle inhibitor early in grain development or to a defect in the dormancy pathway, or both.As well as a reduction in dormancy in lys3 mutants appears to be particularly severe. The extent of embryo enlargement observed in lys3 mutants sets them apart from other shrunken endosperm mutants, including those with mutations directly affecting starch biosynthesis in the endosperm. Whilst it is possible that the reduced post-harvest dormancy observed in Risø1508 may be, at least in part, due to reduced induction of primary dormancy as a result of reduced starch accumulation in lys3 mutants, it seems likely, given the abnormal phytohormone levels associated with this mutation, that lys3 has a direct effect on overall grain development, rather than a primary effect restricted to starch synthesis. These ideas are also supported by the observation that the endosperm of lys3 mutants is irregular and often contains void spaces, indicating that the choreography of development is impaired.A predisposition towards premature germination appears to be a common feature of barley mutants with reduced endosperm starch content , and canLys3 gene will be required to determine its mechanism of action and whether or not it acts together with HvGE to control embryo size perhaps via regulation of phytohormone levels. Efforts to identify the Lys3 gene by genetic mapping are currently underway in our lab.Identification of the None.


Synchrotron X-ray Powder Diffraction and X-ray Tomography were used to identify the dominant corrosion products and measure their dimensions. Uranium dioxide was observed as the dominant corrosion product and time dependent thickness measurements were used to calculate oxidation rates. The effectiveness of physical and chemical grout properties to uranium corrosion and mobilisation is discussed and Inductively Coupled Plasma Mass Spectrometry was used to measure  In a prequel to this study2, we investigated the corrosion behaviour of uranium encapsulated in grout and exposed to water vapour, reflecting conditions during surface storage of the waste. We refer the reader to this paper for further background information and sample history. From the results of this paper, we concluded that under water vapour conditions uranium metal corrodes via the anoxic oxidation reaction displayed in Equation 5, we also found the ingress of oxidising species from the water vapour environment was limited by the reducing permeability of the progressively hydrating grout. As a result, the uranium oxidation rate was observed as initially rapid, but decreased over time, and near 50 weeks plateaued at a rate believed to reflect the steady state exchange of gases through the near-matured grout.Uranium metal is a significant contributor to Intermediate Level nuclear Waste (ILW) at Sellafield, UK. Along with other reactive metals such as Magnox cladding and aluminium, the uranium is encapsulated in a Blast Furnace Slag (BFS) and Ordinary Portland Cement (OPC) mixed grout and stored in stainless steel containers above ground, awaiting disposal via the potential development of a Geological Disposal Facility (GDF)3) may form. This compound is a black powder which has been known to spontaneously ignite in oxidising conditions7 and, in addition to hydrogen gas, significantly increases the risk for future transportation, storage and disposal of the ILW containers.These findings are important because Equation 8. Temperatures are expected to rise to 80 °C as a result of curing cementitious materials and in time, anoxic and chlorinated groundwaters are expected to ingress, progressively saturating the entire facility3. Although now buried underground, the risks here are potentially forming UH3 stores (which could later be accidently discovered), and aqueous release of uranium into the surrounding environment if the steel containment fails.The UK’s current plan is to build a GDF for the disposal of nuclear waste. To ensure the safe containment of such material, the change in environmental conditions must be considered in predictive risk modelling. On completion and after loading the waste, the GDF vault will most likely be backfilled with an OPC based material, thus preventing further reaction with atmospheric gases10. However, uranium mobilisation can be mediated through complexation with strongly oxidising ligands in the aqueous phase  or kept within solution by weaker complexation ligands (e.g. hydroxyl ions13)14. Singularly, these processes are complex and dependent on a multitude of environmental conditions, each of which must be investigated.The encasing grout of an ILW package performs as both a chemical and physical barrier against uranium release into the surrounding environment. It is therefore important to understand the potential mechanisms of uranium immobilisation and mobilisation within the grout matrix and ultimately the factors which affect them. Immobilisation of uranium in grout can occur through precipitation of a salt, solid solution (adsorption and incorporation of uranium into grout hydration phases) and simple sorption to hydrated grout surfaces through electrostatic surface complexation or ion exchange2O diagram presented by Sutton15, under high pH and low Eh conditions uranium corrosion tends towards oxidation to UO2. However, the effect of grout on the U-H2O system and furthermore the dominant corrosion products of uranium and their rate of formation in these conditions are not well known. In the following experiment we address this problem by performing an in-situ study of uranium corrosion in grout under full water submersion conditions, in an attempt to reflect the main features of a saturated GDF. Synchrotron x-ray powder diffraction (XRPD) and tomography (XRT) were used to identify and analyse the morphology and location of the dominant uranium corrosion products over a 47 week time period. We then used Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to measure the concentration of aqueous uranium species in residual waters.Pourbaix diagrams provide a good indication of the thermodynamically stable species of uranium under various aqueous pH and Eh conditions and according to the U-HA series of uranium rods (0.5 mm × 0.5 mm × 20 mm in dimension) were encapsulated in an BFS:OPC grout, then fully submersed in de-ionised water for 2 to 47 weeks. Each sample was analysed on the I12, Joint Engineering, Environment and Processing (JEEP) beam line, at the Diamond Light Source (DLS) over two separate beam times. In total 7 samples were analysed, which were split into two groups as shown in Table Before encapsulation in grout, three samples retained an as-received corrosion layer on the metal surface thereby reflecting uranium fuel which is pre-corroded before waste packaging   also detected UO2 in addition to carbon, therefore these morphological features are most likely attributed to UO2 formation during previous localised water corrosion. Between corrosion masses, the