SPG11-linked hereditary spastic paraplegia is characterized by multisystem neurodegeneration leading to a complex clinical and yet incurable phenotype of progressive spasticity and weakness. Severe cognitive symptoms are present in the majority of SPG11 patients, but a systematic and multidimensional analysis of the neuropsychological phenotype in a larger cohort is lacking. While thinning of the corpus callosum is a well-known structural hallmark observed in SPG11 patients, the neuroanatomical pattern of cortical degeneration is less understood. We here aimed to integrate neuropsychological and brain morphometric measures in SPG11.We examined the neuropsychological profile in 16 SPG11 patients using a defined neuropsychological testing battery. Long-term follow up testing was performed in 7 patients. Cortical and subcortical degeneration was analyzed using an approved, artificial intelligence based magnetic resonance imaging brain morphometry, comparing patients to established reference values and to matched controls.In SPG11 patients, verbal fluency and memory as well as frontal-executive functions were severely impaired. Later disease stages were associated with a global pattern of impairments. Interestingly, reaction times correlated significantly with disease progression. Brain morphometry showed a significant reduction of cortical and subcortical parenchymal volume following a rostro-caudal gradient in SPG11. Whereas performance in memory tasks correlated with white matter damage, verbal fluency measures showed strong associations with frontal and parietal cortical volumes.The present data will help define neuropsychological and imaging read out parameters in early as well as in advanced clinical stages for future interventional trials in SPG11.The online version contains supplementary material available at 10.1186/s13023-022-02451-1. SPG11 cause the most frequent form of autosomal-recessive HSP leading to a multisystem neuronal degeneration [Hereditary spastic paraplegias (HSP) are a heterogeneous group of rare genetic diseases characterized by progressive spasticity and weakness of the legs . Biallelneration –4. Whileneration . As therneration , 7.While the common motor phenotype of HSPs is well validated applying the Spastic Paraplegia Rating Scale (SPRS ) as primLikewise, the known imaging hallmarks of SPG11 are a thin corpus callosum and periventricular white matter hyperintensities , 10, 14,The aim of this study was to comprehensively characterize the cognitive and cerebral atrophy phenotype in a well-defined cohort of SPG11 patients in order to (1) describe the neuropsychological profile of SPG11, (2) depict the progressive impairment in specific cognitive tasks, (3) quantify atrophy of different predefined brain regions, and (4) ultimately correlate the neuropsychological with the cerebral phenotype. The determination of a distinctive pattern of cortical and subcortical degeneration in SPG11 aids the diagnosis and is essential for a specific definition of potential clinical stages in regard to disease progression and for defining important outcome measures for future interventional studies in SPG11.SPG11-linked HSP and the ability to participate in a defined set of cognitive tests. Motor examination included rating by the Spastic Paraplegia Rating Scale , and all participants and legal guardians (if applicable) provided a written informed consent. Inclusion criteria were a clinically and genetically confirmed diagnosis of le (SPRS ) that rale (SPRS ) was perDetailed demographic and clinical characteristics of the study cohort are summarized in Table http://adni.loni.usc.edu). This control group consists of 303 healthy controls  who underwent 3-Tesla cerebral MRI using the standardized ADNI protocol [High resolution MRI of the brain was performed in 12 patients using a 3.0 Tesla scanner  with a gradient field strength up to 45 mT/m (72 mT/m effective). Using the FDA-approved and CE-marked AI-Rad Companion Brain MR for Morphometry Analysis package , MPRAGE images underwent volumetric analysis. Segmentation quality was > 0.7 for all scans. To describe the pattern of brain atrophy in SPG11, the implemented algorithm was used first. It compares normalized morphometric values to reference values derived from a control group of the Alzheimer’s Disease Neuroimaging Initiative . Raw values of baseline and follow-up testing were compared using the Wilcoxon signed-rank test. Absolute volumetric measures were compared using the unpaired Mann–Whitney test. p values < 0.05 were considered significant. Analyses were conducted in IBM SPSS 24 and graphs were generated in GraphPad Prism 8. Anonymized raw data are available from the corresponding authors upon reasonable request.As most data were not normally distributed and due to small sample sizes, non-parametric tests were used. Correlation analyses were calculated by Spearman’s rho . There was a strong significant correlation between this sum score and the total SPRS score, indicating that a more severe motor phenotype of spastic paraplegia was associated with a higher number of impaired cognitive domains . Likewise, the global cognitive MoCA screening correlated significantly with SPRS score .To examine whether patients at more advanced disease stages (i.e. with more severe motor impairment) exhibit more pronounced neuropsychological deficits, a sum score of tests scoring below a percentile rank of 16 was calculated for each patient. On average, patients were impaired in 9 out of 14 tests , verbal fluency—semantic , selective attention—reaction time , incompatibility—reaction time , and Tower of London . These data imply that a more severe spastic paraplegia was associated with a worse performance in these neuropsychological tasks. The subscore of language domains in the MoCA test  correlated significantly with SPRS score . There was no significant correlation between total SPRS scores and all other cognitive parameters (p > 0.05).To identify those tests that correlated best with motor disease stage, raw scores of individual tests were used. There were strong significant correlations between total SPRS scores and story—immediate recall . Three patients with missing values were excluded, thus the sample size was 13. Five out of 6 patients scoring below the median showed impairment in the tasks digit span forward, digit span backward, and verbal fluency (both phonematic and semantic). These neuropsychological results suggest that verbal fluency and verbal short-term memory are linked to an early disease stage.p = 0.018). The global cognitive performance measured by MoCA however did not change significantly during this time period .To characterize the progressive nature of SPG11, the testing battery was repeated after a mean interval of 24 months . The median SPRS score of this sub-sample was 24 (IQR = 12) at the first examination and increased significantly to 29 (IQR = 17) at re-examination .Concerning the course of neuropsychological impairment, the median sum score of tests scoring below a percentile rank of 16 was not significantly different between both examination time points  and also no correlation of the absolute changes in reaction times and SPRS values , indicating that the increase of reaction times may be independent from the worsened motor phenotype  areas being predominantly affected, involving cortical and subcortical regions of both forebrain hemispheres.r = − 0.742, p = 0.005), caudate  and pallidum  and to the mesencephalon .We next asked whether the degree of atrophy of specific brain regions correlates with motor functions or specific neuropsychological outcomes. As the SPRS score mostly reflects motor function, we first correlated imaging data to total SPRS scores. Interestingly, frontal and parietal grey matter and white matter regions showed no significant correlation to SPRS scores  were impaired, which is in line with Faber et al.  reportinFor morphometric analyses, we applied the “AI-Rad Companion Brain MR for Morphometry Analysis” tool, which determines the outlines of different lobes and the separation between white and grey matter areas using artificial intelligence based algorithms. In contrast to the classical methods of voxel based morphometry (VBM) and region of interest (ROI) based volumetry, this method provides unbiased volumetric measures of different brain areas. Although volumetry analyses may be less powerful to demonstrate grey matter alterations , our datPrevious imaging studies on SPG11 predominantly described involvement of white matter regions and the basal ganglia. Our observation of a rostro-caudal gradient of white matter volume decrease in SPG11 matches findings of previous studies: a frontal predominant decrease of fractional anisotropy was described in a study on 5 SPG11 patients . WidesprThe observed atrophy pattern of the frontal and temporal cortex correlates to the neuropsychological deficits of verbal fluency, verbal memory and executive functions. It is also in line with the frontotemporal cognitive deficit reported in the literature , 30. We Our data add to previous evidence of dual neurodevelopmental and -degenerative disease mechanisms in SPG11 , 15. DevIn SPG11, motor and cognitive functions deteriorate over time , 10, 36.We report a rostro-caudal pattern of cortical and subcortical atrophy in SPG11 accompanied with severe motor impairment and neuropsychological impairment, especially of verbal fluency, verbal memory and executive functions. Our data suggest that reaction times and MRI cortical volumetric measures are important outcome measures in future interventional trials for SPG11. As the presented artificial intelligence based volumetric analysis is feasible across imaging platforms, future functional and structural studies in SPG11 are possible in a multicentric and longitudinal manner in order to validate these findings and to reduce potential bias across academic centers.Additional file 1. Figure S1: Individual progression of both Reaction Time and SPRS in SPG11. Table S1: Demographic characteristics of SPG11 and in-house control imaging cohorts. Table S2: Longitudinal results of cognitive parameters . Table S3: Correlation coefficient (r) matrix of imaging data with neuropsychological and motor measures. Table S4: Overview of previous studies applying neuropsychological testing in SPG11 HSP. Supplemental Methods. Supplemental References.


