W, in atmospheric models constitutes the largest source of uncertainty in the calculation of the cirrus formation rate. Using global atmospheric simulations with a spatial resolution of 7 km we obtain for the first time a direct estimate of the distribution of W at the scale relevant for cirrus formation, validated against long-term observations at two different ground sites. The standard deviation in W, σw, varies widely over the globe with the highest values resulting from orographic uplift and convection, and the lowest occurring in the Arctic. Globally about 90% of the simulated σw values are below 0.1 m s−1 and about one in 104 cloud formation events occur in environments with σw > 0.8 m s−1. Combining our estimate with reanalysis products and an advanced cloud formation scheme results in lower homogeneous ice nucleation frequency than previously reported, and a decreasing average ice crystal concentration with decreasing temperature. These features are in agreement with observations and suggest that the correct parameterization of σw is critical to simulate realistic cirrus properties.Cirrus clouds determine the radiative balance of the upper troposphere and the transport of water vapor across the tropopause. The representation of vertical wind velocity,  They control the hydrological balance of the upper troposphere and the longwave cloud feedback3. Outside of areas of strong convection, cirrus form by ice nucleation on aerosol particles. Human activities  and natural phenomena  that modify the concentration of atmospheric aerosol influence the properties of cirrus4, impacting the hydrological balance of the atmosphere and ultimately Earth’s climate7. For example, observational studies suggest that volcanic ash leads to the increase of ice-containing clouds in the upper troposphere7. There is also evidence of aerosol particles affecting the dehydration efficiency of cirrus in the tropical tropopause, hence the concentration of water vapor entering the stratosphere8. Cirrus modification has been suggested as a geoengineering strategy to counterbalance greenhouse warming9. However, modeling studies show discrepancy on the predicted response of cloud properties to the emission of distinct aerosol species11, introducing uncertainty in the reliability of cirrus geoengineering on climate12.Cirrus clouds, made of ice crystals and present at low temperatures (below 235 K) and high altitudes, cover approximately 17% of the Earth14. Moreover, the ice-nucleating efficiency of aerosol depends on particle size and chemistry, and is not completely understood15. Ice formation is also realized by two different processes: homogeneous ice nucleation (HOM), i.e., the spontaneous freezing of supercooled liquid droplets , and, heterogeneous ice nucleation (HET), which requires the presence of ice nucleating particles (INP). Although sparse, INP can profoundly alter the evolution of clouds5. Much investigation has been devoted over the last decade to elucidating the nature of INP showing that a small fraction of atmospheric aerosol  are capable of nucleating ice15. The HOM and HET mechanisms can occur simultaneously, and in fact they “compete” during cloud formation. HOM nucleation requires higher relative humidity (RH) than HET. Thus, by depleting the increase in RH from cooling driven by vertical motion, ice crystals formed by the HET mechanism may inhibit and even prevent HOM nucleation16. However, compared to HOM, HET nucleation leads to lower ice crystal concentration18.The representation of cirrus in general circulation models (GCMs) remains challenging. Wind velocity, temperature, relative humidity and the physicochemical properties of the aerosol may vary at smaller scales (~1 to 20 km) than the typical resolution used in GCMs (~100–200 km)19. Early studies showed that introducing vertical velocity perturbations within parcel model simulations resulted in high variability in ice crystal concentration, Ni21, which has been confirmed by aircraft observations within cirrus24. Modeling studies have also shown that low T and high W favors the HOM over the HET mechanism29, which is reflected in the global distribution of Ni simulated in GCMs30. Field measurements however suggest predominance of HET and lower Ni than implied by HOM24, consistent with sustained levels of supersaturation found in cirrus clouds32. This suggests that the frequency of HOM in cirrus is likely overestimated by GCMs. Recent studies point at overestimation in the parameterized cloud-scale W as the likely cause of the discrepancy between models and observations36.Vertical motion determines the maximum relative humidity in a cloudy parcel and drives ice nucleationσw, since the mean resolved vertical transport is slow compared to cloud-scale motions29. The latter are typically not resolved by CGMs due to their coarse resolution (~100 km). To represent subgrid W variability, most GCMs rely on either poorly constrained parameterizations or empirical correlations representing particular cloud realizations. A recent study37 suggested that vertical wind velocity may be responsible for about 90% variation in calculated ice crystal formation rates, although it is not clear whether the same relation applies to other cloud properties. Field campaign analyses and cloud modeling studies38 also suggest a strong relation between the effect of aerosol emissions on cloud properties and W. These highlight the importance of improving the representation of subgrid W variability in GCMs.Because of the separation between the relevant scale for ice nucleation and the scale resolved by the GCMs, it is likely that several cloud formation events occur within a model grid cell. This unresolved variability is characterized by a “subgrid” distribution of vertical velocity, σw largely determines the balance between homogeneous and heterogeneous ice nucleation during the formation of cirrus.In this work we develop a method, using ultra high resolution global simulations to directly calculate the global distribution of subgrid vertical velocity affecting cirrus formation. By implementing our estimates in a global model and running experiments constrained by observations we show that the global distribution of 41. The simulation was completed using a cubed-sphere grid with a nominal spatial resolution of 7 km and 72 vertical levels, extending from the surface to 0.1 mbar. The time step for integration was set to five minutes and three-dimensional instantaneous meteorological fields were saved every hour. The vertical resolution of the model in the upper troposphere is about 0.5 km. This simulation is referred as the GEOS-5 “nature run” (G5NR). The output of the simulation amounts to about four petabytes and is used to perform Observation System Simulation Experiments42. The G5NR is capable of resolving mesoscale systems and organized convection within large scale midlatitude cyclones, important for the simulation of cirrus40.We developed a method to calculate W from the 7 km G5NR output to 1° (~100 km) horizontal resolution  so that at least 256 W values from the G5NR were used to calculate σw for each 1° grid cell. This procedure resulted in a hourly, three-dimensional global characterization of σw for 200 km global resolution, producing essentially similar results as our 100 km calculation, indicating that very little of the total W variance is resolved at scales greater than 100 km. A second sensitivity test was performed collocating results from a short term 3.5 km resolution run spanning 15 days in May 2006 to the 1° spatial resolution. After scaling was applied  results in α1,0 = 1.46. For r1 = 3.5 km, α1,0 = 1.26. The choice of r0 is rather arbitrary, however for small enough values has little effect on α1,0 . Here r0 is selected much smaller than the typical cloud size to account for short-lived fluctuations that may affect the relaxation of supersaturation in the cloud45.The G5NR resolves waves and vertical motion with periods from minutes to a few hours which are the main drivers of cirrus formationr1,σw = α1,0σw,7km. Equation  and the Manus island in the tropical western Pacific . SGP is a mid-latitude continental site with large variability in temperature and cloud occurrence. Manus is an oceanic site off the coast of Australia with frequent tropical convection. Ground-based radar retrievals13 of vertical velocity at each site over the period (2000–2010) were obtained from the Atmospheric Radiation Measurement program (www.arm.gov/sites). The retrieval algorithm is based on a decomposition of the Doppler vertical velocity. The typical error in vertical velocity is about 15 cm s−1 for a minimum reflectivity of about −40 dBz13. The whole data set spans about 10 years for each site. Data obtained at 10 s intervals for each month were averaged over 5 min to match the time step of the G5NR simulation.The vertical velocity fields from the 7 km simulation were validated against ground-based measurements within cirrus for two different sites with diverse topography and synoptic conditions, and for which long term radar retrievals are availableW values over a 0.5° × 0.5° area centered at each site were obtained from the 7 km simulation at three hour intervals. This corresponds to about 15360 values for each monthly distribution at each site. Notice that this includes the spatial and temporal components of the variance since it is difficult to separate them in the observations. The model results were restricted to ice mixing ratios above 5 × 10−5 kg kg−1 and corresponding ice water content of about 50 mg m−3, selected to match the maximum sensitivity of the retrieval method13. To generate G5NR vertical velocity distributions at the SGP Fig.  and Manuin situ turbulence and high-frequency gravity waves13, the distributions were scaled to 0.1 km horizontal resolution using the method outlined in the previous section. Equation  and from the G5NR (which are available for two separate years). The results are plotted in Fig. σw show little interannual variability, i.e., for the same month the spread in σw between years is typically below 0.05 m s−1. σw at the SGP site shows a strong annual cycle, whereas at the Manus site it is relatively constant over the year. This suggests that location plays an important role in determining σw. The distinctive behavior of σw at each site is well represented by the simulation. However the G5NR tends to predict a stronger annual cycle of σw at the SGP site than implied by observations, with the maximum σw occurring too early during the year and an underestimation in σw between August and October, likely related to the low frequency of convective events during the Fall season in the G5NR40.Seasonal variation in the large scale environment may lead to differences between the simulated and observed distributions. Such deviations are typically within the margin of error of the observations. However they can also signal systematic errors in the simulated σw was used to drive ice nucleation in GEOS-5 and study the impact on the balance between HOM and HET processes. Due to the high computational expense of the 7 km simulations, aerosol-cloud interactions are analyzed in the lower resolution, 100 km, simulation, but using σw obtained from the 7 km run. The goal of the GCM implementation is to analyze the statistics of the ice crystal concentration when the G5NR-generated distribution of σw is used. The main premises behind this approach are that the interannual variability in σw is small and that σw is mostly influenced by local features and convection. This is supported by the low interannual variability in σw found in our 7 km simulation, and in the 10-year-long time series of radar retrievals at the SGP and Manus sites 51, using a relaxation time scale of 6 h. Monthly-averaged σw derived from the G5NR was used to drive ice nucleation. To perform ice nucleation studies, the model was run for five years (2005–2010) using a c90 cubed-sphere grid  and 72 vertical levels. Cloud physical and optical properties obtained in similar simulations have been shown to be in agreement with available in situ and satellite observations49.The model’s cloud