formation of a uniform corrosion layer indicated typical UO2 development.Sample A3L showed an irregular surface, but no significant difference between the uranium metal Fig.  and corr3 formation which were small and scarce enough to prevent XRPD detection. Further investigations are required to ascertain this.Sample A47L appeared considerably more corroded than when previously analysed as A3L Fig. . Before The nitric acid etched samples also retained some as-received metal features Fig. . For exa2. (2) Samples N6L, N22L and A47L showed accelerated corrosion and also displayed grout fracturing. Thus, the occurrence of grout failure appeared independent of the time allowed for corrosion.Figure −1).The pH, Eh and DO content measured from the residual water of each sample were investigated and discussed online in the supplementary material in Figs 238U in the residual waters of samples N2L, A6L and A47L are displayed in Fig. 238U increased by an order of magnitude, demonstrating increased mobilisation of uranium within the grout. However, as previously mentioned sample A47L exhibited grout fracturing which may have enhanced uranium mobilisation by directly exposing the metal and UO2 to water.The concentration of 2 was considered the dominant corrosion product forming on both the nitric acid etched and as-received uranium metal. It was evident that UO2 growth increased over time, with some samples exhibiting accelerated corrosion in comparison to others. Excluding sample A47L, no evidence of uranium hydride formation was present over the 47-week period. Further analysis of sample A47L is required to identify the cause of small localised growths, which may either be indicative of uranium hydride or unidentified as-received corrosion.We examined the corrosion behaviour of uranium encapsulated in grout after full submersion in de-ionised water. Each sample was analysed separately using synchrotron XRT and XRPD to identify and determine time dependent development of corrosion product morphology and dimensions. Overall, UO20. A number of linear Arrhenius equations for oxidation rates under varying corrosion conditions have been determined in the literature22. To indicate the type of corrosion mechanism occurring in water submersed, grouted uranium metal, the oxidation rates derived from the samples here can be compared to the rates expected using these empirically derived equations  and mineralisation around grout grains during the first few months of hydration, reducing grout permeability and therefore preventing water diffusion to the metal surface25. The three samples which exhibited grout fracturing . Considering grout fracturing did not occur on sample N22L which had corroded for a longer time period than both N6L and N12L, it is expected an additional source of oxidising species was available for the latter two samples which allowed accelerated development of oxide to thicknesses sufficient to weaken the encasing grout. Establishment of the oxidising species source may have originated from two factors: (1) experimental error and (2) chance. Experimental errors include poor formation of the grout. For example, drying out of the grout during synchrotron preparation (evacuation to 5 × 10−4 mbar) at 3 weeks and then re-submersing sample A47L may have weakened the grout by removing essential water required for grout hydration thereby decreasing the grout strength. However, this was not observed to be a problem in the water vapour exposed samples in2. Alternatively, the position of the uranium metal within the grout may have been advantageous for the diffusion of oxidising species to the metal surface. The uranium rod in sample N6L was highly angled within the grout and on closer inspection a large portion of the uranium was positioned near the edge of the grout. Figure The grout split at all four apexes of the cuboid shaped uranium metal Fig. . Therefo2 thickness was relatively uniform across all metal surfaces of sample N12L. UO2 formed during water oxidation usually yields stoichiometric UO2.00, which is reportedly tightly packed and least favourable for diffusion than other oxides27. However, here the oxide would have probably been non protective, since growth beyond 0.1 µm thickness produces internal stress that cause the oxide to fracture and spall, promoting diffusion through the oxide layer20. Consistent with our previous studies28, evidence of strong UO2-grout bonding was exhibited in sample A47L, but again the XRPD resolution was insufficient to identify any mineralisation at this interface.Sample N12L did not appear to show any grout discrepancies or advantageous positioning of the uranium. However, a large 250 µm diameter pore Fig.  was obse29; dissolution of 238U was slow but nevertheless significant over long periods of time. However, thermodynamically, a high pH, low Eh and low DO environment was expected to discourage the release of uranium metal in water30. This highlights that the uranium solubility was not necessarily governed by environmental thermodynamics, but instead the transient ion chemistry of the pore water through complexation with aqueous ions31. No other elements were investigated through ICP-MS, however given the grout chemistry, the dominating aqueous species were expected to have been the alkali ions NaOH, KOH, Ca(OH)2 and uranium in the UO2(OH)3− form.The results presented here are consistent with similar spent fuel dissolution experiments+ (254 amu) and UO2+ (270 amu) in positive ion mode (30 kV and 3 nA). The average oxide thickness was calculated as 0.076 µm. The maximum oxide thickness observed on an intact, grouted, nitric acid etched uranium sample was 5.42 µm (sample N2L) and the minimum growth on a sample which exhibited grout failure was 15.68 µm (sample N6L), indicating potentially 5.43 µm growth. From these figures and assuming uniform oxide growth across all metal surfaces of a 500 × 500 × 500 µm cube, the range of approximate volume expansions where grout failure could occur without steel confinement, was calculated as 6–19%. Studies of OPC curing under various conditions demonstrated that curing of the grout under water saturated conditions resulted in the grout retaining greater compressive strength and degree of hydration34, thus these volume expansions are considered conservative for the water vapour exposed samples in2.To predict the oxide thickness required to cause grout failure, the total oxide thickness believed to be present on the uranium prior to grout encapsulation was calculated. A sample of the same dimensions that had undergone the same preparation as the nitric acid etched samples was allowed to oxidise for 2 hrs in air and was then immediately transferred to SIMS. The oxide thickness was measured at multiple locations using a depth profiling function of UO2 and water submersed  of 0.5 × 0.5 × 20 mm uranium metal samples underwent exactly the same reaction procedure, bar two differences: 1) the