Here we apply a process-based modeling approach that parameterizes the MIcrobial-MIneral Carbon Stabilization (MIMICS) model with SOC measurements and remotely sensed environmental data from the Reynolds Creek Experimental Watershed in SW Idaho, USA. Calibrating model parameters reduced error between simulated and observed SOC stocks by 25%, relative to the initial parameter estimates and better captured local gradients in climate and productivity. The calibrated parameter ensemble was used to produce spatially continuous, high-resolution (10 m2) estimates of stocks and associated uncertainties of litter, microbial biomass, particulate, and protected SOC pools across the complex landscape. Subsequent projections of SOC response to idealized environmental disturbances illustrate the spatial complexity of potential SOC vulnerabilities across the watershed. Parametric uncertainty generated physicochemically protected soil C stocks that varied by a mean factor of 4.4 × across individual locations in the watershed and a − 14.9 to + 20.4% range in potential SOC stock response to idealized disturbances, illustrating the need for additional measurements of soil carbon fractions and their turnover time to improve confidence in the MIMICS simulations of SOC dynamics.From hillslope to small catchment scales (< 50 km With changes in global climate and land use accelerating at unprecedented rates5, there is an emergent need to project soil organic carbon (SOC) dynamics under changing environmental conditions. A growing body of work focuses on potential global-scale SOC responses to climate change8, but simulating soil carbon dynamics at more localized scales  offers opportunities to parameterize process-based models at resolutions that have a greater relevance to policy and management10. This more local approach also better connects models and field observations, allowing stronger inference of the proximal controls over SOC persistence and turnover that ultimately builds confidence in model projections12. Such advances, however, require quantitative tools that can be used to link observations and models to calibrate model parameters, assess parametric uncertainty, and generate high resolution estimates of soil carbon stocks and potential vulnerabilities.A large portion of the world’s actively cycled carbon (C) is stored in surface soils16. While often accurate for predicting SOC stocks at fine scales17, digital mapping methods generally have limited ability to project future SOC dynamics and responses since the statistical estimation of SOC does not adequately derive how biogeochemical interactions and environmental conditions combine to govern SOC stocks18. In contrast, process-based models have been extensively relied upon for projecting regional to global scale SOC stocks and their vulnerabilities to changing conditions22. Process-based models mathematically approximate the processes through which soil C enters the soil, decomposes, and then is either stored in soil or cycled back to the atmosphere. The use of process-based models for projecting SOC dynamics at high spatial resolution  is predicated by the availability of accurate, high-resolution forcing data for model calibration and the extrapolation of projections across complex landscapes. Rapid advances in monitoring technology, remote sensing, and machine learning present novel opportunities to generate such environmental data23. In addition to these technical challenges, major uncertainties also exist regarding the viability of using model parameters that were calibrated for regional- to global-scale simulations for projections at much smaller, hillslope- to watershed-scales.Current methodologies for estimating soil carbon fall into two main categories, digital soil mapping and process-based modeling. Digital soil mapping uses statistical correlations between climate, vegetation, topography, soil properties, and soil C stocks to generate high resolution estimates of SOC stocks25. Global-scale simulations typically operate at a coarse spatial resolution (e.g. > 50 km2). At these scales, process-based models of SOC have produced accurate estimates of SOC stocks based predominantly on the influences of soil temperature and moisture, net primary productivity and soil texture29. At smaller scales, additional environmental controls are often inferred to have more pronounced effects on SOC stocks across complex landscapes, including controls imposed by detrital input quality30, microbial community composition31 and physical factors such as landscape position16 and erosion33. These additional influences on SOC dynamics often drive local heterogeneity in SOC stocks. Indeed, the importance of including such local biogeochemical controls in process model representations continues to be demonstrated and theorized34, leading many modern process-based models to incorporate components relating to litter chemistry and microbial processing35. These advances in the process-based SOC model constructs are likely to improve the potential for fine scale process model applications.Controls on SOC stocks are currently thought to change with the scale of observation and inference26 with high-resolution input data, soil carbon measurements, and a Monte Carlo approach for parameter optimization and associated uncertainty analysis. We specifically explore the impact of parametric uncertainty on model projections of SOC stocks and their potential sensitivities across a complex landscape with strong climate gradients and diverse soil properties at the Reynolds Creek Experimental Watershed and Critical Zone Observatory (RCEW-CZO)36. This work demonstrates how a variety of measurements collected across environmental gradients at the pedon and watershed scale improves our understanding and prediction of soil carbon storage and processes at management relevant scales. Ultimately, results from this study advance the general ability to model, map and project the sensitivity of SOC stocks at fine spatial scales, thus providing an important pathway for scientists, land managers and policymakers to gain more detailed spatial information about the existing size and future stability of SOC stocks across complex landscapes.Here we develop a method for model parameter optimization and high-resolution mapping of simulated SOC stocks across complex landscapes at spatial scales consistent with field observations and land management practices. This work combines the MIcrobial-MIneral Carbon Stabilization model  resulted in simulated SOC stocks that showed biases relative to field observations across the Reynolds Creek Critical Zone Observatory . Optimizing the model parameters with a Markov Chain Monte Carlo (MCMC) based method revealed that many different parameter combinations were capable of producing similarly accurate estimates of SOC stocks across the Reynolds Creek Critical Zone Observatory  = 1.7–2.0 kgC m-2; Examples of differing MCMC solutions are shown in Fig. -2 between the model estimates and field observations of SOC stocks across the Reynolds Creek Critical Zone Observatory .Initial simulations of the MIMICS model using high-resolution forcing data Fig.  and the Vslope, Vint, Kslope, Kint), relative to the initial model parameterizations used by Wieder et al.26. Similarly, parameters related to physicochemical protection of soil C (fp and D) are lower than the initial model parameterizations, reflecting the longer turnover time of physicochemically protected organic matter (SOMp) with our optimized parameter values. Finally, the parameter ensemble generated a normal distribution for parameter estimates of MGE and log-normal distributions for microbial turnover rates, however the ‘best’ parameter estimates for microbial anabolism were not dramatically different from initial parameter values.The parameter space covered by our ensemble produced near equivalent estimates for total SOC stocks across the RCEW-CZO  were positively correlated  were positively correlated with microbial turnover rates . Further, fp was positively correlated with desorption (D) of SOMp to the available SOM pool (r = 0.58). No correlations (r > 0.4) were observed between the decomposition rate parameters associated with microbial catabolic capacity and those related to microbial anabolism or persistence of soil C in physicochemical protected pools.Our parameter ensemble also revealed strong autocorrelations for particular parameter combinations in MIMICS. For example, the decomposition rate intercept terms in the model −2; Fig. p) simulated by MIMICS varied by a factor of 4.4 ± 2.0 at individual locations  and oligotrophs (MICK) that were simulated across the RCEW-CZO, with a mean MICr:MICK ratio of 0.80 ± 0.48 across individual locations  of soil C stocks across the watershed.A spatially continuous map of estimated SOC stocks from 0 to 30 cm soil depth was generated for the RCEW-CZO by combining results from the parameter ensemble with gridded spatial data  also show greater total SOC uncertainty.Our simulations also generate spatially explicit estimates of individual soil C pools including mean litter C, microbial C and protected SOC stocks and their associated uncertainty from the parameter ensemble Fig. . Litter To project the sensitivity of existing soil C stocks to idealized environmental perturbations, we used the parameter ensemble to estimate soil C stock response to a uniform 10% increase in NPP and a 1 °C increase in mean annual soil temperature Fig. . Gains i2) estimates of soil carbon stocks across the natural land area of the RCEW-CZO  for a 7700 km2 watershed in the Loess Plateau, China. Our ability to project SOC stocks for the RCEW-CZO at a 10 m2 resolution with high accuracy  show high correlation with each other . Thus, we derived these from remote sensing and statistical modeling. Although these methods are still imprecise, they better account for local heterogeneity in biotic and abiotic factors known to influence SOM persistence and mark an important step forward in considering how we can parameterize and validate soil biogeochemical models.52. The MIMICS model has been shown to have a relatively high dependence on plant productivity, as opposed to greater dependence on soil texture found in other, similar process-based models of SOC dynamics7. In practice, our high-resolution mapping application benefited from MIMICS dependence on NPP, since estimates of productivity are readily derived from high resolution multispectral imagery (< 30 m2)54. Methods for estimating vegetative properties from multispectral imagery are broadly effective for most environments57. In contrast, relatively few methods exist for collecting information on soil properties (e.g. clay content) at similar resolutions58. The reduced influence of clay content relative to NPP on SOC dynamics in MIMICS has been proposed as a potential cause for observed discrepancies between SOC stocks simulated by MIMICS relative to field observations and current theory7. However, results from our parameter optimization procedure demonstrate that such discrepancies in MIMICS projections of protected SOC can be rectified through the model parameterization  derived from soil temperature observations, elevation and estimated incoming solar radiation was a significant factor explaining SOC stocks. Seyfried et al.62 and more recently Seyfried et al.63 documented pronounced aspect effects on local soil temperature at the RCEW-CZO that rival larger elevational gradients. For example, differences in soil temperature associated with aspects in small catchments of the RCEW-CZO (< 2 km2) are greater than differences in MAST observed along a 900 m elevation gradient (5.5 versus 4.4 °C), exemplifying the importance of using fine scales to model environmental processes in complex landscapes.Surprisingly, we found that the inclusion of soil moisture in the MIMICS model, a factor with strong influence on SOC stocks at global scalesOur assessment of SOC stocks and sensitivities at high spatial resolution across