microphysics and ice nucleation schemes are described elsewhere18 show that cloud formation becomes HET dominated within a relative narrow range of INP concentration, typically less than a factor of two. Thus the 80% limit represents the INP concentration at which Ni becomes strongly affected by HET nucleation. Results using different thresholds, i.e., 20%, 50% and 90% are discussed below. Bivariate Ni vs. T distributions were calculated by counting the number of data points falling within a particular Ni and T combination, using 80 logarithmic bins for Ni and 80 linear bins for T. The frequencies were then normalized by the most frequent Ni vs. T combination within the entire domain. Using this method the expected value of Ni is located around the maximum frequency at each temperature.A cirrus formation event was considered dominated by homogeneous freezing when at least 80% of the ice crystals were produced by the HOM mechanism. Modeling studieshttp://opensource.gsfc.nasa.gov/projects/GEOS-5/.The GEOS-5 source code is available under the NASA Open Source Agreement σw derived from the G5NR. High values of σw are found around the Inter-Tropical Convergence Zone (ITCZ) and over continental mountain ranges, confirming the notion that underlying convection and orography are the main drivers of dynamic variability in the upper troposphere52. For the same reason σw is typically higher in the Subtropical Northern hemisphere (NH) than in the Southern hemisphere (SH), except over the Andes mountains where σw tends to be high due to orographic uplift. Within the troposphere σw decreases slightly with height, and becomes small above the tropopause due to gravity wave breaking. This is in agreement with observations53 however differs from previous work where W, instead of σw, was assumed to decrease with height55. Figure W do occur at high altitude, but they become less frequent near the tropopause since Figure σw over the globe. These areas are also associated with a low frequency of cirrus clouds1. Low σw results in low Ni and large ice settling velocities, which may contribute to a more efficient cloud removal and explain in part the low cirrus frequency of these regions. At constant pressure σw tends to decrease poleward from the Tropics in both hemispheres due in part to the breaking of gravity waves at higher pressure levels. In NH the lowest σw values are found at the northernmost latitudes (above 80°). In SH the lowest values of σw are found around −60°, however σw increases south of −60° revealing the orographic effect of the Antarctic continent on W, which may impact the formation of polar stratospheric clouds.Aside from the poles, subtropical regions, usually associated with persistent stratocumulus decks and large scale subsidence , display the smallest σw, P(σw), within cirrus, i.e., positive ice water content. As expected, P(σw) decreases exponentially with σw. However several features of P(σw) stand out. P(σw) peaks at around 0.02 m s−1 and decreases rapidly with increasing σw so that about 90% of the values of σw are below 0.1 m s−1. This suggests that in most cases cirrus formation is driven by large scale vertical transport and inertial gravity waves. Higher values of σw (up to 0.8 m s−1) are associated with high frequency gravity waves from convective and orographic sources56. Although likely, they are progressively less frequent as σw increases. Globally about one in 104 non-convective cirrus formation events are forced by vertical motion with σw > 0.8 m s−1. High values of σw are more likely in the Tropics driven by underlying convection. Removing grid-cells with vigorous convection results in about a factor of five lower probability of finding σw > 0.2 m s−1. Values of σw greater than 0.5 m s−1 seem to be equally likely in SH and NH indicating that they may result from strong orographic uplift in the mountain ranges.Figure σw. Over the Tropics the highest σw coincides with the displacement of the ITCZ  is shown in Fig. σw and low T tend to lead to high annual HOM frequency. This is because under such conditions a larger INP concentration is required to offset the increase in RH from expansion cooling, and is evidenced by the correspondence in the spatial patterns seen in Figs σw are more likely, HOM dominates about 50–60% of the cloud formation events, and up to 80% in the coldest regions of the Tropical tropopause, over the Central Pacific, where a lack of INP also contributes to diminish the frequency of HET nucleation. Outside the Tropical and Subtropical regions, the frequency of cloud events dominated by HOM nucleation is generally low (below 30%), particularly in the NH. In fact, in the Arctic clouds tend to form almost exclusively by HET (HOM frequency is below 10%), mostly resulting from the low σw which precludes efficient HOM nucleation, even though the INP availability is low. HOM-dominated cirrus events are also less frequent over North Africa due to the presence of dust and black carbon, and to the absence of orographic features and convection that would produce high σw.The annual mean frequency of homogeneous freezing events during cirrus formation calculated with GEOS-5 . The apparent decrease in NINP at high RHmax is caused by competition between HOM and HET nucleation. For RHmax < 40% where HOM does not occur, the simulated NINP shows similar characteristics as available reports58. This is in part by design as the heterogeneous ice nucleation spectrum used in the model is derived from a collection of available field data50. Figure NINP statistics.The balance between HOM and HET during cloud formation is significantly influenced by the concentration of ice nucleating particles, 54. Our results are however in better agreement with field campaign data suggesting a significant role of dust and other INP species in cirrus formation10, and, lower Ni and higher saturation ratios than implied by HOM14. This suggests that the parameterization of σw may be responsible for the higher HOM frequency typically found in modeling studies. Notably, forcing ice nucleation with our estimate of σw also results in good agreement of Ni with field campaign data at very low temperature , where most modeling studies show high overestimation of Ni36. This suggests that the overestimation in many models may be a result of poorly constrained σw. GEOS-5 however seems to overestimate the frequency of low Ni for T > 230 K. It must be noticed that ice shattering on in-situ probes, leading to overestimation in the in-situ Ni, are a likely artifact of the measurements at such temperatures59. Results for the NH, where most cirrus field campaigns have taken place23 show similar tendencies  leads to global HOM frequency of about 48% which is still low compared to current reports49.The effect of selecting different thresholds to define a HOM-dominated cirrus is shown in the Supplementary Fig. σw has considerable spatial and seasonal variability. It is important for GCM parameterizations of σw to reproduce such a spatial variability. This indicates that parameterizations based on individual field campaigns should be used with caution when applied to the global scale. Large-scale dynamics, turbulence, orographic uplift, and underlying convection impact σw. Even State-of-the-Art parameterizations of σw52 neglect the effect of convection generating gravity waves that can impact cirrus formation, which may result in a too-low frequency of high σw as indicated by Fig. This work reports for the first time the direct estimation of the subgrid spatial variance in vertical velocity at the scale relevant for cirrus formation. Figure σw to drive the GEOS-5 ice nucleation scheme results in a lower predominance of homogeneous ice nucleation than previously simulated, in better agreement with field measurements. Such lower frequency of HOM events also results in a better agreement of simulated Ni with observations, particularly at low T  near the tropopause62. Our results provide a way to reconcile these two views. High values of W do occur at low T, however the low σw at high altitude limits the frequency of cloud formation events driven by high W, and on average leads to lower Ni. This is agreement with previous work suggesting the structuring of cirrus by dynamics45 and an episodic nature of HOM in cirrus14.Using the direct estimate of w T Fig. . This isσw at a global scale. Several uncertainties however remain in the modeling of cirrus clouds, the most significant being the concentration of INP in the upper troposphere. Even though our results are in relative good agreement with available reports (Supplementary Fig. NINP with a wide range of aerosol concentrations, temperature and relative humidity and enough spatial coverage that would allow comprehensive validation of GCM predictions. The characterization of W at low T also requires better techniques with smaller errors, since in some cases they can be as high as σw13. In turn an improved representation of σw in GCMs may help reducing the uncertainty surrounding the estimation of the impact of aerosol emissions on cirrus, and eventually lead to a better prediction of future climate.Our new estimate provides a way to validate new parameterizations of Supplementary Material


Similarly, low viscosity is required to predict the observed delay of the plume activity, which hints at other delaying mechanisms than just the viscoelasticity of the ice shell. The presence of faults results in large spatial and temporal heterogeneity of geysering activity compared to the traditional models without faults. Our model contributes to understanding the physical mechanisms that control the fault activity, and it provides potentially useful information for future missions that will sample the plume for evidence of life. Key Words: Enceladus—Tidal deformation—Faults—Variable ice shell thickness—Tidal heating—Plume activity and timing. Astrobiology 17, 941–954.We investigated the effect of variations in ice shell thickness and of the tiger stripe fractures crossing Enceladus' south polar terrain on the moon's tidal deformation by performing finite element calculations in three-dimensional geometry. The combination of thinning in the polar region and the presence of faults has a synergistic effect that leads to an increase of both the displacement and stress in the south polar terrain by an order of magnitude compared to that of the traditional model with a uniform shell thickness and without faults. Assuming a simplified conductive heat transfer and neglecting the heat sources below the ice shell, we computed the global heat budget of the ice shell. For the inelastic properties of the shell described by a Maxwell viscoelastic model, we show that unrealistically low average viscosity of the order of 10 The presence of an internal liquid water reservoir, suggested by the discovery of jets of water vapor and icy particles by the Cassini spacecraft in 2005  . With its jets and faults, which are very likely connected to the subsurface ocean, this region provides a means by which to probe the interior of the moon. The jet activity is concentrated along four fractures, informally called “tiger stripes,” and varies both spatially and temporally  distribution of material parameters , but an accurate estimate of inelastic properties of ice valid at tidal timescale is not known.The material parameters that control the tidal deformation of the ice shell are elastic moduli and viscosity  must be replaced by the unit vector normal to the  boundary.We adopted the numerical method developed by Souček et al. , who invφS is approximately described in the works of, for example, Hurford et al.  zones that pass vertically through the ice shell, in which the elastic moduli are reduced by 5 orders of magnitude. The geometry of the prescribed weak zones approximately corresponds to the positions of the recently active faults , and of internal structure (without or with faults). The first model, hereinafter referred to as Mk et al. . The icek et al. . The las,et al. , the weacf. Kite and Rubin, et al.,et al.  due to tidal loading is perfectly correlated with the quadrupole distribution of the tidal potential. The