uranium metal source and (2) the uranium preparation procedure. All uranium metal was sourced from Magnox Ltd., however the group labelled A in Table  the uran2. These samples were analysed during the first synchrotron session. This excludes sample A47L which was a repeat of sample A3L, 44 weeks after it had been reintroduced to full water submersion immediately after the first beam time.After cutting from a square uranium coupon using a Struers Accutom, the samples in Group A were rinsed and cleaned in water, followed by 5 minutes in a sonicator with acetone and then high purity methanol. Each sample was then left to oxidise in air for two hours to ensure the cut surfaces had reached the linear rate stage of oxide growth. Therefore, at the time of grout encapsulation, Group A retained an ‘as-received’ corrosion layer. The surface features and characterisation are discussed in3 for 3 hours until they appeared ‘shiny’. These samples were then rinsed and cleaned using the same procedure as Group A and also allowed to oxidise in air for 2 hrs prior to grout encapsulation. Characterisation of the surface after this pre-treatment is also described in2.After cutting from a circular uranium disc, Group N were coarsely abraded from p600–2500 using SiC grit paper and water as lubricant. Each uranium rod was then submersed in 5 M HNOet al.,36. Each cylindrical specimen was cured for 3 days in a Perspex mould exposed to a moist environment, before transfer to a reaction cell. The cells composed of a clean glass test tube filled with 17 ml de-ionised water, which was sufficient to completely submerse the grouted uranium sample. Laboratory parafilm was used to seal the top of the test tube. Before synchrotron examination, each sample was transferred to a customised, gas tight, quartz glass-stainless steel reaction cell and evacuated to <5 × 10−4 mbar.The metal rods were encapsulated in grout composed of BFS (Redcar Steel Works):OPC (Castle Cement) in a 3:1 ratio and 0.4 w/c. This composition fulfilled the material specification used during ILW encapsulation by Sellafield Ltd., and typical compositions are shown in Utton 37 was used to view and reconstruct the XRT images of each sample and Avizo® was used to produce 3D renders of the XRT data using the generate surface module for specific ranges in greyscale (X-ray intensity) representing each examined material.At the JEEP beam line (Diamond Light Source) XRPD and XRT were performed on each sample separately. For the first beam time energies of 114.6 keV and 115.6 keV were used for XRPD and XRT respectively, however this was reduced to 113.3 keV for both techniques in the second beam time since this energy was further away from the uranium K absorption edge (115.6 keV) and thus produced sharper XRT images. 2D XRPD data were recorded using a flat panel Pixium RF4343  in high resolution mode (2880 × 2881 pixels). This detector has a pixel size of 148 × 148 μm. The beam footprint was ~340 × 340 μm on the sample surface. The high resolution PCO pco.4000 imaging detector with its Module 4 camera was used for imaging using a monochromatic beam to obtain the best resolution, 1 pixel = 0.98 × 0.98 μm. Data Analysis WorkbeNch (DAWN) software2 was used for beam calibration . Within the grout, the uranium rod orientation and position changed, resulting in a 1–2 mm variance in sample to detector distance from the central CeO2 position. Small shifts in the 2θ value of the XRPD peaks were therefore expected when comparing data. Dawn software for 2D diffraction and processing tools38 was used to produce 2D XRPD patterns to 1D.Two horizontal XRPD line scans were measured across the 0.5 mm width of each metal sample at two heights along the length of the sample. CeO238U concentration using a VG Thermo Elemental PQ3 ICP-MS. Samples were prepared for ICP-MS by a set dilution in 1% nitric acid . Blanks and uranium standards were also prepared in 1% nitric acid. An internal bismuth standard of 10 ppb was also added to all blanks, standards and samples.5 ml of the residual water was removed from the N2L, A6L and A47L water to measure the aqueous Data underlying this article can be accessed on Zenodo at https://doi.org/10.5281/zenodo.1118280 and used under the Creative Commons Attribution licence.Supplementary Information


The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation .  They also underestimate anthropogenic heat fluxes. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling.One important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. Satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget fluxes, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and  Earth Observation (EO) has been widely used to study the Urban Heat Island (UHI) phenomenon, but to a lesser extent to quantify heat fluxes15. Recent studies17 have investigated the potential of EO to derive turbulent heat fluxes and identify and analyse the associated uncertainties.The Urban Energy Balance (UEB) needs to account for the 3D nature of cities, quantifying the fluxes into, out of and the storage change within the control volume Fig. . In the 20  or larger). However, such information is practically impossible to derive for extensive areas by in-situ flux measurements and EO-based estimation of spatio-temporal patterns of different UEB components is challenging. Therefore, the question arises whether EO can provide reliable estimates of UEB at the times of satellite acquisitions. Here we address this by investigating the potential of the current satellite missions to retrieve UEB fluxes at local scale, supported by meteorological observations. Recognising the range of city forms, size and settings, we explored three locations: London (UK), Basel (Switzerland) and Heraklion (Greece).Both Earth system science and urban planning communities need spatially disaggregated UEB data at local scale21  and turbulent sensible heat flux (QH) peaking in summer. QH and ΔQS peak in August, whereas turbulent latent heat flux (QE) is relatively minor throughout the year in city centres.The study period (January 2016–May 2017) allowed the seasonal cycle to be examined  model6 takes into account this anisotropy. It simulates reflected and emitted radiation using information derived from EO satellite images (see more details in Methods). For example, Q* in Basel ) in Q* for 2016 was 22.5 W m−2 3, which reduces the 3D urban structure to one-dimensional (1D) elements (see details in Methods). The sensitivity analysis identified that the fraction of wall facet and materials are the two most important variables in the estimation of ΔQS. Daytime peak  ΔQS is largest (up to 400 W m−2) in densely built areas where tall buildings dominate  and surface roughness parameters are used combined with air temperature and humidity from meteorological stations (see details in Methods). For example, for 23 August 2016 in Basel  are found in the most densely built-up and industrial areas. QH