the RCEW-CZO provides a robust example of how fine scale applications of process-based models of SOC dynamics can provide useful information to support land practice and policy decisions. High spatial resolution estimates of SOC stocks across the entire RCEW-CZO provide insight into landscape variation in SOC stocks that may be expected across the semi-arid environments of the Great Basin, USA Fig. . We see 64. The global potential for rangeland C sequestration has been estimated to range from 0.3 to as much as 1.6 Pg CO2-eq per year65. Moreover, experiments like these can provide estimates of the magnitude of soil C change, and their associated uncertainty, that can be expected over large areas given incremental changes in local conditions . High resolution process-based modeling of soil C dynamics provides a critical tool for evaluating such potential, as well as for identifying, protecting and harnessing sequestration potential in soil across earth’s surface.The idealized scenarios used here illustrate the potential to use a calibrated model like MIMICS to make spatially-explicit projections of SOC responses to environmental change. Rangelands exist over ~ 40% of the Earth’s ice-free land and account for ~ 30% of terrestrial SOC to a depth of 1 m2) projections of 0–30 cm SOC stocks may be generated by combining remotely sensed environmental data and process-based modeling, with the aid of parameter optimization methods and high-performance computing. Leveraging the power of modern parameter optimization techniques and machine learning to improve Earth systems models remains a widespread area of emphasis across disciplines in environmental science67. The Monte Carlo simulation used for parameterizing the MIMICS model provided the cornerstone for this study, allowing us to calibrate model parameters, quantify the impacts of parametric uncertainty, and generate spatially explicit estimates of soil C stocks with high-resolution environmental data. For future studies and applications, we see great promise for similar approaches to assist with model parameterization and spatial extrapolations of SOC projections.In this study, we show that accurate, high resolution  that gives rise to diverse climates, soils and vegetation throughout the watershed63. Vegetative productivity is severely limited by low water availability in lower elevations (< 1400 m) of the watershed, which receive an average of 230–300 mm of precipitation each year. Mean annual temperature across the lower elevations of the watershed is 9.1 °C. In the lowland regions, the vegetation is predominately sagebrush (Artemisia spp.) with a sparse underlying mix of grasses and forbs. At the mid-elevations of the watershed (1400–1700 m), pockets of aspen (Populus tremuloides) and willow  begin to emerge in riparian areas, as well as on some of the northern facing slopes, where winter snowpacks supply sufficient water. At higher elevations, Juniper (Juniperus spp.) and Douglas fir (Pseudotsuga menziesii) stands are common, though not pervasive, supported by far greater annual precipitation and lower mean annual air temperatures, averaging 350–994 mm per year and 5.4–8.5 °C. Soils across the RCEW-CZO watershed are mixed and predominantly originate from either felsic material  or mafic basaltic flows73. Soils are generally deep across the RCEW-CZO (> 1 m not including colluvium)74. Soil development is typically greatest at higher elevation in convergent topography. For the top 30 cm of soil, the mafic derived soils are generally loams , while the felsic are generally sandy loams . Across the RCEW-CZO, approximately 85% of the soils are classified as Mollisols, with another 13% classified as Aridisols76.The Reynolds Creek Experimental Watershed and Critical Zone Observatory (RCEW-CZO) in southwestern Idaho, USA covers 239 km78. For model calibration we used previously collected data pertaining to total soil C stocks from 0–30 cm at 89 locations across the RCEW-CZO. The median 0–30 cm soil C stock was used where soil data overlapped within a 10 m2 area.Soil C stocks were previously determined at unique locations across the RCEW between 2014–201679. Here we use a version of the model that calculates equilibrium SOC stocks based on the mean annual values for net primary productivity, soil temperature, litter lignin:N ratio and the soil clay content. Soil moisture may also be used as a control on soil C dynamics in the MIMICS model22. However, model testing using soil moisture inputs derived from aerial imagery and a snow model did not improve model accuracy of SOC stocks estimates at RCEW, and thus soil moisture was not included as a model input. We assumed all field observations of SOC included in the study are at steady state and ignored the interannual and seasonal dynamics of climate and vegetation. In kind, all model simulations were carried out to steady state. These assumptions present uncertainty and limitations regarding the insights and projections from the model, but it remains common practice to allow for best use of available data79.The MIcrobial-Mineral Carbon Stabilization (MIMICS) model has been widely used for estimation of soil C across diverse ecosystems and has been found to perform well across ecosystem gradients and at global scales26. These parameters control the simulated turnover and persistence of litter, microbial biomass, and soil C pools , microbial anabolism , and physicochemical protection of SOC  simulation involving 500,000 MIMICS model runs with unique, random parameter combinations. To improve performance of the MC simulation, we proceeded to tune the minimum and maximum limits on each of the uniform parameter proposal distributions , the relative abundance of microbial r- vs. K-strategist (0.1–10), litter C stocks , and the turnover time of C in the protected SOM pool (50–400 years). For brevity, we refer to these accurate (RMSE < 2.0) and plausible parameterizations throughout the manuscript as the parameter ensemble (n = 30). The lowest RMSE observed over all combinations was 1.72 kgC m-2.Initially, we used a Hamiltonian Markov Chain Monte Carlo (MCMC) algorithm79 and shuffled complex evolution (SCE) algorithms46. We chose to employ a MC simulation to optimize the model parameters due to the simplicity of the algorithm relative the MCMC and SCE methods. The MC simulation requires no statistical assumptions and has no inherent potential to bias the optimized parameters chosen. The MC optimization approach is computationally inefficient compared with MCMC and SCE, which required us to use high performance computing clusters to complete the necessary number of model simulations . We performed a threefold cross-validation to ensure the MC optimization method results were not biased by the nature of the RCEW forcing data or the number of simulations performed. Each cross-validation of the MC optimization procedure used a unique sub-sample (n = 60) of the field observations of SOC stocks to derive the optimized parameter distributions. Cross-validation sub-samples were stratified by co-located estimates of GEP to ensure consistency in the scope of the model projections. The resulting optimized parameter distributions from cross-validation simulations showed no concerning disparities between the subsample distributions and the distributions derived from the full dataset  and USDA Web of Soil Survey76 respectively. Clay content was procured from the Web Soil Survey as an average value for the soil layer from 0–30 cm. The underlying source of the WSS clay data is the SSURGO database81. To create a spatial map of lignin:N content of the dominant vegetative species across the RCEW, the vegetation map provided in Seyfried et al.82 was paired with corresponding litter lignin:N values reported in the literature. From the digital elevation map, incoming solar radiation was calculated using the ESRI Solar Radiation Toolset, which calculates net incoming solar radiation by simulating the solar path over the digital elevation map surface while accounting for atmospheric effects, site latitude and elevation, slope, aspect, daily shifts of the sun angle, and effects of shadows cast by surrounding topography83.Model estimation of soil C stocks across the entirety of the RCEW-CZO required continuous spatial data layers for elevation and incoming solar radiation, soil temperature, net primary productivity, soil clay content and the lignin:N ratio for the dominant vegetative source of surface litter. Digital elevation and soil clay content layers were used as provided online courtesy of the U.S. Geological Survey ; solarIN represents the incoming solar radiation at the estimate location (W m-2) and tMOD is employed as a scaling factor . All coefficients, and the tMOD scaling factor, were determined using a linear optimization algorithm set to minimize error between observed and estimated MAST values for a series of locations across the RCEW-CZO . The spatially dispersed and depth stratified MAST observation data showed no significant differences in MAST across soil depth increments of 5, 10, 20 and 30 cm. Based on this evidence, all MAST data available for soil depths < 30 cm were used to calibrate the MAST estimation equation. Correlation between observed and predicted MAST values equaled R2 = 0.73  was created based on the relationship found between soil temperature, elevation and incoming solar radiation. Across the full extent of the elevation gradient of Reynolds Creek, and for soils with similar aspects, there is a strong relationship between soil temperature and elevation. However, soil temperatures differ substantially for soils with opposing north and south facing aspects, as shown by Godsey et al..73 Fig. . The res60, Landsat 5 imagery courtesy of the U.S. Geological Survey was collected across 10 years, from 2002 to 2011. From this data, specific images were chosen from each year to determine the maximum modified soil-adjusted vegetation index (MSAVI2) values at a 30 m2 resolution across the RCEW-CZO. The MSAVI2 index has been demonstrated as a viable method for identifying differences in vegetative productivity87. MSAVI2 was calculated using the following equation:Hyperspectral satellite-based imagery was used to generate a continuous spatial map of gross ecosystem productivity (GEP) across the RCEW-CZO. Following work previously completed by Fellows et al. in Fig. . To run 2 resolution using nearest neighbor resampling method, and all layers were re-projected to a common projection and coordinate reference system (World Geodetic System (WGS) 1984, Universal Transverse Mercator (UTM) Zone 11). For model calibration and validation datasets, pixel values from the spatial data layers for GEP, MAST, clay content and litter lignin:N content were extracted at the coordinates of sampling points.As needed, all spatial data layers were resampled into raster format with 10 mWe performed MIMICS simulations for each grid cell in the RCEW-CZO spatial data (2.4 million grid cells). Each grid cell simulation involved producing estimates from each member of the parameter ensemble (n = 30). The average estimate at each grid cell was then used to construct the continuous maps Figs. , 6, 7. T88. Root mean square error and correlation between model estimates and field data were specifically performed using the Metrics (https://github.com/mfrasco/Metrics) and base R packages respectively88. The MIMICS model and the developed Markov Chain Monte Carlo and Monte Carlo simulation algorithms were composed in R programming scripts and are available in open-access repositories on GitHub . The MIMICS_HiRes repository provides all modeling and analysis code associated with this study, as well as useful startup information and simplified examples to assist future use. Scripts in the repository were assembled specifically for use with HPC to reduce the time required for model parameterization and simulations.All modeling, analysis and spatial mapping for this study were performed in RSupplementary Information.