stress and displacement fields are symmetric about the equator and, at periapsis, also about the prime meridian. The stress does not exceed 12 kPa, and the maximum displacement at the surface is about 0.8 m.In this section, we discuss the impact of ice thickness variations and faults on the displacement and stress field. As illustrated in V  computed at the same orbital phase are plotted in U suggests that the variations in the ice shell thickness significantly influence the distribution of stress, while the surface displacement is only weakly affected. The local amplitude of stress is controlled by the local ice shell thickness, and compared to model MU, it is strongly (by a factor of about 6) increased in the SPT where the ice thickness reaches its minimum. In a first approximation, the magnitude of stress is inversely proportional to the ice thickness, and the stress pattern in D, where D is the thickness of ice given in U (U and MV is shown in The results for the model Mven in U . The detUF) is illustrated in panels c and d of U . The displacement in the northern hemisphere remains basically unaffected by their presence. The transition zones between the broken blocks with enhanced displacement and the compact parts of the ice shell with only moderate displacement therefore suffer from large stress. The stress field in the SPT has a small-scale structure with pronounced (∼80 kPa) maxima around the tips of the faults. Since the faults are designed as stress free in our model, the areas of enhanced stress do not correlate with the locations of the faults, but they are concentrated around the SPT boundary.The effect of faults on the tidal deformation of an ice shell of constant thickness (model MVF) has a synergistic effect on the deformation in the SPT. The displacement at the faults . We also observe contribution to the gravitational potential on higher degrees. The spectral power at degree 3 is approximately 6% of power at degree 2 for model MVF, 2% for model MUF, and less than 1% for model MV.To allow for comparison with previous works, we have evaluated the potential Love number et al.,Q is represented by a surface heat flux equivalentDespite the fact that the considered ice shell model is purely elastic, we can use the computed stresses to evaluate the dissipation for a Maxwell viscoelastic body, by invoking the correspondence principle and assuming that the Maxwell time (ratio of viscosity to rigidity) is constant in space  and the surface and basal temperatures equal to Ts = 73 K and Tb = 273 K, respectively. We neglect lateral heat fluxes and assume that the shell is thin [(rs − rb)/rs ≫ 1] so that we can solve the heat transfer in Cartesian geometry. Under this simplification, we can express the steady-state basal heat flux as qbasal = q − qtidal/f, where q = -k(Ts − Tb)/[rs − rb] is the conductive heat flux in the absence of heat sources, and the factor f (hereafter referred to as “the heating factor”) characterizes the radial distribution of heat sources. It can be shown that the heating factor f ranges between 1 and 2 for heating profiles increasing with depth, a situation characteristic of tidal heating. The heating factor f = 1 corresponds to the extreme case when all the heat sources are condensed at the base of the ice shell, while f = 2 corresponds to the uniform heat source distribution.The computed estimates of the tidal heat production inside the ice shell can be used to assess the thermal stability in terms of local conditions at the base of the shell. Since convective heat flow within the thin ice shell is not very likely . In that case, the equilibrium line between the freezing and melting regions is given by a simple relation qtidal = fq. Areas where qtidal < fq are characterized by crystallization at the base of the shell, while areas where qtidal > fq are prone to basal melting. The exact location of the boundary between the areas depends on viscosity—the lower value of viscosity is considered to indicate the larger area that is affected by melting. Note that basal melting/freezing does not necessarily imply local ice shell thinning/thickening since this process can be compensated dynamically by a lateral flow in the shell  and f = 2 (uniform source distribution)—and Ptidal is the volume integral of the tidal dissipation. The sign of Pbasal determines whether the overall melting exceeds the overall freezing . The values of Pbasal and Ptidal obtained for different values of ice shell viscosity and the four models considered in this study are summarized in f = 2 is taken into consideration, the transition from global melting to global freezing occurs between 2 · 1013 Pa s and 3 · 1013 Pa s corresponding to values of tidal heating ∼30 GW. For the heating factor f = 1, the stability region is shifted to the viscosity 4–6 · 1013 Pa s and to the tidal heating equal to ∼15 GW. The latter value of heating agrees with the results of Kamata and Nimmo  than in the previous case because the heat loss in the SPT is larger due to a smaller ice shell thickness. However, as in model MU, melting occurs over the entire area of the ice/water boundary if viscosity drops to 2 · 1013 Pa s.To provide the estimate of minimum tidal heating needed for the stability of the ice shell in the absence of deep heat sources, we restrict the further discussion to the extreme case of model MU , heat lomodel MV . Althoug14 Pa s. If the viscosity is only slightly lower (1014 Pa s), the tidal heating dominates over the conductive cooling throughout the SPT. Note that our model takes into account neither frictional heating at the faults nor the energy carried by the jets, and it omits the lateral heat flux, which may be large in the vicinity of faults. These effects, if included in the model, would further enhance the melting in the near-fault region.The melting/freezing pattern becomes more complex if the faults are included . Melting14 Pa s) only if additional heat sources of the magnitude of tens of gigawatts are present in Enceladus' deep interior. However, none of the thermal models discussed above is stable locally. The local stability would require the heat flux from the ocean to be laterally heterogeneous to balance the heat flux Pbasal at the base of the ice shell, and/or the changes in the ice shell thickness due to melting/crystallization would have to be compensated by lateral mass and heat flow within the shell . For models where the faults are implemented (MUF and MVF), we can directly evaluate the faults' opening or closure by investigating the jump in the normal displacement along the faults .Here, we examine the implications of variable ice shell thickness and inclusion of faults on the activity of the faults. Measurements of the brightness of the plume emanating from the faults provided by the Visible and Infrared Mapping Spectrometer . Due to the symmetry properties of the tidal potential, the activity during the second half of the period can be obtained from the one in t = 0.6T can be obtained from that at t = 0.1T if the blue is replaced by red, and vice versa).The activity predicted for the four models considered in this study is presented in U . The picture changes quite dramatically when the faults are included  activity at a point on the fault is proportional to the amplitude of the tensile normal traction component for models without faults or to the jump in normal displacement for models with faults. We then define the predicted activity for each point on the fault as the time average of the local activity over the tidal period, normalized to the maximum over all faults. The spatial variations in the predicted activity obtained in this way are shown in U  cannot explain the discrepancy between the model and the observations. Note that this discrepancy would be even more pronounced in the case of out-of-phase libration. This suggests that either the ice shell thickness in the SPT is larger and/or our interpretation of fault activity in terms of traction/displacement along the faults is oversimplified and some important physical mechanism is possibly still missing in the description of the fault activation. Such a mechanism could be, for example, the hydrology of the fault zone , assuming purely elastic response of the ice. The model of ice shell thickness variations  rheology will be needed to quantify this effect in detail. The faults on the south pole have a negligible impact on the state of stress at the global scale, but their effect on the deformation and stress pattern in the SPT is profound. The inclusion of the faults in the model promotes an overall enhancement of deformation in the SPT, with the maxima of displacement being located in between the faults. A remarkable synergistic effect is obtained when the faults are combined with the polar ice shell thinning, leading to an increase in deformation in the SPT by an order of magnitude compared to the uniform thickness model without faults. The stress and strain concentrate at the tips of the faults and decrease in zones in between the faults.et al.,3  is needed for models with faults. To prevent global crystallization, viscosity of the order of 1013 Pa s would be necessary.We have used the estimated heat production together with a simple estimate of conductive heat losses to investigate the stability of the shell in terms of local melting/freezing conditions at its base. Considering the extreme case where the heat sources in Enceladus' ocean and core are neglected, we find that viscosity lower than 5 · 10e.g., Dhingra et al.,13 Pa s  compared to the outboard ones (Alexandria and Damascus) and by an increase of activity toward the center of the faults. Due to interaction between the faults, the activity maxima of the individual faults are shifted from each other by several hours. If the properties of the ejected particles differ from fault to fault (see, e.g., Goldsby and Kohlstedt, et al.,et al.,13 Pa s, which is highly probable, then additional heat sources in the deep interior are needed to maintain the subsurface ocean in global thermal equilibrium. The nature of possible heat sources in the deep interior has been discussed in a number of papers (Tyler, et al.,14 Pa s. The heat loss of Enceladus controlled by the conduction in the ice shell is thus compensated by interior heat sources of the order of magnitude of 10 GW. This estimate is consistent with the present-day prediction of the equilibrium heat production based on the estimate of the Q-factor in Saturn (Lainey et al.,et al.,Remarkably, the values of viscosity needed to explain the time lag are similar to those needed to reach the melting regime in the SPT. Such average values of viscosity are, however, unrealistically low, at least compared to the available estimates of ice viscosity based on laboratory experiments and mineral physics (s Tyler, , and theet al. (cf. van Hoolst et al.,cf. also Běhounková et al.,cf. geometry of active faults in Porco et al.,et al.,e.g., Castillo-Rogez et al.,The prediction of the overall jet activity is a puzzling problem. For an ice shell that is 25 km thick on average, a satisfactory fit to the brightness data is achieved only for unrealistically low values of viscosity, independently of whether the model includes faults or not. The worst prediction is obtained for the models with variable thickness, which cannot explain the phase lag larger than 4.5 h. This indicates that either the ice shell thickness in the SPT in the model of Čadek et al.  is underWe addressed the influence of faults and uneven ice shell thickness on tidal deformation, tidal heating, and plume activity on Enceladus. We found a remarkable increase in stress, deformation, and tidal heating in the SPT if realistic ice shell thickness and faults are included. The presence of faults results in large spatial and temporal heterogeneity of the predicted plume activity with a possible impact on time variability in the plume composition. The model presented in this study may help to better understand the nature of the faults and to improve the activity prediction. Such information may be valuable for future missions searching for evidence of life on Enceladus.