values have similar spatial patterns and magnitudes to ΔQS, whereas, QE  and has largest values in vegetated areas. The eddy covariance flux footprint27 is used to compare measured with modelled turbulent heat fluxes. For the Basel BAES (Aeschenplatz) flux tower, the QH MAE for 2016 is 41.2 W m−2 .The turbulent heat fluxes are strongly modified by large roughness elements and a complex mix of sources/sinks of heat and water in cities. Here, the Aerodynamic Resistance Method (ARM) is used to derive Qsel Fig. , the hig QE Fig.  is much m−2 Fig. , with moons Fig. . This reF are extremely difficult28. Here, QF is obtained from the residual of the UEB, and thus includes the cumulative uncertainties of all the terms. Energy balance closure typically compares available energy (Q* - ΔQs) with turbulent heat fluxes (QH + QE). Basel’s two flux towers, have total EC turbulent heat fluxes that tend to be slightly greater than the available energy during winter months . This suggests that individual UEB components are incorrect most likely due to the underestimation of QH. Highest relative QF is found south of the river in the most densely built-up areas , health studies  and future proofing . Finally, the frequency of heat waves is expected to increase30 with UHI and other urban characteristics exacerbating the respective warming, resulting in increased energy demand for cooling systems in low and mid-latitude cities, which in turn adds to heat emissions and raises temperatures further31. Our satellite-based approach, strengthened by uncertainty minimization in future applications, is expected to advance the current knowledge of the impacts of heat fluxes on energy consumption in cities, leading to the development of tools and strategies to mitigate these impacts, improving thermal comfort and energy efficiency.Based on the analysis of individual UEB components, we conclude that current satellite missions have the potential to provide information about spatial patterns of urban energy exchanges, if supported by suitable meteorological measurements. Synergistic analysis of specific satellite observations is able to identify and map spatial distributions of heat fluxes at local scale, several times per day (if clear weather). The EO-based methods developed are easily transferable to any city and have the potential to support sustainable planning strategies, since knowledge of UEB patterns at neighbourhood level is needed in urban planning  from Copernicus Sentinel 2 and Landsat 5/7/8, as well as very high resolution (VHR) data (<10 m × 10 m) from Copernicus Contributing Missions, such as SPOT (Satellite Pour l’Observation de la Terre) and WorldView-2, RapidEye, TerraSAR-X and TanDEM-X are used. The EO thermal infrared data are from MODIS (Moderate Resolution Imaging Spectroradiometer), Sentinel 3 (1 km × 1 km) and ASTER  observations (at 90 m × 90 m)21. Synergies between Sentinels 2 and 3 proved useful to identify fractional surface cover needed in local scale surface emissivity and LST23 estimates. All UEB fluxes are estimated at 100 m × 100 m resolution.Three cities were selected: highly urbanized mega-city ; typical central European medium size city, that requires substantial winter heating ; and a smaller, low latitude Mediterranean city that requires substantial summer cooling . Meteorological data from wireless automatic weather station networks (AWSN) and flux towers  in all cities were supplemented by Large Aperture Scintillometers21 and London32). Surface structure parameters  are derived by morphometric analysis22, using the Urban Multi-scale Environmental Predictor (UMEP)33. Surface cover  are obtained from EO using advanced machine learning techniques and detailed spectral mixture models34. Multi-temporal acquisitions are used to update the surface cover fraction maps for the classes of interest. By fusing VHR data with Sentinel-2 observations it is possible to categorize different roof materials35 and build spectral libraries36. Other required parameters  are derived as outlined in Marconcini et al.21. As daily high spatial resolution thermal imagery is unavailable, the thermal radiance retrieved at 1 km × 1 km resolution  are downscaled to 100 m × 100 m LST and emissivity using Mitraka et al.’s23 novel technique. Uncertainties in this approach are quantified by Mitraka et al.24. The downscaled LST values are compared to in-situ surface temperature observations from the micrometeorological flux towers.Surface characteristics include morphology, cover and biophysical parameters. Morphology requires the location and dimensions of urban objects. For this purpose, DSMs are derived from VHR optical stereo imagery (Heraklion) or airborne LiDAR observations  per urban element at the spatial resolution of the satellite data to be obtained. The iterative procedure accounts for the multiple scattering mechanisms and has six steps: 1) Urban morphology and material type are given as input to DART. 2) DART simulates at high spatial resolution  all spectral bands of the satellite sensor being used for comparison. The simulation accounts for the specific atmosphere and illumination conditions of each input satellite scene. In the first iteration, spatially constant OP are set per urban element type. 3) DART-simulated spectral image output are georeferenced and spatially resampled to the input satellite scene. 4) DART-simulated and satellite images are compared on a pixel basis to improve the OP map per urban element, using the area of the urban elements within each satellite pixel . If the DART-simulated and satellite images do not match, the procedure re-iterates from step 2. 5) DART simulates the angular spectral radiative flux along all directions sampling the upper hemisphere. 6) Shortwave K* (K↓ - K↑) and longwave L* (L↓ - L↑) exitance maps are computed as a double integral over the spectrum and the upper hemisphere. Finally, Q* (K* + L*) is resampled to 100 m × 100 m spatial resolution and compared against observations from the micrometeorological towers.Q* is the difference of the incoming solar (shortwave) radiation (K↓) and atmosphere thermal (longwave) emission (L↓), minus the outgoing reflected solar radiation (K↑) and urban surface thermal emission (L↑). Satellites observe in few spectral bands from restricted viewing directions, whereas Q* is an integral over a wide spectral domain and the whole hemisphere. Here, the DART modelS is the net flow of heat stored in the urban volume . In urban areas, the net heat stored in the canopy is a relatively large fraction of Q* and directly evaluating ΔQS in the urban canopy is difficult38. To determine ΔQS we use the Element Surface Temperature Method (ESTM), which reduces the 3D urban volume to four elements, i.e., building roofs, walls, and internal mass and ground 6:ΔΤi/Δt is the rate of temperature change over the period for each element i, ρci is the volumetric heat capacity, Δxi is the element thickness and fi is the fraction of each surface type. Each element type has sublayers . Without internal element temperature data the average is determined:Q is the conductive heat flux through the surface and k the thermal conductivity. For the internal surfaces  the surface temperature of element i is determined by setting the conductive heat transfer out of (in to) the surface equal to the radiative and convective heat losses (gains), as described by Offerle et al.6.