Molecular characterization and survival analysis of a cohort of glioblastoma, IDH-wildtype.

Glioblastoma, IDH-wild type, the most common malignant primary central nervous system tumor, represents a formidable challenge in clinical management due to its poor prognosis and limited therapeutic responses. With an evolving understanding of its underlying biology, there is an urgent need to identify prognostic molecular groups that can be subject to targeted therapy. This study established a cohort of 124 sequential glioblastomas from a tertiary hospital and aimed to find correlations between molecular features and survival outcomes. Comprehensive molecular characterization of the cohort revealed prevalent alterations as previously described, such as TERT promoter mutations and involvement of the PI3K-Akt-mTOR, CK4/6-CDKN2A/B-RB1, and p14ARF-MDM2-MDM4-p53 pathways. MGMT promoter methylation is a significant predictor of improved overall survival, aligned with previous data. Conversely, age showed a marginal association with higher mortality. Multivariate analysis to account for the effect of MGMT promoter methylation and age showed that, in contrast to other published series, this cohort demonstrated improved survival for tumors harboring PTEN mutations, and that there was no observed difference for most other molecular alterations, including EGFR amplification, RB1 loss, or the coexistence of EGFR amplification and deletion/exon skipping (EGFRvIII). Despite limitations in sample size, this study contributes data to the molecular landscape of glioblastomas, prompting further investigations to examine these findings more closely in larger cohorts.

Structural complexity biases vegetation greenness measures.

Vegetation 'greenness' characterized by spectral vegetation indices (VIs) is an integrative measure of vegetation leaf abundance, biochemical properties and pigment composition. Surprisingly, satellite observations reveal that several major VIs over the US Corn Belt are higher than those over the Amazon rainforest, despite the forests having a greater leaf area. This contradicting pattern underscores the pressing need to understand the underlying drivers and their impacts to prevent misinterpretations. Here we show that macroscale shadows cast by complex forest structures result in lower greenness measures compared with those cast by structurally simple and homogeneous crops. The shadow-induced contradictory pattern of VIs is inevitable because most Earth-observing satellites do not view the Earth in the solar direction and thus view shadows due to the sun-sensor geometry. The shadow impacts have important implications for the interpretation of VIs and solar-induced chlorophyll fluorescence as measures of global vegetation changes. For instance, a land-conversion process from forests to crops over the Amazon shows notable increases in VIs despite a decrease in leaf area. Our findings highlight the importance of considering shadow impacts to accurately interpret remotely sensed VIs and solar-induced chlorophyll fluorescence for assessing global vegetation and its changes.

Capturing patterns of evolutionary relatedness with reflectance spectra to model and monitor biodiversity.

Biogeographic history can set initial conditions for vegetation community assemblages that determine their climate responses at broad extents that land surface models attempt to forecast. Numerous studies have indicated that evolutionarily conserved biochemical, structural, and other functional attributes of plant species are captured in visible-to-short wavelength infrared, 400 to 2,500 nm, reflectance properties of vegetation. Here, we present a remotely sensed phylogenetic clustering and an evolutionary framework to accommodate spectra, distributions, and traits. Spectral properties evolutionarily conserved in plants provide the opportunity to spatially aggregate species into lineages (interpreted as "lineage functional types" or LFT) with improved classification accuracy. In this study, we use Airborne Visible/Infrared Imaging Spectrometer data from the 2013 Hyperspectral Infrared Imager campaign over the southern Sierra Nevada, California flight box, to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from 1372 surveys and a phylogeny representing 1,572 species. Despite temporal and spatial differences in our training data, we classified plant lineages with moderate reliability (Kappa = 0.76) and overall classification accuracy of 80.9%. We present an assessment of classification error and detail study limitations to facilitate future LFT development. This work demonstrates that lineage-based methods may be a promising way to leverage the new-generation high-resolution and high return-interval hyperspectral data planned for the forthcoming satellite missions with sparsely sampled existing ground-based ecological data.