Therefore, ENP-based AOF can be an important component of the mitigation strategy to limit global warming.Artificial ocean fertilization (AOF) aims to safely stimulate phytoplankton growth in the ocean and enhance carbon sequestration. AOF carbon sequestration efficiency appears lower than natural ocean fertilization processes due mainly to the low bioavailability of added nutrients, along with low export rates of AOF-produced biomass to the deep ocean. Here we explore the potential application of engineered nanoparticles (ENPs) to overcome these issues. Data from 123 studies show that some ENPs may enhance phytoplankton growth at concentrations below those likely to be toxic in marine ecosystems. ENPs may also increase bloom lifetime, boost phytoplankton aggregation and carbon export, and address secondary limiting factors in AOF. Life-cycle assessment and cost analyses suggest that net CO This Analysis considers the potential benefits and challenges of applying engineered nanoparticles for artificial-ocean-fertilization-driven carbon sequestration One CDR approach, artificial ocean fertilization (AOF), involves the intentional addition of a limiting nutrient  to stimulate phytoplankton growth and CO2 uptake in the oceans  from the atmosphere, in addition to sharp COans Fig. 2–9. A fr2+ into the ocean over 25–300 km2 have been conducted, in addition to several field-scale studies monitoring natural occurrences of ocean fertilization by iron10. These studies have shown that although considerable phytoplankton blooms can be stimulated in most artificial additions, their efficiencies in CO2 drawdown are much less than those observed for naturally occurring fertilization, for example, via terrestrial dust deposition through the atmosphere6. Slow export rates of biomass, especially if not enhanced compared to remineralization rates (the rates at which organic carbon is converted into CO2 by bacteria), undo the potential benefits of AOF. Remineralization can also lead to oxygen depletion in subsurface waters and the production of methane and nitrous oxide with higher global warming potential than CO2 (ref. 11). Nevertheless, with increasingly wide public concerns about the impacts of climate change and the need for negative emission technologies to meet the targets of the 2015 Paris Agreement, recently there has been a renewed interest in AOF5.To date, 13 field-scale experiments employing artificial addition of Fe15. The important role of naturally occurring nanoparticles is already established in natural ocean fertilization19. For instance, natural iron (oxyhydr)oxide nanoparticles have been proposed as a source of bioavailable iron in fluxes from glaciers15, continental sediments13, volcanic ashes12 and hydrothermal vent emissions19. Natural nanoparticles may also supply other nutrients such as phosphorus16. These findings suggest that the use of engineered nanoparticles (ENPs) in AOF may lead to desirable efficiencies because of their nanocharacteristics, which resemble those of their natural counterparts. In addition, it is possible to design ENPs to mitigate the drawbacks of the current AOF approach, thereby maximizing their efficiency. If considerable CO2 drawdown is achieved by using ENPs, this may allow applications of the approach as a CDR technology at smaller scales or specific locations, and thus allay some of the concerns regarding risks of geoengineering the entire marine ecosystem and downstream ‘nutrient stealing’.Due to their relatively high number density, and very high specific surface area and diffusivity, nanoparticles (1–100 nm) are probably bioavailable to phytoplankton in the ocean22 and the application of various ENPs for enhancing sustainable agriculture23 and aquaculture25. In soil/groundwater remediation, despite initial challenges regarding ENP delivery to contaminant source zones and concerns about ENP risks to drinking water resources, the technology has already been commercialized widely around the world20.ENP applications in environmental systems at relatively large scales have been widely studied in the past two decades, such as nanoscale zero-valent iron (NZVI) slurries used for soil/groundwater remediationHere we summarize possible opportunities for ENPs to address the drawbacks and risks of AOF. We further estimate the environmental impacts and economic costs of ENP use for AOF by conducting life-cycle assessment oxides and eventually micrometre-sized particles or aggregates that probably sank out of the photic zone, reducing iron bioavailability  increased by 35–756% compared with controls34. In particular, the role of NZVI in enhancing marine microalgae growth was comparable to or higher than commonly used EDTA-Fe at equimolar concentrations of 11.7 µM (ref. 31). This was attributed to similar or higher bioavailability of ENPs, which can bind to extracellular polymeric substances (EPSs) around the algae cell, leading to ENP uptake via endocytosis31. Importantly, algal growth rates were similar for a wide range of NZVI concentrations (1.17–117 µM)31, revealing another advantage of using ENPs: algal growth is less dependent on ENP concentration in contrast to the substantial impact of the concentration when soluble iron is used36.Numerous studies have demonstrated the stimulating effects of ENPs on algal growth Fig. . Notable31. Our estimate of the potential ENP concentration range for AOF  and surfaces (here phytoplankton EPSs) which control the species bioavailability, not the concentrations39. Such depletion of secondary limiting nutrients may be addressed effectively within the context of nanotechnology by using a mixture of ENPs or a versatile core–shell nanocomposite as illustrated in Fig. 40. Phytoplankton nutrient co-limitation by trace metal micronutrients such as zinc, cobalt, manganese and aluminium has been reported42, while stimulations of algal growth in the presence of ZnO and Al2O3 ENPs have also been observed, suggesting that ENPs are promising in addressing co-limitation by other nutrients  (oxyhydr)oxides which are naturally formed in conventional AOF. Moreover, light insufficiency hinders photoreduction of colloidal Fe(III) to soluble Fe(II) and thus decreases iron bioavailability for phytoplankton uptake29. This may, in turn, accelerate iron removal in particulate form from the water column. On the other hand, potentially well-dispersed ENPs are generally directly bioavailable to phytoplankton without needing further dissolution under light limiting conditions33.Light limitation has been proposed as an important factor in reducing the efficacy of AOF45. Phytoplankton sinking rates can be enhanced when materials with a higher density are adsorbed onto cells or incorporated into aggregates to induce a ballast effect. Silica ENPs (at a concentration of 75 mg l−1) were found effective in facilitating the sedimentation and removal of cyanobacteria from the water column46. Uptake of silica increased cyanobacteria density and sinking velocity through the formation of cyanobacteria–SiO2 complexes. Even a 1% increase in the density of phytoplankton can double the sinking speed45. This means the mass ratio of added SiO2 ENPs to phytoplankton biomass generated during the bloom could be just 1:100 to double the biomass export rate via the ballast effect.Application of ENPs to AOF may also enhance the export efficiency of accumulated phytoplankton biomass. Individual phytoplankton cells, especially non-diatom species and live cells, can have a density lower than that of seawater, reducing their sinking potential47. The use of SiO2 and CeO2 ENPs has also been proposed to facilitate oil-spill removal by promoting the production of EPSs by marine phytoplankton, leading to enhanced aggregation and sinking47. Accordingly, SiO2 ENPs might not only address co-limitation by multiple nutrients but also may facilitate the export of the phytoplankton as a ballast agent and phytoplankton EPS promoter. More efficient export of ballasted organic matter may reduce subsurface deoxygenation and methane and nitrous oxide production caused by water column remineralization.Another important factor affecting phytoplankton export is their aggregation tendency. Marine phytoplankton EPSs are critical for the formation of marine snow, which is deemed key to phytoplankton export to the deep sea49. There are opportunities for ENPs to protect a developing bloom by repelling grazers and competitors, and this may be controlled by tuning the doses of various ENPs considered for AOF. Mesocosm experiments have shown that ENPs such as CuNPs and AuNPs, when added in combination with nutrients (nitrogen and phosphorus), can enhance the bloom continuation by more than 50 days50, due to the adverse effect of ENPs on planktonic species competitors. Further, aluminium oxide ENPs skewed the selective feeding pattern of Daphnia towards algal feed that was not exposed to ENPs51.Crustacean grazers can shorten phytoplankton bloom lifetime and reduce AOF efficiency2 yr−1 at 25 °C) is generally one to two orders of magnitude less than that of soluble Fe2+ (222.8 cm2 yr−1 at 25 °C) under quiescent conditions52.Multilayer core–shell ENPs can be designed with a sequence of layers according to the sequence of the micronutrient limitations and subsequent grazing pressure during different stages of phytoplankton bloom development and sinking Fig. . This pr53, ENPs’ abilities to enhance phytoplankton growth and promote EPS production (which enhances cell aggregation and biomass export) are all advantageous for carbon dioxide removal. Further research is also required to understand these concomitant factors under natural conditions and in the presence of ENPs.Although factors such as biomass sinking, phytoplankton bloom longevity and aggregate size might be countervailing and are currently the subject of ongoing research2, ZnO, CeO2, Al2O3 and TiO2 nanoparticles, that may be relevant for future AOF applications , their polymer coatings and AOF operation . We considered a range of ENP synthesis methods including chemical, mechanical and green (plant extract-based54) approaches  emissions per kg ENPs for all processes involved are 38.3 kg on average (ranging from 11.2 to 156 kg) which is 13 times (or 4–53 times) larger than those of iron sulfate conventionally used in AOF . On average, these lead to 4–10 times larger CO2 emissions compared with conventionally used iron sulfate. Henceforth, we discuss these ENP types only, as these may also be the main ENPs for AOF.We conducted an LCA and an LCC analysis for several relevant ENPs , although 50–67% of this may be compensated by CO2 release from excess microbial respiration, decreasing the estimate to 40–400 kg of CO2 (refs. 57). The lower limit of this range is still ~3-fold higher than the amount of CO2e emissions per kg NZVI, SiO2 and Al2O3 ENPs synthesized using green methods (12.9 kg), and still higher when synthesized with the ball-milling method (30.0 kg). Based on this efficiency range, our analyses show that AOF with iron sulfate costs ~US$0.030–0.300 per kg CO2 removed, which is within previously reported ranges of US$0.008–0.450 (refs. 58) and leads to 2.97 kg CO2e emissions per kg iron used as determined here. Although the use of ENPs  may increase costs by ~2–5 times  compared with iron sulfate, we have shown here that ENPs may enhance AOF efficiencies much above those of iron sulfate2. Further, our LCC analysis also considers labour, capital and environmental costs in addition to monitoring costs which were rarely included previously58. Our approach is relatively conservative because the labour, capital and environmental costs are mostly embedded in the prices of materials and processes considered in life-cycle inventories while we have also specified these costs separately  causes the largest environmental impacts among different ENP types/synthesis methods. Although green methods in all impact categories, except urban land occupation, are the most environmentally friendly synthesis methods, in a few categories  they can also cause large impacts (Supplementary Table 2e emissions) among different categories, except for green synthesis of NZVI and AOF processes (ship only) which show the highest environmental impact in the category of terrestrial acidification  production, the promoting effect of several ENPs on algae growth Fig.  suggests61. Typically this is achieved using polymers that are inexpensive, efficient, biocompatible, and can be adsorbed onto ENP surfaces via simple physisorption processes and/or using metals that are also aimed to improve ENP performance in a core–shell structure52. Polymers generally provide steric repulsion at the surface of ENPs while a metallic shell may reduce the Hamaker constant or enhance electrostatic repulsion to overcome forces such as van der Waals and magnetic attractions52. Ligands with low molecular weight , which are well solvated in a concentrated brine, have shown a considerable steric stabilization effect in seawater conditions for 10 nm SiO2 ENPs61. Some polymers , which do not bind with divalent cations, provide a steric stabilization effect for iron oxide ENPs against bridging flocculation62. Such polymers might also reduce the heteroaggregation of ENPs with ubiquitous colloids in seawater, although further research is needed to investigate these effects. Heteroaggregation between ENPs and phytoplankton cells due to polymer bridging63 could be one possible pathway for ENP removal from the ocean surface. However, continuous excretion of soluble EPS fraction from phytoplankton64, especially due to the presence of ENPs47, might cause detachment of ENPs from the cell wall  are higher by 4–10 and 2–5 times, respectively, than using dissolved iron, these results are based on the worst-case scenario where the use of ENPs leads to similar CO2 removal as achieved by the conventional approach. The potential for ENPs to address the aforementioned challenges with conventional AOF will likely improve the efficiency of AOF and may alleviate the concerns about its implementation. In conclusion, ENP-based AOF may be a remarkable carbon dioxide removal approach to fight climate change.Overall, ENPs, due to their great number density, specific surface area, diffusion, bioavailability and potential for designing their effective targetability and other functionalities, may provide multiple benefits for application in AOF. These benefits address the challenges of using conventional approaches for AOF (use of dissolved nutrients), which include enhancing bioavailability, nutrient co-delivery, photosynthetic efficiency, export efficiency and phytoplankton bloom longevity. Although our analyses show that COAny methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41565-022-01226-w.Supplementary InformationSupplementary sections 1–5, Figs. 1–3 and Tables 1–6.