ΔQ33. As land cover thermal properties differ, detailed land cover information was required. The seven land cover classes  and local knowledge. The meteorological forcing from the AWSN with the EO downscaled LST are used.The morphology of the urban surface was derived from high resolution DSM, including 3D information of vegetation40 to estimate QH:p the specific heat of air at constant pressure (1005 J kg−1 K−1), Ta is the air temperature provided by the AWSN and ra is the aerodynamic resistance (s m−1). Analogously, QE is expressed as:s* is the saturation water vapour pressure (hPa) at surface air temperature, ea is the atmospheric water vapour pressure (hPa), γ is the psychrometric constant (0.67 hPa K−1) and rs is the stomatal resistance (s m−1). Stomatal resistance is calculated following Kato et al.41 using a simplified equation from Nishida et al.42:sMIN is the minimum stomatal resistance and rcuticle is the canopy resistance related to the diffusion through the cuticle layer of leaves (105 s m−1). Functions f1 and f2 are from Nishida et al.42 and rsMIN is for each vegetation type41. QE is calculated by land cover type and weighted by the pervious rsMIN type in every pixel. The aerodynamic resistance ra is40:* the friction velocity (m s−1):κ is von Kármán’s constant (0.4), zref is height of wind measurements, zd is the zero-plane displacement height, L is the Obukhov length, z0m (z0h) is the roughness length and ψm (ψh) is the stability function for momentum (heat). z0h is often reported within the dimensionless number kβ−1, defined asThe methodology uses the ARM approach28 measurements can be used to determine kβ−1 for their flux footprint. Here, flux tower measurements are used as reference values for the magnitude of the fluxes of momentum, sensible and latent heat during the satellite overpass. z0h is calculated from the roughness Reynold’s number (Re*) as43:α = 1.29 and Re* (=z0mu*/ν) uses the kinematic molecular viscosity ν (1.461 × 10−5 m s−1).EC and scintillometryzd) and z0m are calculated with morphometic parameterization of Kanda et al.44, using urban characteristics  derived from 1 m × 1 m DSM, using UMEP33.The displacement height  can be determined from regression between (QH + QE) and (Q* - ΔQS) to give an estimate of QF and uncertainty. This requires three assumptions: 1) All sources of anthropogenic heat are released into the environment. 2) Advection is negligible at the scale of interest or cancels between scales: at the microscale ; at the local scale ; and at the meso-scale . Moisture advection enhances latent heat flux and can be of similar size to QH, but opposite in sign, thereby essentially offsetting each other45. For this local scale study the advection error is within the QF estimate from energy balance closure. 3) Any unmeasured terms are incorporated in the error of the QF estimate. The resulting QF is evaluated by comparison to QF estimates from alternative approaches, based on inventories46.With the other UEB components known, Qhttp://urbanfluxes.eu/data/.This research was conducted under the framework of the URBANFLUXES project that received funding from the European Union’s Horizon 2020 Research and Innovation Programme and joined the H2020 Pilot on Open Research Data. Therefore, all datasets related to the present study are included in the URBANFLUXES Data Repository as Open Data and can be accessed at: Supplementary Information


P < 5 × 10−8): TEX41 , FOXO1 , C1orf21  and MRVI1 . Gene-based testing revealed three significant genes, the most significant gene was COL4A2 (P = 1.41 × 10−8) encoding type IV collagen. Other candidate genes at associated loci were also involved in smooth muscle tone regulation. Our findings provide new information for understanding the development of arterial stiffness.Arterial stiffness index (ASI) is a non-invasive measure of arterial stiffness using infra-red finger sensors (photoplethysmography). It is a well-suited measure for large populations as it is relatively inexpensive to perform, and data can be acquired within seconds. These features raise interest in using ASI as a tool to estimate cardiovascular disease risk as prior work demonstrates increased arterial stiffness is associated with elevated systolic blood pressure, and ASI is predictive of cardiovascular disease and mortality. We conducted genome-wide association studies (GWASs) for ASI in 127,121 UK Biobank participants of European-ancestry. Our primary analyses identified variants at four loci reaching genome-wide significance ( Carotid-femoral pulse wave velocity (PWV) is the reference standard method for measuring arterial stiffness. However, an alternative and more convenient non-invasive method is to record the digital blood volume waveforms using infra-red finger sensors (photoplethysmography)8, where measurements can be recorded in a seated position rather than supine position required for carotid-femoral PWV recordings. This automatic technique is able to detect the waveform formed by the digital volume pulse, which is created by two components. First, pressure is transmitted from the left ventricle to the finger (direct component) whilst the second component is due to the transmission of pressure from the heart to the lower body (reflected component) via the aorta. The digital volume pulse can therefore be visualised as a dicrotic waveform, and the interval between the peaks of the direct and reflected components can be recorded to derive the arterial stiffness index (ASI), when divided into the individual’s height. Higher ASI values reflect arterial walls with greater stiffness, due to the earlier arrival of wave reflection. ASI has been shown to have close agreement with other techniques measuring arterial stiffness, including PWV  and augmentation index 9.Arterial stiffness measures have been reported as independent markers of vascular ageing9. In a study of asymptomatic middle-aged patients, the mean ASI value was significantly higher (P = 0.002) in individuals with at least one significant (>50%) coronary stenotic plaque than those without a >50% stenotic lesion10. More recently, ASI has been shown to be an independent predictor of CVD, myocardial infarction and mortality in the UK Biobank cohort11. During a median follow-up period of 2.8 years, the risk of CVD and myocardial infarction for individuals with higher ASI were 27% and 38% higher respectively. For all-cause mortality, each standard deviation change in ASI has a hazard ratio of 1.08  in a multi-variant