EGFR and ERBB2 exon 20 insertion/duplication in advanced non-small cell lung cancer: genomic profiling and clinicopathologic features.

Background: Exon 20 (ex20) in-frame insertions or duplications (ins/dup) in epidermal growth factor receptor (EGFR) and its analog erb-b2 receptor tyrosine kinase 2 (ERBB2) are each detected in 1.5% of non-small cell lung cancer (NSCLC). Unlike EGFR p.L858R or ex19 deletions, ex20 ins/dup is associated with de novo resistance to classic EGFR inhibitors, lack of response to immune checkpoint inhibitors, and poor prognosis. US Food and Drug Administration has approved mobocertinib and amivantamab for targeting tumors with this aberration, but the number of comprehensive studies on ex20 ins/dup NSCLC is limited. We identified 18 cases of NSCLCs with EGFR/ERBB2 ex20 ins/dup and correlated the findings with clinical and morphologic information including programed death-ligand 1 (PD-L1) expression. Methods: A total of 536 NSCLC cases tested at our institution between 2014 and 2023 were reviewed. A custom-designed 214-gene next-generation sequencing panel was used for detecting DNA variants, and the FusionPlex CTL panel (ArcherDx) was used for the detection of fusion transcripts from formalin-fixed, paraffin-embedded tissue. Immunohistochemistry (IHC)for PD-L1 was performed using 22C3 or E1L3N clones. Results: Nine EGFR and nine ERBB2 ex20 ins/dup variants were identified from an equal number of men and women, 14 were non- or light smokers, and 15 had stage IV disease. All 18 cases were adenocarcinomas. Seven of the 11 cases with available primary tumors had acinar predominant pattern, two had lepidic predominant pattern, and the remainder had papillary (one case) and mucinous (one case) patterns. Ex20 ins/dup variants were heterogenous in-frame one to four amino acids spanning A767-V774 in EGFR and Y772-P780 in ERBB2 and were clustered in the loop following the C-helix and α C-helix. Twelve cases (67%) had co-existing TP53 variants. Copy number variation in CDK4 amplification was identified in one case. No fusion or microsatellite instability was identified in any case. PD-L1 was positive in two cases, low positive in four cases, and negative in 11 cases. Conclusions: NSCLCs harboring EGFR/ERBB2 ex20 ins/dup are rare and tend to be acinar predominant, negative for PD-L1, more frequent in non- or light smokers, and mutually exclusive with other driver mutations in NSCLC. The correlation of different EGFR/ERBB2 ex20 ins/dup variants and co-existing mutations with response to targeted therapy and the possibility of developing resistant mutations after mobocertinib treatment warrants further investigation.

Cost-Effectiveness Analysis of 68Ga-DOTATATE PET/MRI in Radiotherapy Planning in Patients with Intermediate-Risk Meningioma.

BACKGROUND AND PURPOSE: While contrast-enhanced MR imaging is the criterion standard in meningioma diagnosis and treatment response assessment, gallium 68Ga-DOTATATE PET/MR imaging has increasingly demonstrated utility in meningioma diagnosis and management. Integrating 68Ga-DOTATATE PET/MR imaging in postsurgical radiation planning reduces the planning target volume and organ-at-risk dose. However, 68Ga-DOTATATE PET/MR imaging is not widely implemented in clinical practice due to higher perceived costs. Our study analyzes the cost-effectiveness of 68Ga-DOTATATE PET/MR imaging for postresection radiation therapy planning in patients with intermediate-risk meningioma. MATERIALS AND METHODS: We developed a decision-analytical model based on both recommended guidelines on meningioma management and our institutional experience. Markov models were implemented to estimate quality-adjusted life-years (QALY). Cost-effectiveness analyses with willingness-to-pay thresholds of $50,000/QALY and $100,000/QALY were performed from a societal perspective. Sensitivity analyses were conducted to validate the results. Model input values were based on published literature. RESULTS: The cost-effectiveness results demonstrated that 68Ga-DOTATATE PET/MR imaging yields higher QALY (5.47 versus 5.05) at a higher cost ($404,260 versus $395,535) compared with MR imaging alone. The incremental cost-effectiveness ratio analysis determined that 68Ga-DOTATATE PET/MR imaging is cost-effective at a willingness to pay of $50,000/QALY and $100,000/QALY. Furthermore, sensitivity analyses showed that 68Ga-DOTATATE PET/MR imaging is cost-effective at $50,000/QALY ($100,000/QALY) for specificity and sensitivity values above 76% (58%) and 53% (44%), respectively. CONCLUSIONS: 68Ga-DOTATATE PET/MR imaging as an adjunct imaging technique is cost-effective in postoperative treatment planning in patients with meningiomas. Most important, the model results show that the sensitivity and specificity cost-effective thresholds of 68Ga-DOTATATE PET/MR imaging could be attained in clinical practice.