From 2007 to 2013, the southeastern Bering Sea was dominated by extensive sea ice and below-average ocean temperatures. In 2014 there was a shift to reduced sea ice on the southern shelf and above-average ocean temperatures. These conditions continued in 2015 and 2016. During these three years, the spring bloom at mooring site M4  occurred primarily in May, which is typical of years without sea ice. At mooring site M2  the spring bloom occurred earlier especially in 2016. Higher chlorophyll fluorescence was observed at M4 than at M2. In addition, these three warm years continued the pattern near St. Matthew Island of high concentrations (>1 μM) of nitrite occurring during summer in warm years. Historically, the dominant parameters controlling sea-ice extent are winds and air temperature, with the persistence of frigid, northerly winds in winter and spring resulting in extensive ice. After mid-March 2014 and 2016 there were no cold northerly or northeasterly winds. Cold northerly winds persisted into mid-April in 2015, but did not result in extensive sea ice south of 58°N. The apparent mechanism that helped limit ice on the southeastern shelf was the strong advection of warm water from the Gulf of Alaska through Unimak Pass. This pattern has been uncommon, occurring in only one other year (2003) in a 37-year record of estimated transport through Unimak Pass. During years with no sea ice on the southern shelf , the depth-averaged temperature there was correlated to the previous summers ocean temperature. The Bering Sea is a productive high-latitude sea situated between the Arctic Ocean and the North Pacific . This maThe eastern Bering Sea is ice-free during the summer and much of the fall. Sea ice usually begins to form on the northern shelf in December, with strong, frigid northerly winds both opening polynyas where sea ice forms and advecting the ice southward . The leahttp://www.nprb.org/bering-sea-project) was designed to explore this ecosystem from 2007 to 2010. Because of the historical record of high inter-annual variability, it was expected that at least one of the project years would also support warmer (less sea ice) conditions. Unfortunately for the Bering Sea Project, the warm period of 2001–2005 was followed by a period of extensive sea ice during spring and cold ocean temperatures over the entire eastern shelf (2007–2012) [Historically, the southeastern Bering Sea has been characterized by high year-to-year variability in sea-ice extent in March and April . This hi07–2012) . During Gadus chalcogrammus) in late summer [A major finding from the Bering Sea Project is that a transition exists at ~60°N that divides the eastern Bering Sea shelf into northern and southern regions ,9,10; ine summer ,11; redue summer ; and finAt the end of the Bering Sea Project in 2010, it was not known how long the cold period would continue, nor were there well-formed hypotheses of whether the southern Bering Sea would shift back to high year-to-year variability or into another multi-year period (stanza) of warmth. This paper examines the southeastern Bering Sea starting when conditions turned from cold to warm in 2014. Fortunately, EcoFOCI’s and BASIS’ long-term observational plan supported extensive field seasons in the Bering Sea in 2014 (five oceanographic cruises with 79 days at sea) and 2016 (6 oceanographic cruises with 78 days at sea). In 2015, the long-term plan was structured so that most of the EcoFOCI/BASIS research would be in the Gulf of Alaska. Predictions that 2015 would also be warm resulted in additional funding to plan and execute an extensive field season (four cruises with 59 days at sea) in the Bering Sea.This paper explores the temporal variability on the southern Bering Sea shelf during three consecutive warm years (2014–2016) and compares these years to previous years (1995–2013). Patterns of variability in ocean temperatures and chlorophyll fluorescence are examined at two long-term mooring sites, and related to the presence of ice in March and April. While atmospheric forcing is the primary driver of sea ice on the Bering Sea shelf, evidence is presented that the extremely warm conditions in the Gulf of Alaska in 2015 played a role in limiting the southern extent of ice that year. How chlorophyll fluorescence varies during these years is discussed, as is the surprisingly high and unforeseen accumulation of nitrite in oxygenated waters near St. Matthew Island. These observations reveal similarities and differences in the lower trophic system between the two warm periods: 2001–2005 and 2014–2016.http://www.natice.noaa.gov/), with data available from 1972 to 2002. The second source (2002–2011) was the Advanced Microwave Scanning Radiometer EOS (https://nsidc.org/data/AE_SI12/versions/3). After October 2011, Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) from the National Snow and Ice Data Center  were used. Details are provided by Stabeno et al. [Three sources of sea-ice data were used. The first source was the National Ice Center . NARR uses the high-resolution National Centers for Environmental Prediction (NCEP) Eta model (~32 km grid size) and includes additional assimilated parameters to improve the reanalysis product . ReanalyStabeno et al. [Data collected from a series of moorings deployed at two biophysical sites (M2 and M4) included temperature , salinity (SBE-37), and fluorescence (WET Labs DLSB ECO Fluorometer). At M2, moorings are typically deployed and recovered in May and September, while at M4 one year-long mooring is typically deployed in September. In addition, current measurements were collected at a site in Unimak Pass . All insGordon et al. [Knap et al. [Since 2003, the EcoFOCI and BASIS programs have collected discrete oceanographic samples for nutrient analysis, with some variance in the handling and processing of these samples. During EcoFOCI cruises, samples were filtered through 0.45 μm cellulose acetate filters and either analyzed at sea, or returned to NOAA's Pacific Marine Environmental Laboratory for analysis following protocols of n et al. . During p et al. . In subsp et al. . In botha fluorescence. This instrument was calibrated and the data processed according to manufacturer's specifications.The Prawler is a platform that utilizes wave energy to move an instrument package up the mooring line using a ratcheting device, and then collects data during a free-fall down the mooring line. The rapidity of cycling is dependent upon the available wave energy and settings on the mooring. In May 2016, a Prawler mooring was deployed at M2 (72-m bottom depth) and profiled the upper ~45 m of the water column approximately every two hours. Among the various sensors deployed on the Prawler was a Wet labs ECO FLNTU fluorometer that measured chlorophyll-Sea ice is the dominant characteristic of the eastern Bering Sea shelf ,7, with The interannual variability in spring ice cover discussed in the introduction is evident in the time series of mean ice concentration in a box roughly 100 km on a side centered at M2 . The firOver the southern Bering Sea shelf, the presence/absence of sea ice in March and April largely determines the depth-averaged ocean temperature Figs  and 3A, 2 = 0.86, p<0.01) if 2006 is not included in the analysis. If 2006 is included, then the correlation is not significant (p>0.05), supporting the claim that even a low percentage of areal ice cover, especially if it is in April, can significantly cool the water column.Areal ice concentrations in March and April were inversely related to annual ocean temperatures . In yearTo investigate the water column in more detail during the most recent warm period, we examined the near-surface and near-bottom temperatures at M2 and M4 in 2014–2016. At M2, the near-surface thermistors were at 4 m in May through September and at 11 m the remainder of the year, while at M4 the near-surface thermistor was at 11 m for the entire year. Near-bottom thermistors were ~10 m off the bottom for all moorings for each year. Both M2 and M4 were deployed in the middle domain near the 70-m isobath . The midIce extent is determined largely by atmospheric forcing . Frigid Melting ice rapidly cools only the surface waters, while cooling of near-bottom waters occurs only after vertical mixing of the water column . The thrAfter the first ice event in mid-March 2014, ice remained north of M4 and near-surface temperatures were only slightly cooler at M4 than at M2 through mid-August . In contNear-surface temperatures varied among the three years, with near-surface summer temperatures ~2°C colder in 2015 than in 2014 or 2016. This interannual difference occurred at both mooring sites, even though the depth-averaged temperatures in 2015 at M2 exceeded those in 2014 . This apWhile the most obvious impact of the presence of sea ice after mid-March is cold ocean temperatures, ice also influences the timing of chlorophyll blooms. The spring chlorophyll bloom in years with extensive sea ice in March and April is earlier (April) than years with no ice in March or April ,8. In adDuffy-Anderson et al. [In April 2015, the stratification at M4 resulting from melting ice was associated with increased chlorophyll . This hin et al. . A similn et al. . FinallyIn 2016, the fluorometers deployed on both the winter and summer moorings at M2 showed much higher chlorophyll fluorescence values than in the other two years. In addition, the bloom began in mid-April earlier than is typical for a year without sea ice ,8, likelConsiderable variability on time scales of less than daily is evident in this time series . In the In addition to warmer temperature and later spring blooms during years with reduced sea ice ,8, otherMordy et al. [During warm conditions in 2005, there appeared to have been a disruption in the nitrogen cycle  in the vicinity of St. Matthew Island . Nitrifiy et al. , suggestThe strength and persistence of cold northerly winds are the primary factors that determine the southern extent of sea ice, although extremely warm ocean conditions during the previous summer can delay, but not prevent, the advance of sea ice . DespiteA pair of sea surface temperature (SST) maps shows the apparent intrusion of warm water into the Bering Sea . In late6 m3 s−1 of Gulf of Alaska shelf water flows northward into the Bering Sea through this pass, advecting heat, salt, nutrients and zooplankton onto the southern Bering Sea shelf. In addition, some Gulf of Alaska water that enters the Bering Sea through other Aleutian Passes is advected onto the shelf through Bering Canyon, but the magnitude of this flow appears to be much weaker than that through Unimak Pass [Water from the Gulf of Alaska enters the Bering Sea through the Aleutian Passes, but the greatest net transport of shelf water is through Unimak Pass . During mak Pass . During mak Pass .