adjusted model11.Previous studies have evaluated the utility of ASI as a potential clinical marker of CVD, having shown good sensitivity (87%) and specificity (87%) when differentiating between older men with coronary artery disease (CAD) and younger men without CAD13 and ranging between 0.26 and 0.40 in population studies15. The identification of single nucleotide variants (SNVs) may improve the current understanding of the mechanisms controlling arterial stiffness and may be a useful addition to the development of disease risk models. Previous genome-wide association studies (GWASs) for arterial stiffness were mostly performed using PWV as the phenotype. One of the first arterial stiffness GWASs was on 644 individuals from the Framingham Heart Study, using carotid-brachial PWV as the phenotype, and no variants reached genome-wide significance16. Subsequent genetic studies have reported inconsistent findings but none have studied ASI as the phenotypic measure17. The wealth of data available within the UK Biobank study offers the opportunity to produce more robust findings in a large population cohort. We therefore performed a GWAS to identify SNVs associated with ASI and explored the biological mechanisms underlying arterial stiffness.Our current knowledge of the biological factors and pathways contributing to arterial stiffness is limited. Heritability studies of arterial stiffness using PWV measurements have suggested moderate genetic contribution with estimates up to 0.53 in twin studiesIn total, 143,590 UK Biobank participants met the inclusion criteria to derive ASI at the baseline visit  for ASI in our primary analysis  were genome-wide significant in the secondary GWAS that excluded the MAP adjustment , rs7331212 an intronic variant at FOXO1  and rs1930290 , located in the gene region of the open reading frame C1orf21. The intronic variant rs10840457 within the MRVI1 gene region on chromosome 11  was only genome-wide significant in our primary analysis, however there was some support in the secondary analyses (P = 3.3 × 10−7) and untransformed ASI (P = 5.4 × 10−8).The most significant associated variants in our primary GWAS were: rs1006923, an intronic variant at P < 1 × 10−5) with ASI in our primary analysis , rs8107744 in the RSPH6A gene . Other variants of note were rs371147897 found in the OR4A47 gene region (P = 1.6 × 10−7), rs9501489 located in DDR1 (P = 4.0 × 10−6) and rs149320025 located in the FUCA1 gene region on chromosome 1 (P = 4.0 × 10−7).Forty-three variants at non-overlapping loci had suggestive genome-wide significance (18. The majority of SNVs and their proxies (r2 > 0.8) were located in introns (63%), 31% were located in intergenic regions, there was one acceptor splice variant at MRVI1, rs11042902 and the remainder were located in exons and 3′-untranslated regions (3′-UTRs). Of the 219 variants, 151 variants mapped to the four genome-wide significant loci.In our primary analysis, 219 candidate variants were identified by SNP2GENE function in the Functional Mapping and Annotation of Genome-wide Association Studies (FUMA) platform19, which mapped the output SNVs from BOLT-LMM to 18,666 protein coding genes, identified four genes that reached the gene-wide significance threshold (P = 2.67 × 10−6). COL4A2 was the most significant gene for ASI (P = 1.41 × 10−8) and MRVI1 (P = 3.08 × 10−7), FOXO1 (P = 7.54 × 10−7) and FOXO3 (P = 2.34 × 10−6) were also significant  and FBXO46 located on chromosome 19 (P = 6.72 × 10−6) indicated suggestive significance. The FBXO46 gene is located less than 67 kb away from rs8107744 at the RSPH6A locus on chromosome 19 that was significant from single variant analyses using untransformed ASI values 18. Significant eQTLs were observed for SLC25A15 at the FOXO1 locus across several tissues including oesophagus, transformed fibroblasts and sun exposed skin in the lower leg , ZEB2 at the TEX41 locus in the aorta  and C1orf21 and APOBEC4 at the C1orf21 locus in nerve and brain tissue (Supplementary Table To identify further candidate genes at each locus we reviewed results from expression quantitative trait loci (eQTL) analyses across 53 tissue types from Genotype-Tissue Expression (GTEx) database from FUMA20. Genome-wide associations were observed for variants at three of the four ASI loci. Variants at TEX41 (rs1006923) and MRVI1 (rs10840457) were associated with blood pressure traits and ASI from analyses in UK Biobank21. Significant associations were also observed with pulse wave peak to peak time (PPT) for rs1006923 (TEX41) and rs7331212 at the FOXO1 locus by the Neale lab21 that has publicly released GWAS results for over 4,200 phenotypes found within UK Biobank. The variant at the TEX41 locus was additionally associated with CAD22  heritability observed for ASI. We further followed-up with several candidate genes using bioinformatics analyses at each of the identified loci.et al.17, have mainly used PWV as the phenotypic measure of stiffness and the reported findings were limited. A meta-analysis including GWAS results of 20,634 individuals from 9 discovery cohorts and of 5,306 individuals from two replication cohorts, all from European ancestry, identified one locus on chromosome 14 in the 3′-BCL11B gene desert. This gene desert was shown to be associated with the carotid-femoral PWV 23. However, variants at this locus were not statistically significantly associated with ASI (P = 0.08) in our dataset.Previous genetic association studies for arterial stiffness, as summarised by Logan et al.24. Two SNVs were found to be associated with baPWV – rs7271920 (P = 7.15 × 10−9) and rs10125157 (P = 8.25 × 10−7). Neither variant was significant  in the replication cohort in their study that included 1,206 individuals. We also observed non-significant results for both variants  in our study. It is not too surprising to observe a lack of significant findings for the two variants reported by Park et al.24 in the UK Biobank cohort. A contributing factor may be the difference in the size of the populations, and the different ethnicities, and that the reported variants by Park et al.24 may be false positive findings as there was no replication in their study.More recently, a GWAS for brachial-ankle PWV (baPWV), involving 402 Korean patients  with diagnosed CVD has been reported by Park 25. In other words, whilst PWV is determined mainly by the speed at which waveform travels, for ASI, other factors such as the reflective point would also impact on its measurements leading to potential differences in their aetiologies.The lack of replication of results on PWV in UK Biobank and across PWV GWASs may also be due to the lack of methodological standardisation to derive PWV, as it can be measured at different sites such as carotid-femoral or brachial-ankle. Our findings suggest that ASI and PWV may have different aetiologies and thus may provide independent data on the underlying biological mechanisms and potential cardiovascular