Inclusion of these modelled water table depth and soil moisture contents significantly improves the accuracy of a neural network model in predicting active fires at two-weekly time scale. This constitutes an important step towards devising an operational fire early warning system for Indonesia.Soil moisture deficits and water table dynamics are major biophysical controls on peat and non-peat fires in Indonesia. Development of modern fire forecasting models in Indonesia is hampered by the lack of scalable hydrologic datasets or scalable hydrology models that can inform the fire forecasting models on soil hydrologic behaviour. Existing fire forecasting models in Indonesia use weather data-derived fire probability indices, which often do not adequately proxy the sub-surface hydrologic dynamics. Here we demonstrate that soil moisture and water table dynamics can be simulated successfully across tropical peatlands and non-peatland areas by using a process-based eco-hydrology model ( Indonesia has the largest coverage of tropical peatlands 2 of which 10–15% are affected by fires each year4. Such fires can emit substantial amounts of greenhouse gases during drought years 5, cause significant increases in morbidities and mortalities through smoke haze pollution8 and degrade biodiversity10. These episodic events also have profound negative impacts on the economy11 and ecosystem services10 of the affected areas.Tropical peatland and forest fires in Indonesia are strongly associated with episodic drought events14. Prolonged droughts in Indonesia are caused by El-Niño and/or positive Indian Ocean Dipole (IOD) events which result in cooler-than-normal sea surface temperatures, and thus reduced precipitation16. Exacerbated by extensive peat drainage for agriculture and plantation establishment, these droughts can trigger severe peatland fires by lowering the WTD and hence desiccating surface and sub-surface peats18. Loss of fire-resistant natural forest canopy cover through large-scale deforestation can also alter the micro-meteorology and hydrology of tropical peatlands, further aggravating the fire situation19. The severe drought episodes of 2015 and 2019 across Indonesia, for example, caused by strong El-Niño events and positive IOD, led to a major and damaging increase in peatland fires, highlighting an urgent need to develop operational systems to forecast potentially severe fire events to mitigate the impacts of fire and haze21.Water table depth (WTD) and peat moisture contents are predominant biophysical controls over the occurrence of peat and forest fires in tropical peatlands24, there has been little progress to date in developing an operational fire early warning system for Indonesian peatlands that takes advantage of recent advances in soil hydrology modelling27. Although seasonal variations in near-surface peat moisture contents, and rates of groundwater recharge have been shown to be correlated with fire occurrence patterns in tropical peatlands29, the explicit inclusion of hydrologic dynamics into peat fire forecasting models remains constrained by several factors. These factors include: a scarcity of available (i) measurements of seasonal variations in WTD and peat moisture contents, (ii) measurements of peat soil physical and hydrological properties such as bulk density, organic carbon, moisture retention, and hydraulic conductivity, and (iii) process-based peat hydrology models that can predict hydrologic dynamics across temporal and spatial scales28.Despite the recognized importance of peatland hydrology in determining fire activity in Indonesiaecosys27 viz. soil moisture and WTD  for the tropical peatlands of Riau province  Fig.  as a funecosys model simulated WTD drawdown and desiccation of near-surface (0–0.05 m depth) peat and mineral soil at a 0.05° resolution across Riau during both the short and the long dry seasons in 2015  and lateral water exchange   and are not known to receive significant moisture through lateral recharge36. Furthermore, during the dry season in 2015, the upland non-peatland areas also had drier soils that meant no potential lateral run-off or discharge from those areas  and ANN (WTHR + HYDROL), to examine whether and how hydrologic dynamics would improve the predictive capability of fire forecasting over tropical peatlands  modelsR2) (R2 = ~ 0.8 for ANN (WTHR + HYDROL) vs. R2 = ~ 0.4 for ANN (WTHR)) and lower mean absolute errors (MAE) (MAE = ~ 40 two-weeks−1 for ANN (WTHR + HYDROL) vs. MAE = ~ 100 two-weeks−1 for ANN (WTHR)) for ANN (WTHR + HYDROL) predicted vs. validation fire counts  model was off by at least two weeks in predicting MODIS active fire count in most years as indicated by smaller R2 for ANN (WTHR) vs. predicted time-differenced  fire counts  model was significantly more skillful than ANN (WTHR) in predicting those two extreme episodic fire events  performed consistently better than ANN (WTHR) in predicting MODIS active fire counts over a range of weather and fire years from 2008 to 2015, as indicated by higher coefficient of determination  model missed most of the peatland fire hotspots during 2013 and 2014 and rang false alarms during 2015  the dearth of soil, weather and land use data for initializing and testing the model, and (ii) the simplified selection of user defined lateral model boundary conditions to optimize computational requirements , which introduced potential uncertainties into the ecosys simulations, we demonstrate here that outputs from a process-based soil hydrology model significantly increases the skill of fire forecasts in tropical ecosystems. The methodology presented here can easily be scaled up to provide the framework for skillful early fire warning system for the whole of Indonesia and other tropical countries exhibiting a complex land-cover mix of peatlands, non-peatlands, degraded and pristine forests. For instance, ecosys can be driven by seasonal forecasts such as those available through the Copernicus Data Server (CDS)  to forecast seasonal variations in hydrology, which would then feed into the fire forecasting model. Beyond skillful fire prediction, an ecosys-based fire early warning system could also be used to assess the impact of different management interventions to mitigate against potentially severe droughts in future such as reforesting deforested peatlands and rewetting drained peat swamps.Our study demonstrates that using publicly available data, a scalable and satisfactorily validated process-based soil hydrology model such as ecosys that has previously been validated across tropical27, temperate38 and boreal41 peatlands   throughout each model run reflecting phenology, plant physiology, land use and land cover types and common agricultural and forest management practices across Riau  models, with and without including simulated WTD and soil moisture as predictors, to predict MODIS active fire count from 2008 to 2015 for each modelled grid-cell across Riau at a two-week time step. The ANN (WTHR) model used weather variables only, e.g., average air temperature, radiation, wind speed and relative humidity, and total precipitation over each of the two-week time steps, as features or predictor variables  and ANN (WTHR + HYDROL) models were represented by 6 and 8 nodes respectively46. The two hidden layers in both the models had 32 and 16 neurons respectively. The input layer and the two hidden layers of each ANN model had an activation function ‘relu’37. The loss function for each ANN model was mean squared error, which was optimised by using an optimizer ‘ADAM’46 with a learning rate of 0.001. Each of the two parallel ANN models was trained and validated by using leave-one-year-out strategy, where MODIS fire count data for one year was held out as validation dataset in each iteration. Early stopping strategy was used to avoid overfitting and to increase the generalization power of the model46.The two ANN models were trained for each grid-cell using leave-one-year-out strategy. Each of the fully connected ANN regression models comprised of four layers—an input and an output layers and two hidden layers in betweenR2) and mean absolute error (MAE). For this purpose, predicted and MODIS active fire counts at each two-week time step were pooled separately for all peatland grids and all non-peatland grids for each validation year between 2008 and 2018. The ANN (WTHR) and ANN (WTHR + HYDROL) models were then compared against each other using the R2 and MAE statistics generated while regressing accumulated modelled active fire counts vs. MODIS active fire count for each test year, separately for both peatland and non-peatland grids. To assess their temporal accuracies, the two ANN models were assessed by comparing predicted versus MODIS time-differenced  fire counts.Comparative model performance was evaluated by comparing out-of-sample coefficient of determination (Supplementary Information.