-1) and strongest in the cold months (21.3 cm s-1). Maximum correlation of flow through Unimak Pass occurs with alongshore (toward 225°) winds [-1) and the alongshore (toward 225°) NARR winds  provided the following relationship:2 = 0.45. Using SST anomalies from a location just upstream  winds . A lineaStrong, positive temperature anomalies developed in the northeast Pacific basin in winter of 2014 . The anoAn examination of the monthly-averaged flow through Unimak Pass  indicateThe presence of sea ice in March and April determines whether the southern Bering Sea will be warm or cold. Our analysis indicates that the more persistent the ice, the colder the depth averaged temperature at M2. In addition, in years with no sea ice during the winter and early spring, the amount of heat in the water during the previous summer strongly influences the temperature in the following year. Finally, the low ice extents in 2014 and 2016 appeared to be primarily a result of anomalously weak northerly winds, but in 2015 strong northward flow of warm Gulf of Alaska water through Unimak Pass likely contributed to limiting ice extent on the southern shelf. This warm water resulted from the positive temperature anomalies that dominated the coastal Gulf of Alaska in 2015. With the available data, it is not possible to determine whether the warm ocean temperatures in the Gulf of Alaska or the magnitude of the northward flow was more important in limiting ice over the southeastern Bering Shelf, but we suggest that it was a combination of both strong transport and warm ocean temperatures in January-March together that limited sea-ice advection.This second occurrence of consecutive low-ice/warm-ocean years lends support to the paradigm that the Bering Sea has shifted from a system with high year-to-year variability to a system of stanzas–multiple years of warm followed by multiple years of cold. It is not understood what may have caused such a shift, or if it is just a temporary change, but, hopefully, examination of the large-scale climate models can provide insight into this question.Duffy-Anderson et al. [While 2014–2016 were similar to 2001–2003 (the first three years of the previous warm period) in limited ice extent, warm ocean conditions in the vicinity of M2, and localized high nitrite values, there were some subtle differences. At M2, the timing of the spring chlorophyll bloom was earlier (~2 weeks) in 2014 and 2016 than in 2001–2003, while at M4 the timing was more comparable between the two periods . While tn et al. . The warhttp://www.weather.gov/afc/ice), but by June 2017 the depth-averaged temperatures at M2 were average, indicating that ocean temperature near M2 in 2017 (like the 2006 transition between the previous warm and cold stanzas) will likely be average. With this end of a warm stanza, questions arise. Will the Bering Sea continue in a pattern of warm/cold stanzas? On a broader note, if the southern Bering Sea has shifted from high interannual variability to a pattern of stanzas of warm and cold years, what will be the impact on the Bering Sea ecosystem? Specifically, will these three years suffice to examine one of the primary hypotheses that resulted from the Bering Sea Program: that multiple consecutive years of little or no sea ice on the southern shelf would result in poor young-of-the-year pollock survival in the second and succeeding years [This latest string of three warm years apparently ended this year 2017). In March, sea ice had covered M4, and the ice edge was near or at M2 on 13 March 2017 (. In MarcS1 DataMoored temperature data presented from 2014–2016.(ZIP)Click here for additional data file.S2 DataMoored chlorophyll fluorescence data presented in (ZIP)Click here for additional data file.S3 DataNitrate concentrations collected on the Bering Sea shelf (2013–2016) and presented in (XLSX)Click here for additional data file.S4 DataChlorophyll fluorescence data collected by prowler and presented in (XLSX)Click here for additional data file.


Conversely, the power driver was found to increase the translational distance in the critical region, with mean deviations of 2.45 mm for the power driver compared to 1.54 mm with the manual driver. The increase in translational deviation was significant (p = 0.002) indicating that there may be some loss in performance from the adoption of the power driver.This work presents an analysis and comparison of the efficacy of two methods for pedicle screw placement during posterior spinal fusion surgery. A total of 100 screws , all placed utilizing a surgical navigation system, were analyzed and compared. Final screw placement was compared to initial surgical plans using the navigation system, and the final screw locations were analyzed on the basis of angular deviation from these planned trajectories as well as screw translation within a critical reference plane. The power driver was found to insignificantly decrease the resulting angular deviation of these pedicle screws with a mean deviation of 3.35 degrees compared to 3.44 degrees with the manual driver ( Spinal fusion procedures are used to treat a variety of spinal conditions, including injuries to the vertebrae, spinal instability due to infections or tumors, and abnormal spinal curvature. Spinal fusions with instrumentation are typically able to relieve pain in the lower back and lower extremities and correct abnormal spinal geometry , 2. The Posterior spinal fusion (PSF) is a procedure in which bone growth is promoted between two or more vertebrae, eventually joining them into a single bone. This procedure may be performed for a variety of reasons, including damage to the spine such as degenerative changes, fracture or damage to an intervertebral disc, spinal deformations such as scoliosis or kyphosis, spinal instability due to infection or tumor removal, or relief of pain due to abnormal bone growth as in spinal stenosis , 2.During a PSF procedure, pedicle screws are inserted into the vertebra pedicle  to allowDue in part to the critical nature of the spine and the nervous tissue protected by the spinal column, PSF procedures involve a range of risks, including the potential for nervous tissue damage. In particular, complications may occur due to inaccurate pedicle screw placement that results in a medial breach or perforation of the pedicle. In addition, poor screw placement may allow for premature loosening of screws, potentially resulting in a failure to fuse. For these reasons, significant emphasis is given to the careful training of surgeons in the correct placement of pedicle screws. Expert knowledge of the spinal anatomy and relevant anatomical landmarks in tandem with guided training and experience is paramount for surgeons to accurately place pedicle screws.While accurate pedicle screw placement can be achieved by skilled surgeons without the use of imaging or navigation tools , 5, a vaEven with the use of navigation aids for pedicle screw placement, spinal fusion procedures still pose significant technical challenges to surgeons and require intense physical effort from the surgeon throughout the procedure. In addition, as prolonged operative time has been shown to increase the risk of infection and other complications for many minimally invasive procedures, a reduction of operative time stands to significantly benefit patients –14.Given the importance of minimizing operative time as well as overall surgeon effort during spinal fusion procedures, the use of power-assisted drivers is being adopted. The power driver used in this study has been designed to decrease surgeon fatigue during screw insertion. In addition, according to the manufacturer, this driver may offer significantly reduced procedure times, demonstrating a 51% reduction in the time required for the tapping of the pedicle and a 55% reduction in the time required for inserting pedicle screws as described in a study conducted by the manufacturer .While the development of the power driver may aid in the advancement of PSF, to the authors' knowledge, no comparative accuracy study between a power and manual driver has been conducted. This work aims to provide quantitative data comparing the two methods of pedicle screw insertion.The study methodology is divided into five sections. The first section briefly describes the study design. The second section describes the collection of data using the StealthStation and O-arm imaging system  for both manual and power procedures. These systems are used for all patients throughout this study and use intraoperative 3D scans and optical tracking to provide pseudo-real-time feedback to the surgeon with regard to tool position relative to patient anatomy. The third section describes the postprocessing steps that were taken after the procedure to determine the final screw placement. Finally, the last two sections detail the two metrics used to assess procedure performance: angular deviation and reference point deviation. All research was conducted in accordance with a test protocol approved by the University of Colorado Anschutz Medical Campus's Institutional Review Board (COMIRB Protocol 11-0990).After local IRB approval, a consecutive series of patients undergoing posterior spinal fusion were prospectively recruited, and those agreeing to participate were consented for inclusion in the study. Patients meeting the following inclusion criteria were eligible for enrollment: (1) aged 18–75, (2) scheduled to undergo thoracic and/or lumbar posterior spinal fusion procedures with pedicle screw placement using O-arm and StealthStation at the University of Colorado Hospital, and (3) planned fusion involved at least 2 motion segments. Exclusion criteria were (1) pregnancy and (2) revision surgeries with previous pedicle screw instrumentation. All procedures were performed by two surgeons with more than 10 years of clinical practice, including regular use of navigation during pedicle screw placement. The power driver was available to the surgeons for a limited time during the study, so all subjects enrolled during that time period had screws placed with the power driver. Patients enrolled outside of that time frame had pedicle screws placed with a manual driver.Once general anesthesia was administered to the patient, access to surgical space was achieved via open incision. A reference arc is rigidly attached to one of the patient's vertebra  and the imaging system is moved into position to take the first 3D scan with the 3D camera system in place. This scan captures the spinal region to be fused and locates the reference arc with respect to the patient's spine .Following the reference arc attachment and initial 3D scan, tool positions and orientations are tracked via the surgical navigation system and overlaid onto the initial 3D scan. This visualization allows for pseudo-real-time navigation of the tools with respect to the patient's anatomy with an accuracy of approximately 1 to 1.4 mm –19. It sBefore each screw is inserted, several initial steps are taken by the surgeon. First, a surgical awl is inserted into the pedicle. The awl, which is tracked via the navigation system, is positioned with its tip at a depth within the pedicle isthmus which the surgeon believes most susceptible to a medial