risk factors. A previous expert consensus statement on arterial stiffness described PWV as a measure of regional stiffness, while ASI is seen as a surrogate marker of stiffness through wave reflection assessmentsTEX41 locus has previously been reported to be significantly associated with CAD in a mixed population GWAS that included UK Biobank participants22. It is located 129 kb upstream and in low linkage disequilibrium  to rs1438896 a variant reported by Warren et al.26 with blood pressure traits. Another variant at the TEX41 locus rs183032 is 50 kb downstream to our lead variant with a moderate LD (r2 = 0.38) is associated with aortic stenosis in an Icelandic cohort27. Helgadottir (2018)27 suggested the tumour growth factor-β (TGF-β) through ZEB227 as a candidate gene. ZEB2 has a role as a DNA-binding transcriptional repressor that interacts with the main signal transducers for TGF-β receptors (SMADs). Considering that TGF-β is involved in the regulation of vascular smooth muscle differentiation, as well as in the collagen up-regulation in the vascular wall, changes in the expression of TGF-β have the potential to alter arterial stiffness. We note additional support for ZEB2 from eQTL data in aortic tissue in our analyses ). The MRVI1 gene is responsible for encoding the MRVI1/IRAG protein, which is present in a number of tissues including aorta and trachea31, and is involved in smooth muscle contractility. Specifically, there is inhibition of calcium release from endoplasmic reticulum following co-expression of IRAG and cGMP-dependent protein kinase type Iβ (cGKIβ) in the presence of cGMP31. In a study using IRAG-knockout mice, the authors concluded that signalling of cGKIβ via IRAG is a vital functional component in the regulation of smooth muscle tone and intracellular calcium by nitric oxide and atrial natriuretic peptide32. At this locus MRVI1 represents an interesting candidate, but we note this is a locus that was not genome-wide significant in the secondary analyses, thus further validation will be required.At the 4COL4A2 as the most significant gene association with ASI in our cohort, with FOXO1, MRVI1 and FOXO3 also significant. The lead SNV from the GWAS at the COL4A2 locus is rs9521719 (P = 1.1 × 10−7). COL4A2, along with the adjacent gene COL4A1, encodes the protein subunits of type IV collagen forming heterotrimers. Type IV collagen is a vital structural component of basement membranes and mutations in these genes are seen in disorders such as myopathy, intracerebral haemorrhage and glaucoma33. A previous GWAS reported rs3742207 located near the COL4A1 to have strong association (P = 7.08 × 10−7) with PWV in a cohort comprising of 4,221 Sardinian individuals34. This variant was successfully replicated internally in 1,828 individuals and also in 813 Amish individuals. The PWV variant rs3742207 did not show any association with ASI in our cohort (P = 0.95). This result may add additional support on there being a difference in the genetic mechanism of ASI compared to PWV, both markers of arterial stiffness.Gene-based testing by MAGMA revealed 35. This dissimilarity might be explained by the differences in stiffness measure, covariates included and populations. In addition, due to the methodological inconsistencies mentioned above, it should be noted that the heritability of PWV would differ depending on the site of measurement. Mitchell et al.14 reported moderate heritability for carotid-femoral PWV (h2 = 0.40) in their study of 1,480 participants in the Framingham Study offspring cohort. However, when using the less common approach of measuring PWV between the carotid and brachial artery, the heritability estimate was lower (h2 = 0.09). In twin studies, the reported heritability estimates range between 0.38 and 0.5336 where the PWV measurements were either located at the wrist  or foot .We found the genome-wide SNV heritability of ASI to be 6%. This estimate is much lower than the reported heritability for carotid-femoral PWV, which ranged between 0.36–0.4015 so it was included as a covariate in the fully adjusted model, rather than systolic blood pressure (SBP) and diastolic blood pressure (DBP). Despite using the largest cohort to date in identifying genetic variants for arterial stiffness, the main limitation of our study is that loci discovered therein require formal validation in an independent dataset. However, an external study of similar sample size with ASI measurements is currently lacking. We note if we use a more stringent P-value for reporting, P ≤ 1 × 10−8, then two loci would remain significant (TEX41 and FOXO1). In addition, our study cohort consisted of middle-aged individuals of European-ancestry, so our findings may not be generalised to other age groups and ethnic populations.The main strength of our study is the very large sample size that, despite the low estimated heritability for ASI, has enabled the identification of genetic variants not found in previous studies. Mean arterial pressure has been shown to have a strong influence on arterial stiffnessTEX41 and FOXO1, have SNV associations that may alter arterial stiffness through blood pressure regulation and vascular smooth muscle differentiation. Our results also suggest an important role of calcium in the regulation of smooth muscle tone contributing to arterial stiffness. Further research will be necessary to validate our discovered loci in a separate cohort and their confirmation can potentially lead to the development of risk models that can be used in clinical practice.In conclusion, we identified four loci significantly associated with ASI, an independent predictor of CVD and mortality. The two most significant loci, The UK Biobank is a large population-based prospective study of >500,000 participants aged 40–69 years at baseline recruited between 2006 and 2010 in England, Scotland and Wales. During the initial assessment visit, a broad range of biochemical, clinical and genotype data were collected and participants had a number of physical measurements. The UK Biobank study was approved by the North West Multi-Centre Research Ethics Committee and all enrolled individuals have provided written informed consent for collection, storage, make availability of their data for health-related research. All methods were carried out in accordance with the relevant guidelines and regulations.TM, CareFusion, USA). The shape of the volume waveform in the finger is directly related to the time it takes for the pulse waveforms to travel through the arterial tree in the lower body and to be reflected back to the finger. Measurements were made by clipping the device to a finger and the reading is made over 10–15 seconds. As the participants’ heights were unknown until after the recording of the waveform data, the actual ASI values were calculated (ASI = height/PPT) by UK Biobank outside the assessment centre visit.Pulse wave ASI (UK Biobank Field 21021), measured in m/s, was