High-temperature, high-pressure conditions in the front shock wave caused intense ablation of the asteroid material and formation of huge amount of meteoritic dust dispersed in the atmosphere during the flyby. Massive snowfalls just few days before and after the event conserved precipitated meteoritic dust in thin layer of snow which was collected and studied later. The most intriguing and challenging material discovered in the dust was closed-shell 10–70 µm exotic polygonal graphitic carbon microcrystals of undisclosed nature. Using optical and electron microscopy and Raman spectroscopy the atomic structure of closed-shell microcrystals was thoroughly studied and their graphitic nature was revealed. It was found that some of the particles formed by multilayer graphitic polygonal shells have extensive hollows inside. Comparative microscopic and spectroscopic analysis of meteorite exotic carbon microcrystals with different graphite species, carbon onions, and diamond revealed two distinctively different closed-shell carbon particles. The first type of the particles can be attributed to carbon onions with characteristic graphite nanocrystalline basic structural units (BSU) of 49 nm lateral size and less and, probably, BSU heteroatomic termination necklace with oxygen content comparable to 1.1% and more. It was shown that the second type of unique graphitic carbon particles of a convex shape and perfect hexagonal symmetry with lateral dimensions of 14 µm correspond to multiply twinned closed-shell graphite microcrystals with polyhexacyclooctadecane (–C Chelyabinsk (Russia) superbolide  was a unThe Chelyabinsk superbolid was a historical event due to different reasons. Every year several thousand tons of space debris fall to Earth in the form of micrometeorites. Normally, they are fully or partially remelted, evaporated and condensed back in the atmosphere to form dust particles with diameters less than 50 μm ,3,4. DesAlmost all 12,000 t of parent Chelyabinsk asteroid material—ordinary chondrite of the LL5 petrological class . The wea2 on chondrite particles [Among meteorite materials collected at the end of winter–beginning of spring 2013 from snowy Southern Urals steppes, the most intriguing and challenging were exotic carbon microcrystals of undisclosed nature formed by reduction and precipitation of elementary carbon from atmospheric COarticles . It was Based on microscopic, structural and spectroscopic data as well as theoretical quantum-chemical and molecular-dynamics simulations multiply twinned closed-shell graphite microcrystals formed during structural decomposition of Goldberg-type nano- and microdiamonds ,20 were sp2 closed-shell graphitic particles with distinctive hollows inside. It was shown that the multilayered graphitic walls of some microcrystals are formed by fused triangular fragments which form pentagonal and hexagonal facets of finite dimensions. For the first time original extended collection of Raman spectra of Chelyabinsk meteorite carbon particles are presented in the study. Based on comparative analysis of the images and Raman spectra, two very distinctive types of multiply twinned ECMs were identified. Comparative analysis of Raman spectra of the first type of ECM with different graphite species, diamond, and carbon onions attributed ECM to carbon onions with characteristic graphite nanocrystalline basic structural units (BSU) of 49 nm lateral size and less and, probably, BSU heteroatomic termination necklace with oxygen content comparable to 1.1% or more. A comparative analysis of microscopic images and Raman spectra of unique graphitic carbon particle of perfect hexagonal symmetry with lateral dimensions up to 14 µm directly indicates an existence of previously hypothesized perfect multiply twinned closed-shell graphite microcrystals with polyhexacyclooctadecane (–C18–)n core [The main goal of this study is a detailed microscopic and Raman spectroscopy study of unique exotic multifaceted carbon microcrystals of complex nature discovered in Chelyabinsk meteoritic dust precipitated from the atmosphere and conserved in deep Ural snow back in February of 2013. For the first time, key details of sample preparation of meteoritic dust which contains ECMs are presented in the study. Using optical and electron microscopy and Raman spectroscopy it was discovered that exotic multifaceted microcrystals are decane –C–n core [The collection of meteorite dust turned out to be possible due to favorable meteorological conditions, namely extensive anticyclone with cloudy weather without any fallout within a few days after the meteorite flyby. As a result, the snow hardened, and an icy crust formed on its surface. In total, 53 samples were collected in trajectory region. The snow was melted under in a warm room, and the resulting water was filtered through a funnel with filter paper placed in it and meteorite dust samples were collected  is a powerful tool to study structural properties of different carbon allotropes. One can easily found detailed reviews devoted to RS of different carbon species  is divi−1 can be distinguished in the spectra of all ECMs. The peak at 2750 cm−1 is the second order D peak and is called the 2D mode  and is presented even in high-quality graphite samples (see below). The red shoulder of 2D peak maybe attributed to some structural defects as well. The second peak (1582 cm−1) is called G mode (Graphitization) and shows the degree of graphitization. The presence of 3D peak at a frequency of ~1330 cm−1 in all but one (Sample 0) RS ECMs can be caused by multiple structural defects, facets, etc. and it will be discussed in details below. It is necessary to note that it is very different from Raman intensity caused by sp3 hybridized carbon atoms. 2G and D + D’ peaks at 2950 and 2950 cm−1 are attributed to high-quality graphite as well could be determined by anharmonicity caused by electric factor [The Raman spectra of Chelyabinsk meteorite ECMs are presented in c factor .−1) of graphite-related materials  somehow excludes a possibility of a significant contribution of sp3 coordinated atoms in formation of Raman spectra in one-phonon region of any sp2 species. Following [D mode  and micronanocrystalline (µncr) graphites  are presas found , that thDiamond Raman spectrum c is presD and D’ energy regions which directly indicates small effective dimensions of structural fragments, sp2 surface defect states which could be caused by carbon pentagon and heptagon rings and, probably a presence of visible amount of terminated oxygen groups.The one-phonon Raman spectrum of giant fullerenes and carbon onions  (AdaptedG peak (1582 cm−1) without any traces of either 3D peak, or blue shoulder of G peak n core [Let’s analyze one-phonon spectra of Chelyabinsk superbolid ECMs presented in f G peak  which coraphites a–d. It d defects . Raman sdecane –C–n core [–)n core ,20.D region (1365 cm−1) and a visible blue shoulder of G peak (1582–1590 cm−1). These spectral features reveal multiple types of lattice defects (see above) and correspond to the lateral BSU size aL smaller 49 nm coupled with oxygen neckless concentration greater 1.1% and a presence of surface sp2 defects. Taking into account their closed-shell nature the spectra mostly resemble Raman spectrum of giant fullerenes and carbon onions presented at the bottom of RS spectra in one-phonon region of Samples 5, 7, 8 of Chelyabinsk ECMs  demonstr−1) of sp2 carbon materials [sp2 π-electron system contributes to such pronounced anharmonicity with the shape [sp2 carbon materials in two-phonon spectral region demonstrate diffuse shapes with very wide peaks which intensity and width is mostly determined by the lateral dimensions of the fragments and structural irregularities.The two-phonon Raman spectra  which perfectly correspond to the 2440, 2726 and 3250 cm−1 peaks of mono-graphite [18–)n core [The ECM Raman spectra in two-phonon spectral region of Chelyabinsk superbolid n core ,20.−1) with relatively small structural domains at 2440, 2726 and 2950 cm−1. It is necessary to note that the intensity ratio of G/2D peaks for Samples 5, 7, 8 is visibly smaller than the ratio for Sample 0 n core which served as formation embryo during the high-temperature, high-pressure synthesis on chondrite particles from atmospheric CO2. Following structural model presented earlier [Concerted comparative analysis of microscopic images and Raman spectra of Chelyabinsk meteorite exotic carbon microcrystals and different crystals , perfect


The adaptation of influenza seed viruses in egg culture can result in a variable antigenic vaccine match each season. The cell-based quadrivalent inactivated influenza vaccine (IIV4c) contains viruses grown in mammalian cell lines rather than eggs. IIV4c is not subject to egg-adaptive changes and therefore may offer improved protection relative to egg-based vaccines, depending on the degree of match with circulating influenza viruses. We summarize the relative vaccine effectiveness (rVE) of IIV4c versus egg-based quadrivalent influenza vaccines (IIV4e) to prevent influenza-related medical encounters (IRMEs) from three retrospective observational cohort studies conducted during the 2017–2018, 2018–2019, and 2019–2020 US influenza seasons using the same underlying electronic medical record dataset for all three seasons—with the addition of linked medical claims for the latter two seasons. We identified IRMEs using diagnostic codes specific to influenza disease (ICD J09*-J11*) from the records of over 10 million people. We estimated rVE using propensity score methods adjusting for age, sex, race, ethnicity, geographic location, week of vaccination, and health status. Subgroup analyses included specific age groups. IIV4c consistently had higher relative effectiveness than IIV4e across all seasons assessed, which were characterized by different dominant circulating strains and variable antigenic drift or egg adaptation. Traditionally, influenza vaccines are manufactured in viral culture, a process in which an influenza vaccine seed virus must first bind to a cellular receptor to infect a cell. The receptors on the surface of avian cells differ from those on mammalian cells. For a human influenza virus to propagate efficiently in avian cells, it sometimes must adapt to bind the avian receptor; this process is known as egg adaptation. Importantly, adaption occurs in the region of the influenza virus that is dominant antigenically. Thus, as influenza viruses adapt to grow in eggs, antigenic differences from circulating viruses may arise. These differences may cause egg-based vaccines to be potentially less effective at preventing influenza virus infection than vaccines manufactured in other ways, including those with vaccine viruses grown in mammalian cell cultures or using recombinant technologies ,2. AlthoThe cell-based quadrivalent inactivated influenza vaccine  contains viruses grown in a mammalian cell line. The cell-culture technology process isolates and grows viruses in Madin–Darby canine kidney (MDCK) cells rather than fertilized hen’s (or chicken) eggs, which are traditionally used for influenza vaccines. The production of cell-based influenza vaccines using cell-cultured candidate vaccine viruses (CVVs) does not appear to select for antigenic changes as does egg-propagation, and therefore permits the growth of viruses that are more similar to the expected circulating strain ,5,6. As Influenza vaccines are the best countermeasure against influenza virus infection. However, the effectiveness of current vaccines varies. Vaccine effectiveness is affected by many variables, including health status, age, prior influenza exposures of persons receiving the influenza vaccine, the outcome being prevented, and vaccine-specific qualities. The degree of vaccine match with circulating strains is a major factor affecting vaccine effectiveness. A vaccine mismatch occurs if the antigenic properties of circulating viruses differ significantly from those of the corresponding vaccine virus; an example would be an ≥8-fold reduction in the hemagglutination inhibition (HI) titer for circulating viruses of a given type or subtype compared with the homologous titer for the corresponding vaccine virus . AntigenA 2016 systematic review determined that pooled effectiveness against symptomatic influenza caused by A(H3N2) in the general outpatient population was as low as 23% (95% CI: 2% to 40%) when there was a mismatch between the vaccine and circulating viruses . AdditioRandomized controlled trials (RCT) and real-world evidence (RWE) are both necessary and complementary components in understanding the public health value of seasonal influenza vaccination. RCTs are designed to evaluate the efficacy and safety in well-defined and usually healthy study populations. The observational nature of RWE studies permits the assessment of vaccine effectiveness more frequently and in larger and more inclusive populations in the context of routine care ,11. AlonIn this review, we discuss the relative vaccine effectiveness (rVE) of IIV4c versus IIV4e in children and adults over three consecutive influenza seasons from 2017 to 2020, as well as the epidemiological characteristics of each season. The rVE for each season was estimated based on the same underlying EMR dataset for all three seasons, with the addition of linked claims for the latter two seasons ,24,25,26This is a review of three retrospective cohort studies that were conducted during the 2017–2018, 2018–2019, and 2019–2020 influenza seasons. The study of the 2017–2018 season used a dataset of electronic medical records (EMRs) from primary care and specialty clinics . For the subsequent two seasons, this EMR dataset was linked to available pharmacy and medical claims . The dataset for each retrospective cohort study included only de-identified clinical data that met Protected Health Information (PHI) data requirements and were certified for Health Insurance Portability and Accountability Act (HIPAA) compliance. The studies were conducted and reported in accordance with Good Pharmacoepidemiological Practice from the Declaration of Helsinki, which is applicable to local regulations, and the Reporting of Studies Conducted using Observational Routinely Collected Health Data (RECORD) ,25,26,28Eligible subjects were ≥4 years of age, resided in the US, and had a record of vaccination with IIV4c or IIV4e as determined using current procedural terminology (CPT), code for vaccine administered (CVX), and/or national drug codes (NDC) . The obsadjusted) × 100. Additional details on the statistical methods can be found in the original publications . To. To32]. The 2017–2018 season was a high-severity season in which the most prevalent strain was A(H3N2), with some B Yamagata circulation in the latter half of the season and documented egg-adaptation for A(H3N2) ,38,39. TA total of 1,353,862 vaccinated individuals were included in the analysis of the 2017–2018 season; 10,126,333 individuals in the 2018–2019 season; and 5,625,478 individuals in the analysis of the 2019–2020 season . IIV4c rIIV4c performed better than IIV4e in the prevention of IRMEs in the overall population and in the pediatric and adult subgroups over the three consecutive US influenza seasons, except for the 4–17-year age group in 2017–2018 and adults ≥65 years old in all seasons. The rVE for the overall population ranged from 7.6% for the 2018–2019 season to 19.2% for the 2017–2018 season. IIV4c significantly improved protection from IRME relative to IIV4e in the overall population and the 18–64 age subgroup during all three seasons  22,23,2,26. In tThe relative benefit of IIV4c over IIV4e was most prominent in the 2017–2018 season, during which A(H3N2) viruses predominated and there was also documented egg-adaptation in the A(H3N2) strains of IIV4e vaccines . Point eIn the oldest age group (≥65 years old) during the 2017–2018 and 2018–2019 seasons, there were no significant differences between IIV4c and IIV4e. In 2019–2020, the rVE showed a benefit of IIV4e over IIV4c among patients aged ≥65 years old for protection from IRME relative to IIV4e. As shown in Influenza activity and/or healthcare-seeking behavior changed in mid-March 2020  as COVIDA subgroup analysis conducted during 2018–2019 included 2,113,216 subjects ≥4 years of age with at least one condition that put them at high risk of influenza complications  24,31].,31.24,31During the 2019–2020 season, the CDC reported an estimated 74,717 influenza hospitalizations among adults 18–49 years of age and 86,171 influenza hospitalizations among adults 50–64 years of age . In the Influenza causes significant morbidity and mortality globally. Vaccination against influenza is a central strategy in the prevention of influenza illness. However, vaccine effectiveness varies each season, with multiple factors contributing to sub-optimal vaccine effectiveness in some seasons. While many factors influence vaccine effectiveness, egg-adaptation related to the production of influenza vaccines in embryonated chicken eggs may be avoided by cell-culture technologies . Cell-cuIIV4c often had higher relative effectiveness than IIV4e across three seasons from 2017 to 2020, which were characterized by different dominant circulating strains and different levels of drift or egg-adaptation. Currently, the US Advisory Committee on Immunization Practices (ACIP) does not preferentially recommend any specific vaccine over any other when more than one licensed, age-appropriate vaccine is available, particularly because all vaccine options are not uniformly available throughout the US . HoweverFor the ≥65 year age group, the rVE point estimates favored IIV4e over IIV4c in all three seasons evaluated, but the difference was only significant in the 2019–2020 season. The statistical methods used in the studies were intended to adjust for baseline characteristic differences between individuals who received one vaccine over another, which could be related to their risk of having influenza illness and/or seeking medical care for that illness. In the absence of a clear biological rationale for improved vaccine effectiveness of IIV4e versus IIV4c among older adults—given that both of these non-adjuvanted, inactivated vaccines have the same antigen content—it is possible our findings may be partially the result of residual confounding due to remaining patient characteristic imbalances between the two vaccine groups. A binary categorization of the presence/absence of certain CCI comorbidity groups may be sufficient for adjusting for health status differences for individuals younger than 65 years of age who tend to have fewer comorbidities, but it may be insufficient among older adults who may have multiple comorbidities. If having multiple comorbidities (or certain combinations of comorbidities) resulted in a multiplicative increase in the risk of influenza , then some degree of residual confounding may be expected.® adjuvant or a higher dose of influenza antigens have been designed for the population aged ≥65 years of age [The apparent lack of benefit of IIV4c versus IIV4e among patients aged ≥65 years of age suggests that other vaccines may be preferred for this population. Due to immunosenescence, vaccines enhanced with either the MF59s of age . A recens of age .The reduction in IRMEs for the pediatric population and adults younger than 65 years of age observed in this study could do much to reduce the overall burden of influenza. Influenza infections in children and adults <65 years of age account for an estimated annual average economic burden of $1.84 billion in direct medical expenditures and $6.94 billion in indirect costs due to missed school by sick children and missed work by sick adults and the parents and guardians who care for children with influenza ,55. The


Despite decades of clinical use and detailed understandings of their mechanisms of action, clinically useful predictive biomarkers for approved microtubule targeting agents such as eribulin, paclitaxel and vinorelbine have eluded development. Our results now provide the basis for gene expression-based, drug-specific predictive biomarkers for eribulin and vinorelbine, as well as deeper understandings of the molecular pathways associated with eribulin and vinorelbine response.p-values and drug overlaps, yielding 52, 29 and 80 genes uniquely associated with eribulin, paclitaxel and vinorelbine, respectively. Further restriction to minimum 2-fold ratios followed by reintroducing data from the middle two quartiles identified 9 and 13 drug-specific unique fingerprint genes for eribulin and vinorelbine, respectively; surprisingly, no gene met all criteria for paclitaxel. Interactome and Reactome pathway analyses showed that unique fingerprint genes of both drugs were primarily associated with cellular signaling, not microtubule-related pathways, although considerable differences existed in individual pathways identified. Finally, four-gene  and five-gene  multivariate regression models for eribulin and vinorelbine showed high statistical correlation with drug-specific responses across the 100 cell lines and accurately calculated predicted mean IC50s for the most and least sensitive cell line quartiles as surrogates for responders and nonresponders, respectively. Collectively, these results provide a foundation for developing drug-specific predictive biomarkers for eribulin and vinorelbine.Eribulin, a natural product-based microtubule targeting agent with cytotoxic and noncytotoxic mechanisms, is FDA approved for certain patients with advanced breast cancer and liposarcoma. To investigate the feasibility of developing drug-specific predictive biomarkers, we quantified antiproliferative activities of eribulin versus paclitaxel and vinorelbine against 100 human cancer cell lines from the Cancer Cell Line Encyclopedia, and correlated results with publicly available databases to identify genes and pathways associated with eribulin response, either uniquely or shared with paclitaxel or vinorelbine. Mean expression ratios of 11,985 genes between the most and least sensitive cell line quartiles were sorted by  Microtubule targeting agents (MTAs), including eribulin, several taxanes, vinca alkaloids, and at least one epothilone, are important clinical anticancer drugs for several cancer types ,2,3,4. DEribulin, a synthetic analog of the marine sponge natural product halichondrin B , is an M®. Paclitaxel and vinorelbine tartrate  were obtained as dry powders from Selleckchem . Eribulin, paclitaxel and vinorelbine were prepared as 10 mM stock solutions in 100% (v/v) DMSO, aliquoted into small volumes and stored at −20 °C until day of use. Although not a designated test agent for this study, cisplatin was included in all assays as an internal reference control for assay performance; cisplatin was obtained as a laboratory grade liquid formulation from Hospira Australia Pty Ltd. , with storage per manufacturer’s instructions and dilutions for cell culture studies on day of use.Eribulin mesylate  was supplied by Eisai Inc.  as laboratory-grade dry powder active pharmaceutical ingredient (API) obtained from the same manufacturing stream used for Eisai’s branded clinical product, HalavenTM in vitro cell line screening service (see One hundred established human cancer cell lines were selected based on inclusion in both the CCLE and Crown Bioscience’s OmniPanelvice see . A list TM in vitro cell line screening service in their Beijing (China) laboratories. All OmniPanelTM cell lines are routinely tested for mycoplasma and authenticated using short tandem repeat (STR) DNA profiling. Testing was conducted using the CellTiter-Glo® Luminescent Cell Viability Assay  following 72 h compound exposures in 96-well plate assay formats. Compounds were added 24 h after cell seeding into plates, with initial seeding densities for individual cell lines having been previously optimized for OmniPanelTM screening. Based on the authors’ prior experience with the test agents, assays employed 9-step half-log test concentrations of 30 pM–300 nM for eribulin and 100 pM–1 μM for paclitaxel and vinorelbine. Concentrations of test agents inhibiting 50% of viable cell densities in vehicle-treated control wells compared to wells with the highest drug concentrations were defined as IC50s and were determined using GraphPad Prism software . Dose–response curve fitting used a nonlinear regression model with sigmoidal dose response, fixed 100% top y-axis values defined by vehicle and floating bottom y-axis values at either the highest concentration tested or another concentration that resulted in a minimum y-axis reading, ensuring that calculated IC50s represent actual antiproliferative biological responses occurring during the 72 h treatment period without plateau artifacts based on different growth rates of different cell lines.Measurement of antiproliferative activities of test agents against the selected 100 CCLE cell lines was performed by Crown Bioscience