breach. This initial “Entry” trajectory depth position is saved by the technician operating the navigation system . Once thWith these initial steps complete, and the Entry and Plan trajectories saved, the surgical awl is removed and screw insertion can begin . For each patient, all screws are inserted with either a manual- or power-assisted driver, with the surgeon using the same type of driver for all screws in a given patient. Both tools include passive reflective markers so they can be optically tracked by the navigation system throughout the insertion. When the screw is tightened into its final position, the trajectory of the driver is saved in the navigation system as the “Screw” trajectory. These steps, including the insertion of the awl and screw insertion, are then repeated for all subsequent screws.After all screws have been inserted, a second 3D scan is taken which serves to capture the final locations of all screws relative to the reference arc. Once this scan has been completed and saved, the reference arc is removed. In total, 64 manually driven and 36 power-driven screws were inserted in 11 patients throughout this study.Following each spinal fusion procedure, O-arm scans were examined to assess final screw placement. To do this, the two 3D scans  must be carefully aligned and merged using the second scan as the reference image. An initial merge of the scans is done automatically via StealthStation's Cranial Suite; however, this merge is not accurate enough to allow for good analysis, and it is not possible to export these images to 3rd party software for custom merging. Thus, a final, manual merge is completed by a single user trained by Medtronic in the correct operation of the StealthStation. This user must visually align the two scans until no discernable difference exists in any of the three imaging planes . Several tools, including zoom, color, and transparency adjustment, are built into the Cranial Suite to allow for the effective and accurate alignment and merging of the two scans. While all postprocessing was performed by a single user, no statistically significant difference was seen when comparing this primary user to a trained, secondary user.Once the scans have been merged, the final position of each screw must be drawn . This isWhen all data have been exported from the navigation system, they are imported into MATLAB  for analysis. The Actual trajectory's entry and terminal points are multiplied by the transformation matrix corresponding to the merge, and all trajectories are then drawn and visualized in MATLAB to correlate imported data to StealthStation data and as a visual verification to ensure no unexpected errors occurred while drawing trajectories or exporting the data. At this point, all data are ready for final comparison and analysis.A common metric used to assess the success of pedicle screw placement is the angular deviation between the final screw location  and the intended screw location (Plan trajectory). Given the assumption that the Plan trajectory and the Actual trajectory should have nearly identical entry locations, it is expected that translational deviations between trajectories will be very small. Thus, small angular deviations between these trajectories should be indicative of precise screw placement. As the initial hole created by the awl serves as the entry point for the screw, this assumption should be valid in most cases. It should also be noted that while the above does make the assumption that the vectors share a common origin, this does not affect the calculated angular deviation.Tα and Tp are vectors representing the two trajectories being compared. Once the Actual trajectory has been transformed into the Plan/Screw trajectory frame, the angular deviation between any two of the three trajectories can be calculated using , where TIn addition to calculating the angular deviation between the Actual and Plan trajectories, angular deviations were also calculated between the Actual and Screw trajectories as well as the Screw and Plan trajectories.While the angular deviation calculation is a commonly used calculation for determining accurate screw placement, this calculation does not assess any translational differences that may occur between trajectories. More importantly, while angular deviation provides a useful metric for determining overall alignment of pedicle screws, it does not necessarily indicate how close these screws came to breaching the pedicle isthmus. Because a pedicle breach  represents a key failure criterion when assessing pedicle screw placement and because any movement of the patient or reference arc may result in unintended translational errors, an additional metric was employed to compare screw placement within a critical region of the pedicle isthmus .The critical region is determined via the Entry trajectory that is recorded initially as the awl is pressed into the pedicle isthmus. This trajectory ends with the tip of the awl at a point which we will refer to as the critical depth (CD) within the isthmus. This point does not necessarily represent the center of the isthmus, but is estimated to be close to the depth within the isthmus where the risk of perforation is the highest.Given point CD, the closest point along the line defined by the Plan trajectory vector to CD can be obtained. This new point is used as the reference point (RP), and points RP and CD can then be used to define a plane within the pedicle which we will refer to as the critical region .As shown in , vector Once point RP has been determined along the Plan trajectory, it is then used to compare with the Screw and Actual trajectories . Thus, tIn addition, because the Screw trajectory is also navigated, the final reference point deviation of the Actual trajectory from this additional reference point was also considered. While this trajectory is not shown in α2 and α1 represent the terminal point and entry point of the Actual trajectory, respectively. The orthogonal distance that is calculated is thus the shortest distance between the Reference Point and the Actual trajectory.The distance from the marked reference point along the Plan trajectory to the Actual trajectory  is calculated according toAll computations were performed in MATLAB with reference points, trajectories, and distances drawn graphically to provide visual verification.p values shown in these results.Results are divided into two sections. The results of the angular deviation analysis are presented, followed by the results of the reference point analysis. It should be noted that unequal sample sizes between the two groups and skewed data distributions were anticipated (and observed) in the results of this study. For this reason, a permutation method was implemented to determine the p = 0.853, p = 0.856, p = 0.913 for Plan versus Actual, Screw versus Actual, and Plan versus Screw, resp.).Angular deviations for all screws were calculated across both the manual and power groups. These results are shown in p = 0.002) and Actual versus Screw trajectories (p = 0.009). While Screw versus Plan trajectories were not found to be statistically significant using this metric (p = 0.321), this was to be expected, as both of these trajectories are entirely dependent on the navigated tools and do not rely on the final position of the screw within the pedicle.Reference point deviations for all screws were calculated for both the manual and power groups. The results of this comparison are shown in The results of this study suggest that there is some small performance lost via use of the power driver relative to the manual pedicle screw driver. While angular deviations from the Plan trajectory were statistically insignificant with means in favor of the power driver, the statistically significant translational errors in the critical region indicate that the power driver was less able to follow the Plan trajectory accurately through the pedicle isthmus, potentially increasing the risk of perforation in this region. However, in this study, no perforations were observed.This study presents a novel metric for assessing pedicle screw placement performance, utilizing a reference point within the pedicle to compare trajectories in 3D. While previous work has relied on the more simplistic review of X-rays and CT scans, to our knowledge, no previous work has attempted to use a metric such as reference point deviation in assessing screw placement.The conclusions drawn from this study are based entirely on the metrics presented here. It should be noted however that this paper is not intended to present a risk–benefit analysis of the manual versus power drivers but rather to provide an analysis of two important performance metrics: angular deviation and reference point deviation.While the reference point deviation metric has, to our knowledge, not been used previously, the angular deviation metric has been used in other studies to access the accuracy of pedicle screw placement using 3D navigation . The meaOne method which is often cited to access successful screw placement is the Gertzbein-Robins criterion, which defines success based on the distance in mm by which a screw breaches the pedicle . While tMany factors may influence whether a surgeon chooses to use a particular tool, and as different tools may entail different risks and benefits, it is important to recognize the limitations of these tools with respect to specific patients and procedures. As noted previously, the power driver used in this study has a number of potential advantages that may offset some of the performance risks noted here. In particular, the projected, decrease in procedure time may offer overwhelming benefits to both the patient and the surgeon. The decreased risk of infection that often stems from shorter, minimally invasive procedures may, in effect, be a much more important factor than any observed decrease in critical region accuracy.In addition, the long-term impact of this driver on the surgeon is an important factor to consider. Manual tools used in minimally invasive procedures often require intense physical exertion from the surgeon over long periods of time. This exertion can result in localized muscle fatigue in the short term, potentially impeding a surgeon's ability to perform procedures quickly and accurately. Perhaps most importantly, repeated exertion at the level required by these tools and procedures may lead to repetitive stress injuries to surgeons over time, further impeding their endurance in the operating room.Improved ergonomics and decreased insertion time may thus offer significant benefits to the surgeon and patient; however, length of procedure and surgeon fatigue were in no way evaluated in this study. To our knowledge, no independently conducted study currently exists demonstrating these impacts with the power driver used here. Thus, despite the results of this study, further work is needed to determine whether the overall performance of the power driver significantly impacts patient outcomes for better or worse.