derived using the pulse waveform obtained at the finger (preferably index finger of the non-dependent hand though can be placed on any finger or thumb) with an infra-red sensor . UK Biobank participants are free to withdraw at any time and so 15 individuals were removed based on the application specific list of anonymised IDs. Individuals with absence of notch position in the pulse waveform (Field 4204) were excluded  as the notch in the digital volume pulse waveform indicates the reflected component of the pressure transmitted and is therefore required to calculate PPT. Outlier ASI values, defined as three inter-quartile ranges below the first quartile or above the third quartile (n = 81), were also removed from analyses. Finally, participants with missing height, weight or blood pressure measurements were removed from the final analyses (n = 848).Data were taken at the UK Biobank Assessment Centres during the baseline recruitment between 2937. The ID numbers of the arterial stiffness devices used were obtained from Field 4206 in UK Biobank.Blood pressure was measured using the Omron 705 IT electronic blood pressure monitor . SBP and DBP were derived as the mean of the two recorded automated measurements , except for 1,141 individuals who only had one recorded reading. MAP estimation was calculated using the traditional formula: MAP = DBP + 1/3(SBP-DBP). Height (Field 50) was measured using a Seca 202 device . For individuals who reported use of anti-hypertensive medications through questionnaires in UK biobank , their SBP and DBP were adjusting by adding 15 mmHg and 10 mmHg respectively to the mean recorded readings38. Briefly, genotypic data was obtained through either UK Biobank Axiom or UK BiLEVE Axiom arrays . The Haplotype Reference Consortium (HRC) and the merged UK10K sequencing +1000 Genomes were used as a reference panels for imputation with preference for the HRC panel where SNPs were present in both panels. This study utilised the refreshed genetic dataset made available by UK Biobank in July 201738.Central QC and imputation of genotypic data performed by UK Biobank has been previously describedGenetic QC was performed in 164,835 individuals who underwent assessment of arterial stiffness using finger photoplethysmography and had available genotypic data. This process excluded participants with either high missingness or high heterozygosity defined by UK Biobank in their genotypic data (n = 345), as well as those with mis-match between self-reported and inferred sex from the genotypes (n = 116).k-means clustering analysis was performed according to data from the first and second principal components (PC1 and PC2) using the ‘pvclust’ R package (version 2.0–0)39 to objectively identify the main ethnic groups  within UK Biobank. “White” participants were defined for those present in the “White” cluster for both PC1 and PC2 analyses. They also needed to match their self-reported ancestry though “mixed, “other” and “missing” were treated as being broad ethnicity. After restriction to European-ancestry only, 127,121 individuals remained. Lastly, we performed SNV-level QC to exclude 237,751 SNVs from genotyped SNVs in these individuals with the following thresholds: minor allele frequency (MAF) of 1%, Hardy-Weinberg equilibrium  and missingness of 0.015 using PLINK 1.940. After exclusions, a final total of 546,505 model SNVs was generated for our GWAS analyses.A 4-way 51.We applied rank-based INT on the residuals from the regression of ASI against the phenotypic covariates  before performing genetic analyses as the distribution did not approximate a normal distribution  score ≥ 0.3. Our primary ASI GWAS was on the INT residuals including the following covariates: age, age2, sex, weight, genotyping array (UK Biobank vs UK BiLEVE), device used to obtain pulse waveform, smoking status (current vs non-current smokers), MAP and first 10 principal components (PCs). As a sensitivity analyses, we also performed a GWAS using untransformed ASI values and same covariates as those included in our primary analysis.We performed three GWASs using BOLT-LMM (v2.3.2) software41.A secondary GWAS where MAP was excluded as a covariate was also performed. All GWASs assumed a linear mixed infinitesimal model method under an additive genetic model implemented in BOLT-LMM. This method accounts for cryptic population structure and allows the inclusion of related individuals permitting greater power compared to principal component analysisP-value ≤ 5 × 10−8 and R (version 3.5.1) statistical Software42 was used to generate Manhattan plots and QQ plots. Regional association plots were made for genome-wide significant loci using the LocusZoom web-based platform (locuszoom.org) with the hg19/1000 Genomes (Nov 2014) EUR Build.Genome-wide significance was defined as 43. A secondary signal was declared if all three of the following conditions were met:(i)P-value of newly identified variant was <1 × 10−6original (ii)P-values on a –log10 scale is 1.5 or less, /−log10(P sec) < 1.5)ratio between the lead SNV and secondary association (iii)P-values on a –log10 scale is 1.5 or less /−log10(P cond) < 1.5)ratio between the original association and conditional association Conditional analysis was performed for genome-wide significant locus to detect independent association signals using an approximate conditional and joint multiple-SNP (COJO) analysis implemented in genome-wide complex trait analysis (GCTA) tool18 web-based application (http://fuma.ctglab.nl/) in order to perform functional annotations via its SNP2GENE function. The default FUMA settings were used whereby significant SNVs (P < 5 × 10−8), and those that were in LD (r2 ≥ 0.6), with a MAF ≥ 0.01 were selected for further annotation. In addition, the maximum distance between LD blocks to merge into a locus was set at <250 kb and the UKB release 2 European was used as the reference panel population. The functional consequences for these SNVs were then obtained with ANNOVAR44. For gene-based analysis, the integrated MAGMA v1.0619 was utilised in which SNVs are mapped to a gene according their genomic location before gene-level associations with ASI were tested. Here, a P-value with Bonferroni adjustment of 0.05/18697 = 2.67 × 10−6 was used to define genome-wide significance. For eQTL mapping, this was implemented by the FUMA platform, GTEx v7 database45 , 53 tissue types was used as the gene expression reference data and the significant threshold was defined as false discovery rate (FDR) < 0.05. Genome-wide significant SNVs and those in LD with r2 > 0.8 were examined for previously reported GWAS associations with other traits using PhenoScanner20, an online search tool containing over 350 million publicly available association results. The significance threshold chosen for reporting therein was P-value < 5 × 10−8.Summary statistics from our primary model GWAS analyses were uploaded onto the FUMA (v1.3.4c)Supplementary Information