under contract from Eisai Inc., using Crown’s OmniPanelIC50s for 5 cell lines for eribulin  and 1 cell line for paclitaxel (HCT-15) exceeded the 300 nM and 1000 nM top concentrations used for these 2 drugs, respectively, so surrogate values of 301 nM eribulin and 1001 nM paclitaxel were assigned for purposes of defining cell lines in the bottom (least sensitive) quartile and calculating means/medians of bottom quartiles. Although using such surrogates does not change assignment of bottom quartiles or median IC50 values, it probably results in minor underestimations of mean bottom quartile IC50s, SDs and SEMs for eribulin and paclitaxel, although such underestimations are likely to be small due to the large overall number of cell lines tested.https://depmap.org/portal/ accessed on 3 August 2021; release version “DepMap Public 18Q2”, 2 May 2018). DepMap data using the symbol SEPT10 were replaced with the currently accepted symbol SEPTIN10. Expression values are log2 gene-level reads per kilobase million (RPKM) derived from RNA Sequencing (RNA-Seq), aligned using TopHat version 1.4 and quantified using the pipeline developed for the Genotype-Tissue Expression (GTEx) project as described by Tsherniak et al. | ≥ 1) and (iii) after reintroducing data from the middle two cell line quartiles, retaining only those genes with statistical significance (p < 0.05) for the cognate drug in full linear regression analysis with all 100 cell lines, but lacking statistical significance for both noncognate drugs. Genes meeting all the above criteria for a given drug were designated as unique fingerprint genes or UFGs for that drug. No gene met all criteria for paclitaxel; thus, no paclitaxel UFGs were identified in this study.Ratios of mean expression levels for each gene between the most and least sensitive cell line quartiles  for each drug were calculated. Genes with top/bottom quartile ratios with ® Cloud  ≥ 1). Following this restriction, data from the middle two cell line quartiles were then reintroduced, and each gene in the three subsets was subjected to full linear regression analysis including expression data and IC50s from all 100 cell lines. Only genes with expression showing statistical significance for a given cognate drug but lacking statistical significance for the other two noncognate drugs were designated as UFGs for each drug. This selection process yielded 9 UFGs for eribulin and 13 UFGs for vinorelbine  not utilizing gene expression information from the 50 cell lines in the middle two quartiles, (ii) accepting a risk that small numbers of cell lines in the top and bottom quartiles might have outlier expression patterns that could disproportionately influence gene selection, and (iii) failing to provide a basis for identifying drug-specific UFGs based on the informational power available from all 100 cell lines. To address these limitations, a final curation of the 52, 29 and 80 ectively F,G, was orelbine . UnexpecC5ORF38 and GPR157, were not themselves captured by network propagation. Interestingly, despite sharing mechanistic similarity as MTAs, only four genes overlapped between the eribulin and vinorelbine 100-gene propagated networks . We speculate that this may relate to the fact that the original UFGs were selected, in part, based on lack of correlations to noncognate drugs.We next asked what mechanisms and pathways were associated with eribulin and vinorelbine UFGs. To this end, network propagation  analysesTo further elucidate the pathways associated with the eribulin and vinorelbine UFG interactomes, the eribulin and vinorelbine 100-gene propagated networks were used as query inputs for Reactome pathway enrichment analyses. The resulting Reactome maps indicate that most pathways for both drugs fall into three main Reactome groupings or ‘islands’: the Signal Transduction, Immune System and Cell Cycle islands C,D. Whilp < 0.05 significance plus q < 0.1 to account for FDR, 26 and 122 Reactome pathways were identified for eribulin and vinorelbine UFG interactomes, respectively  by first calculating each cell line’s predicted IC50 based on individual expression of each of the 9 eribulin or 13 vinorelbine UFGs for that cell line, averaging the resulting 9 or 13 predicted IC50s for each cell line, then comparing the averaged predicted IC50s to actual measured IC50s for each cell line for all three drugs. Although no gene met the criteria for assignment as a paclitaxel UFG, predicted IC50s based on expression of eribulin or vinorelbine UFGs are conceptually drug agnostic, so paclitaxel was included as a drug specificity comparator. Not surprisingly, the resulting MVR models built from all 9 or 13 eribulin or vinorelbine UFGs, respectively, successfully predicted cognate drug IC50s with high statistical significance . Unexpec2 values) yielded a four-gene eribulin MVR model  and a five-gene vinorelbine MVR model ; predicted IC50s from both models correlated with measured IC50s across all 100 cell lines with high statistical significance for cognate but not noncognate drugs  whose expression is uniquely associated with response to eribulin versus the other two drugs, with four eribulin UFGs being sufficient for a multigene panel that accurately predicts high versus low eribulin response. Separately, 13 UFGs were identified as uniquely associated with vinorelbine response, with 5 of these being sufficient for a predictive vinorelbine multigene panel. Notably, no genes uniquely associated with paclitaxel response were identified using the same stringent criteria used to identify eribulin and vinorelbine UFGs.In addition to its cytotoxic antimitotic activity ,6,7, eri−7 to 10−6 M range for all three drugs, eribulin’s steeper slope resulted primarily from increased responses in its most sensitive cell lines, which collectively dropped into the sub-nM IC50 range. In addition, several nonoverlapping cell lines existed for each drug in their respective top and bottom quartiles, suggesting that aggregating response data across large numbers of cell lines might yield information on drug-specific pathways and mechanisms, a concept first established by the well-known NCI60 cell line screen . Coincidentally, the NCI60 screen was first used to predict a tubulin-based mechanism for eribulin’s natural product parent, halichondrin B [Responses of 100 CCLE human cancer cell lines to eribulin and comparator MTAs paclitaxel and vinorelbine were first quantified by IC50 values and ordered by response. While sensitivities across the 100 cell lines trended in the same direction for the three drugs, the range of eribulin’s response from most to least sensitive was considerably greater than the other two drugs, reflected by a much steeper IC50 slope for eribulin compared to the shallower and parallel slopes for paclitaxel and vinorelbine. Importantly, while IC50s for the least sensitive cell lines were in the same 10ondrin B ,25. We sp-value and gene expression ratios between top and bottom quartiles provided the first indications that the three drugs could be distinguished from each other for biomarker development. Notably, at p < 0.0025 stringency, 52 and 80 genes were unique to eribulin and vinorelbine, respectively, with no genes shared between eribulin and paclitaxel, and only nine genes shared between eribulin and vinorelbine. At the same stringency, vinorelbine and paclitaxel shared only one gene. Thus, paclitaxel, a microtubule stabilizer, showed the least similarity with eribulin and vinorelbine, both microtubule destabilizers, yet even eribulin and vinorelbine could be distinguished from each other based on many more genes uniquely associated with each drug compared to shared genes between them.Using top and bottom quartiles as surrogates for responders and nonresponders, respectively, IC50s were correlated with DepMap gene expression data for 11,985 genes. Z-score heat mapping and filtering on p < 0.0025 genes to include only those with at least 2-fold expression difference between top and bottom quartiles, followed by reintroduction of data from the 50 cell lines in the middle two quartiles, resulted in identification of 9 and 13 UFGs for eribulin and vinorelbine, respectively. Interestingly, no gene met these final UFG criteria for paclitaxel. Notably, seven of the nine eribulin UFGs were upregulated with increased response, while 11 of the 13 vinorelbine UFGs were downregulated with increased response, supporting our hypothesis that increased eribulin response may be driven by upregulation of pathways governing sensitivity, whereas increased vinorelbine response may depend more on downregulation of general resistance pathways.Further restriction of the To elucidate biological pathways associated with eribulin and vinorelbine UFGs, network propagation  was usedFinally, subsets of eribulin and vinorelbine UFGs prioritized by highest individual correlation coefficients were used to build drug-specific MVR models that correlate with responses across all 100 cell lines and accurately predict average response levels of the most and least sensitive quartiles based solely on expression levels of only four or five genes, respectively. While others have reported gene or pathway signatures for MTAs, these have been mainly for paclitaxel ,29,30,31IRX2 is a homeobox gene important in normal embryonic development and has been implicated in cancer, while C5ORF38 is coordinately regulated with IRX2, suggesting both are involved in cellular differentiation and growth regulation [DAAM1 is involved in Wnt signaling and early embryonic gastrulation, both processes associated with cellular differentiation [CD70 codes for a ligand for the immune costimulatory molecule CD27 and thus is involved in the activation and proliferation of T cells, including regulatory T cells [Considering the importance of signaling-related Reactome pathways in eribulin and vinorelbine responses, it is reasonable to ask if the four eribulin and five vinorelbine UFGs in the predictive MVR models directly control responses to the two drugs. Strictly speaking, UFG expression levels were only correlated with response, so firm conclusions of direct involvement cannot be drawn. However, with that caveat, information on known roles of the genes permits some speculation. For eribulin, gulation . Similarntiation . Finally T cells . Thus, wEPHA2 codes for EPH receptor A2, a tyrosine kinase involved in cancer-related signaling, while NGEF codes for a guanine nucleotide exchange factor associated with signaling from EPH receptor A2, RhoA, Rac1 and CDC42 as well as cellular transformation and tumorigenesis [SEPTIN10 is associated with B cell leukemias [TRIP10 is involved in tumorigenesis and cancer progression [VSIG10 codes for an immunoglobulin-related protein associated with both cell adhesion and macrophage involvement in colonic pathologies including colon cancer [For vinorelbine, igenesis ,47. SEPTeukemias , while Tgression . Finallyn cancer . Thus, wIn summary, we have combined response data for eribulin, paclitaxel and vinorelbine from 100 human cancer cell lines together with gene expression data to identify small numbers of genes (UFGs) that are uniquely associated with eribulin and vinorelbine responses. Reactome pathway analyses based on UFG-seeded propagated gene networks revealed that responses to both eribulin and vinorelbine are dominantly associated with cellular signaling processes as opposed to canonical MT-based antimitotic processes, perhaps also explaining our inability to identify paclitaxel UFGs using the same criteria. Despite top-level dependence on cellular signaling for response to both eribulin and vinorelbine, detailed analyses of the specific Reactome pathways identified reveal considerable discrimination between the two drugs. Finally, we show that small subsets of eribulin and vinorelbine UFGs can be successfully combined into MVR models that accurately predict a high versus low response to the two drugs based on expression of only four or five genes. Our results indicate that further investigation of the genes, pathways and MVR panels identified here is warranted, with further validation in both preclinical tumor models and in the clinical setting being of highest priority. Ultimately, our hope is that the current results together with such future validation work will lead to the development of drug-specific, gene expression-based predictive biomarker panels for eribulin and vinorelbine that support improved therapeutic decision-making in clinical settings.