The El Niño-Southern Oscillation (ENSO) can significantly affect the rapid intensification of tropical cyclones over the western North Pacific (WNP). However, ENSO events have various durations, which can lead to different atmospheric and oceanic conditions. Here we show that during short duration El Niño events, the WNP tropical cyclone rapid-intensification mean occurrence position migrates westward by ~8.0° longitude, which is caused by reduced vertical wind shear, increased mid-tropospheric humidity, and enhanced tropical cyclone heat potential over the westernmost WNP. The changes in these factors are caused by westward advected upper ocean heat during the decaying phase of a short duration El Niño. As super El Niño events tend to have short durations and their frequency is projected to increase under global warming, our findings have important implications for future projections of WNP tropical cyclone activity. El Niño events have important impacts on tropical cyclone activity over the western North Pacific. Here, the authors show that short duration El Niño could significantly shift the tropical cyclone rapid intensification westward by transporting ocean heat energy to the western Pacific. As western North Pacific (WNP) TCs account for nearly one-third of total TCs globally each year, it is important to understand the WNP TC variability as well as its underlying mechanisms. The El Niño-Southern Oscillation (ENSO) modulates many aspects of the WNP TC activity6. Previous studies have found a close relationship between TC activity and changes in ENSO-related atmospheric environmental parameters, such as the vertical wind shear (VWS), relative humidity (RH), and relative vorticity6. Although these studies have improved the predictions of TC tracks, forecasting TC intensity change, especially TC rapid intensification (RI), remains challenging9. ENSO events are important contributors to the WNP TC RI because they affect both large-scale atmospheric circulation and oceanic processes8. The large-scale atmospheric circulation associated with ENSO events can lead to meridional shifts in the RI TC (TC with RI process) genesis positions8, whereas ENSO-induced upper ocean heat content (OHC) is an important contributor to TC RI over both of the eastern Pacific and the Atlantic11. A recent study revealed that the ocean heat meridionally discharged by ENSO can significantly increase the TC frequency and intensity over the eastern North Pacific12. The WNP subsurface OHC is also affected by ENSO13, however, the relative roles of ENSO-related changes in the large-scale atmospheric circulation and OHC in affecting the WNP TC RI remain unclear.Tropical cyclones (TCs) have the potential to cause severe damage to the economy, infrastructure, and inhabitants in affected areas15. SD El Niño events decay rapidly into La Niña events after reaching their peak intensity, whereas LD El Niño events extend to the end of the decaying year with sea surface temperature (SST) anomalies remaining positive over the middle Pacific. Several mechanisms have been proposed to explain the termination of an El Niño event such as the western boundary reflection of Rossby waves16, the Ekman transport under local circulation of the western Pacific anticyclone17, and the recharge–discharge oscillator18. These mechanisms capture the important role of the upper OHC change in determining an El Niño’s duration14. However, previous studies have not distinguished between SD and LD El Niño events when exploring the relationship between the El Niño and WNP TC RI. Whether the El Niño event types have different impacts on the WNP TC RI and the role of upper OHC change in affecting TC RI remain unsolved problems. By using multiple atmospheric and oceanic datasets, we found that the WNP TC RI shows an obvious westward migration during the SD El Niño decaying phase due to the westward advection of subsurface OHC.El Niño events, which are the warm phases of ENSO, can be categorized into two subtypes based on their duration: short duration (SD) and long duration (LD)The two types of El Niño events have distinct evolutionary features during their decaying phases. The Niño-3.4 indices for the four SD El Niño events (see Methods section) enter negative phases during summer and autumn, which indicates that these El Niño events are of SD and followed by La Niña events Fig. , whereas19, which leads to a cooled central Pacific. Similarly, the central Pacific is also cooled during the SD El Niño decaying phase. However, there is no westward migration of RI occurrence position during La Niña events  and China Meteorological Administration (CMA), to validate the Joint Typhoon Warning Center (JTWC). The composite RI occurrence position during the SD El Niño decaying phase for both JMA and CMA show obvious westward migration toward the SCS-WPS region , but that for the mean TC genesis longitudes is only –5.56° (P = 0.108). This indicates the westward migration of TC RI occurrence position is not caused by the westward migration of RI TC genesis. The composite difference between the mean RI TC genesis longitude and the mean RI occurrence longitude is further compared for each of the three occasions: SD El Niño events, LD El Niño events, and neutral years. During SD El Niño events, the composite difference between mean RI TC genesis longitude and mean RI occurrence longitude is 7.98° (P = 0.0493), that in LD El Niño events is 4.88° (P = 0.1468), and that in neutral years is 5.5° (P = 2.89 × 10–8). These results further suggest that the westward migration of RI occurrence position during SD El Niño decaying phases is independent of the change in TC RI genesis position.To quantitatively evaluate the influence of SD El Niño events on the westward migration of the mean RI occurrence position, the composite difference of several RI-related variables between different types of El Niño events and the neutral years are shown in Supplementary Table P = 0.023) over the eastern WNP. This indicates that the RI TCs formed over the open sea during SD El Niño events have a delayed RI occurrence relative to the neutral years  over the western WNP. This means the RI TCs that formed over the western WNP during SD El Niño years occur earlier than that during neutral years on average. The above results imply that the environmental conditions over the western WNP during SD El Niño events may be more favorable for RI occurrence than that in the eastern WNP, which makes TCs formed remotely over open sea to move westward and begin RI. The mean RI duration time difference for SD El Niño events is 4.52 h (P = 0.078), which is longer than during neutral years. During LD El Niño events, all the above variables are not statistically significant. For the composite difference between all El Niño events and neutral years, only the RI duration time is statistically significant with 3.64 h (P = 0.024) longer than neutral years. This will not be further discussed because all other variables are not statistically significant.The composite difference in the mean RI occurrence time between SD El Niño events and the neutral years is 43.69 h , may significantly affect the TC RI35. The RH shows positive anomalies over the SCS-WPS region during SD El Niño decaying phases  for SD El Niño events and –1.17 per h (P = 0.7607) for LD El Niño events, both of which are statistically insignificant, and this indicates that the previous intensity change may not affect the WNP TC RI change. Not all predictors can affect the RI within a single ocean basin. The dominant predictors are different among various ocean basins42. Over the SCS-WPS region during SD El Niño decaying phases, the 200-hPa divergence shows positive anomalies, and the vertical velocity anomalies indicate upward motion in the entire troposphere  over the SCS-WPS region. The SSTA can be affected by both the upward oceanic heat transport and the surface heat flux. Similar to the OHC pattern, there are positive anomalies for the SSTA over the SCS-WPS region. This indicates that the upper OHC plays a role in transporting oceanic heat to the sea surface to offset the cooling effect caused by TC or other factors, which maintains the SSTA as positive. The SST warming over the SCS-WPS region combined with SST cooling over the middle Pacific can lead to a weakened VWS over the SCS-WPS region, which provides a favorable condition for TC genesis and intensification. However, the TC intensification needs persistent energy fueling. The positive TCHP over the SCS-WPS region indicates that the OHC increase is large enough to provide persistent energy fueling for the TC intensification. In brief, the SD El Niño induced OHC increase over the SCS-WPS region is large enough to maintain a positive SSTA and provide extra energy  for fueling the TC intensification.18. The cooled equatorial subsurface and warmed off-equatorial subsurface are caused by a meridional discharge of the OHC driven by Sverdrup transport  to February(1), both the SD and LD El Niño events reach their peaks and coincide with subsurface warming in the middle and eastern Pacific, and subsurface cooling in the western Pacific  waves propagating westwards (eastwards) which are reflected as upwelling Kelvin (downwelling Rossby) waves after reaching the western (eastern) boundaries. The upwelling Kelvin wave and downwelling Rossby wave can produce westward zonal currents, which is consistent with the observed the boundary-originated zonal currents   reanalysis, which has a horizontal resolution of 2.5° × 2.5° in longitude and latitude49. The 40-year European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-40)50 was employed to validate the RH anomalies in the NCEP-NCAR reanalysis. The SST dataset used to analyze the evolution of El Niño events was from the Extended Reconstructed SST version 3b (ERSST), which has a horizontal resolution of 2.0° × 2.0° in longitude and latitude51. The monthly mean oceanic temperature, and zonal and meridional currents were obtained from the Simple Ocean Data Assimilation (SODA) dataset, which has a horizontal resolution of 0.5° × 0.5° in longitude and latitude and 40 vertical layers52.The best-track TC dataset used in this study was from the JTWC, which covers the period 1950–201014. Thus, among the nine El Niño events, the SD El Niño events are 1972/1973, 1982/1983, 1986/1987, and 1997/1998, and the LD El Niño events are 1951/1952, 1957/1958, 1965/1966, 1976/1977, and 1991/1992. El Niño Modoki events were categorized into three subtypes according to their evolutionary patterns21: prolonged decaying , the abrupt decaying , and the symmetric decaying . For all El Niño events, the first year is the developing year and the second year is the decaying year. The months in developing years are denoted by (0), while the months in decaying years are denoted by (1). When excluding the SD El Niño, LD El Niño, El Niño Modoki, and La Niña events, the remaining years during 1950–2010 are defined as neutral years. As the El Niño’s evolution has phase-locking feature with mature phase in boreal winter, we choose 2 years’ time windows to evaluate the SD and LD El Niño events, with the first year denoting the developing phase and the second year denoting the decaying phase. The decaying phase of LD El Niño events can last longer than 1 year, but we still use 1 year as the decaying phases of both SD and LD El Niño events because the two types of El Niño events can already be distinguished in the first year during their decaying phase, and the focus of this study is also in the first decaying year.The El Niño events were identified using the 3-month running mean Niño-3.4 indices  >0.5 °C. The monthly Niño-3.4 index has shown an upward trend since the 1950s. To avoid the influence on the identification of the El Niño events, the monthly Niño-3.4 indices of each 5-year periods are calculated using different 30-year base periods to remove the warming trend of Niño-3.4 index following the Climate Prediction Center (CPC). For instance, the Niño-3.4 indices during 1950–1955 will be based on the 1936–1965 base period, Niño-3.4 indices during 1956–1960 will be based on the 1941–1970 base period, and so on and so forth. The trend has influence on El Niño magnitude, but does not influence the classification of SD and LD El Niño events  is the in situ temperature. T100 is the vertically averaged ocean temperature between the depth of 5–100 m, while the SST is the temperature at the ocean surface. The T100 can represent the upper OHC, while the TCHP indicates the portion of OHC that can fuel the TC activity. Thus, T100 changes can affect both the SST and TCHP anomalies. In addition, the SST changes can be affected by other factors, such as the solar radiation and cloud, and it is also important in driving the large-scale circulation. To estimate an approximate contribution of T100 to TCHP and SST, the SCS-WPS regional mean T100 anomalies are correlated with those of the TCHP and SST. It turns out that the correlation coefficient is 0.94 (88% explained variance) between the T100 and TCHP anomalies and is 0.78 (61% explained variance) between the T100 anomalies and SSTA. T100, TCHP, and SST are dynamically connected and their physical mechanism is unique, that is, the more time the TCs are moving over the ocean, the warmer subsurface water needed (a larger T100 is needed to provide TCHP) because TCs can cool the surface water (SST) and induce upwelling.The TCHP represents the OHC contained in the water warmer than 26 °C. It is calculated as follows–1) is defined as the genesis. A TC RI is defined as having a maximum sustained wind speed increase of at least 30 kt (15.4 m s–1) within a 24-h period33. As the definitions of RI vary55, to verify the robustness of the results, we checked the results based on the definitions of 35 kt per 24 h, 40 kt per 24 h, 45 kt per 24 h, and 15 kt per 12 h, respectively. The westward migration of the RI occurrence position can be observed for the other choices of RI definitions.The time a TC reaches the storm intensity  was imposed to the composite anomalies of RI-related variables, and the atmospheric and oceanic climate factors. To calculate the EDF, we first calculate the decorrelation timescale of the series involved in the composite analysis. For the RI-related variables, the autocorrelation is applied to the series composed by the RI TCs, and because they are not autocorrelated  and LD (N = 5) El Niño years are discrete annual values, they are also not autocorrelated. We still calculated their autocorrelations with 1-year lag and the results turn out to be insignificant, thus the EDFs are equal to their sample sizes. During neutral years (N = 44), the VWS, TCHP and T100 are not autocorrelated, while RH and SST have 2-year decorrelation timescales  on his website. It can be calculated as follows:The maximum potential intensity is an important estimate of the upper boundary of a TC’s intensityVm is the maximum gradient wind speed, Ts is the SST, To is the mean outflow temperature, Ck is the exchange coefficient for enthalpy, CD is the drag coefficient, CAPE* is the convective available potential energy of air lifted from saturation at sea level, which is in reference to the environmental sounding, and CAPE is that of boundary layer air. We use the monthly mean specific humidity, sea level pressure, and air temperature from NCEP-NCAR, and the SST from the ERSST to calculate the maximum potential intensity.where JTWC, JMA, CMA, ERSST, NCEP-NCAR, ERA40, SODA).The original observational datasets used in this study are publicly available and can be downloaded from the respective websites (Supplementary Information


The protein N-myc down-regulated gene 1 (NDRG1) represses tumour metastasis. It is phosphorylated at several sites by serum and glucocorticoid-regulated kinase 1 (SGK1). Here we show that NDRG1 is also regulated by the oncogenic MAP kinase-interacting kinase (MNK) pathway, a target for cancer therapy.in vitro, indicating that additional signalling connections are involved. Taken together, our data indicate that MNK signaling regulates NDRG1 at transcriptional and post-translational levels.Inhibiting MNKs increases the expression of NDRG1 protein and mRNA in breast cancer cells. MNK inhibition also decreases the phosphorylation of NDRG1. Phosphorylation of NDRG1 is reduced in cells lacking MNK1, but not MNK2-knockout cells, indicating that NDRG1 phosphorylation is a specific target for MNK1. However, MNK1 cannot directly phosphorylate NDRG1 We show that SGK1 phosphorylates MNK1 at a conserved site, which represses its activity. NDRG1, SGK1 and the MNKs are implicated in cell migration and metastasis. As expected, knocking-down NDRG1 promoted cell migration. However, whereas MNK inhibition impairs these processes irrespective of NDRG1 levels, SGK inhibition only did so in NDRG1-depleted cells. Thus, MNKs and SGK affect migration/invasion through distinct mechanisms.Our data reveal several novel connections between signalling pathways important for tumour biology. NDRG1) is widely expressed in mammalian cells, especially epithelial cells. NDRG1 has been reported to be a tumour suppressor and/or negative regulator of metastasis in various cancers, including breast cancer  each underwent greater radiolabelling when incubated with SGK1, this was not the case for the S353A or S353A/S372A mutants Figure . Thus, SMutating the SGK1 sites increased MNK1a activity Figure , as showNDRG1 is generally reported to repress migration or metastasis , 32, 33,Knockdown of NDRG1 was efficient Figure  and, as Knocking down NDRG1 also promoted invasion of MDA-MB-231 cells Figure . MNK-I1 Given the potential link between SGK1, NDRG1 phosphorylation and cancer cell migration we used siRNA to deplete NDRG1 in MDA-MB-231 cells and then treated them with SGKi Figure . As alreNDRG1 knock-down also enhanced cell invasion and, similar to the situation for cell migration, SGKi inhibited invasion by NDRG1-depleted but not control cells Figure . These fOur data reveal that MNKs regulate both the cellular levels and the phosphorylation of the metastasis suppressor NDRG1. MNK inhibition impairs cell migration and invasion, while deleting NDRG1 increases these processes. Thus, it is likely that the control of NDRG1 protein levels by the MNKs contributes to their role in regulating cell migration/invasion. However, since MNK inhibition still impairs migration of cells depleted for NDRG1, the MNKs must also control migration via additional mechanisms. Figure NDRG1 mRNA levels, especially in MDA-MB-231 cells. Our data indicate that this reflects increased transcription of the NDRG1 gene, adding to the emerging but still limited information indicating that MNKs regulate gene transcription. For example, eIF4E phosphorylation controls the transcriptional activity of NF-kB ATP, beads with SGK1 protein and 300 ng non-activated MNK were incubated with 0.1 μl [γ-32P]ATP (where used), 3000 Ci/mmol (PerkinElmer) and 0.2 μl non-radioactive ATP (10 μM) (Sigma) in kinase assay buffer  at 30°C for 30 min. Where indicated, HA-NDRG1 (immunoprecipitated from HEK293 cell lysates) or 250 ng eIF4E (expressed in E. coli) were used as test substrates. NDRG1 immunoprecipitates were washed twice with high salt buffer to remove contaminating kinase activity. Products were analysed by SDS-PAGE. Coomassie blue staining was used to assess protein levels and a phosphorimager scanner (PerkinElmer) to visualise the radioactive signals.SGK1 protein was prepared by immunoprecipitation using anti-SGK1 antibodies. For kinase assay with [γ-Non-radioactive assays were performed as described above, using the indicated substrates, but using only unlabelled ATP.


The development of novel targeted agents aimed at selective inhibition of dysregulated oncogenic pathways has been a major focus and advancing area in translational oncology research. In acute myeloid leukemia (AML) the first successes have been in targeting mutations in the receptor tyrosine kinase FLT3. Small molecule inhibitors targeting FLT3 have been in clinical use for the past two decades with many patients showing favorable initial responses; however, development of resistance occurs almost universally. Here we describe mechanisms of resistance to FLT3 inhibitors and ongoing studies aimed at overcoming it.Mutations in FLT3 (FMS-like tyrosine kinase 3) are the most common genetic alteration in patients with AML occurring in approximately 30% of adult and 15% of pediatric patients at the time of diagnosis ; howeverPreclinical studies demonstrating robust anti-leukemic effects of FLT3 inhibition lead to development of ATP-competitive tyrosine kinase inhibitors (TKIs) targeting mutated FLT3 for clinical use. First generation agents with activity against FLT3 such as sunitinib, sorafenib, and midostaurin were multi-kinase inhibitors that also targeted related receptors such as PDGFR and KIT. Given the multi-kinase nature of these compounds, their use was limited due to poor potency against FLT3 and increased toxicity due to off-target activity. To address these concerns, the second-generation TKI, quizartinib (AC220) was developed with increased potency against and selectivity for FLT3. Initial responses to treatment with single-agent quizartinib were promising with 44% of relapsed or refractory FLT3-ITD AML patients achieving a composite complete remission in a phase II study ; howeverFurther analysis of patient samples to better understand mechanisms of relapse revealed secondary point mutations in the FLT3 kinase domain in patients who relapsed during quizartinib monotherapy.The most common quizartinib-resistance conferring mutations occur at the D835 and F691 loci and confer cross-resistance to the first-generation inhibitor sorafenib –19. MutaDevelopment of secondary point mutations represent the best characterized mechanism of acquired resistance to FLT3 inhibition; however, resistant FLT3-ITD cells lacking secondary point mutations have been frequently identified  indicatiActivation of parallel signaling pathways independent of the FLT3 receptor also plays a role in resistance to FLT3 inhibition. For example, upregulation of the receptor tyrosine kinase AXL has been implicated in resistance to TKIs in a number of tumor types –31. SimiThe above findings highlight some of the challenges to successful translation of FLT3 inhibition to clinical application. A number of new inhibitors and dual inhibition strategies have been developed to overcome these barriers. Two of the newer generation FLT3 inhibitors that have advanced the furthest in clinical development are crenolanib and gilteritinib. Crenolanib is a type 1 TKI that is highly selective for FLT3 and PDGFR and retains a high level of activity against D835 mutant FLT3 and to a lesser degree against the F691 gatekeeper mutation . In clinThe ongoing struggles to translate the preclinical promise of FLT3 inhibition into successful clinical use underscore the importance of a deep understanding of the biology of target receptors when developing translational strategies. As it is unlikely that FLT3 inhibition alone will be sufficient to induce durable remissions, ongoing research should be aimed at devising rational combination strategies with other targeted agents based upon understanding of the molecular mechanisms at play. Other promising preclinical lines of investigation include combination with MEK inhibitors  or targe