A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing.

Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning (DL), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in Earth systems. A difficult test for DL-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A DL emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here, we examine, with a case study in satellite-based remote sensing, the hypothesis that DL approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the DL emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of DL and the growing desire for higher resolution simulations in the Earth sciences.

Temporal Interpolation of Geostationary Satellite Imagery With Optical Flow.

Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the tradeoffs to spatial, spectral, and temporal resolutions of observations. In weather tracking, high-frequency temporal observations are critical and used to improve forecasts, study severe events, and extract atmospheric motion, among others. However, while the current generation of geostationary (GEO) satellites has hemispheric coverage at 10-15-min intervals, higher temporal frequency observations are ideal for studying mesoscale severe weather events. In this work, we present a novel application of deep learning-based optical flow to temporal upsampling of GEO satellite imagery. We apply this technique to 16 bands of the GOES-R/Advanced Baseline Imager mesoscale dataset to temporally enhance full-disk hemispheric snapshots of different spatial resolutions from 10 to 1 min. Experiments show the effectiveness of task-specific optical flow and multiscale blocks for interpolating high-frequency severe weather events relative to bilinear and global optical flow baselines. Finally, we demonstrate strong performance in capturing variability during convective precipitation events.

Greening of human-dominated ecosystems in India.

Satellite data show the Earth has been greening and identify croplands in India as one of the most prominent greening hotspots. Though India's agriculture has been dependent on irrigation enhancement to reduce crop water stress and increase production, the spatiotemporal dynamics of how irrigation influenced the satellite observed greenness remains unclear. Here, we use satellite-derived leaf area data and survey-based agricultural statistics together with results from state-of-the-art Land Surface Models (LSM) to investigate the role of irrigation in the greening of India's croplands. We find that satellite observations provide multiple lines of evidence showing strong contributions of irrigation to significant greening during dry season and in drier environments. The national statistics support irrigation-driven yield enhancement and increased dry season cropping intensity. These suggest a continuous shift in India's agriculture toward an irrigation-driven dry season cropping system and confirm the importance of land management in the greening phenomenon. However, the LSMs identify CO2 fertilization as a primary driver of greening whereas land use and management have marginal impacts on the simulated leaf area changes. This finding urges a closer collaboration of the modeling, Earth observation, and land system science communities to improve representation of land management in the Earth system modeling.

Genomic Profiling of Metastatic Uveal Melanoma Shows Frequent Coexisting BAP1 or SF3B1 and GNAQ/GNA11 Mutations and Correlation With Prognosis.

OBJECTIVES: To identify therapeutic targets and correlate with clinical outcomes from mutation profiling of metastatic uveal melanoma (UM) using next-generation sequencing (NGS). METHODS: Melanoma cases that were tested using DNA-based NGS panels of 25 and/or 214 genes were evaluated retrospectively (263 cases) and identified 27 UM cases. BAP1 expression was examined by immunohistochemistry. RESULTS: Mutations in GNA11 (14) and GNAQ (12) were found in 96% (n = 27) of cases of UM, and most had coexisting BAP1 (17) or SF3B1 (4) mutations. Coexisting GNAQ/11-SF3B1 mutations correlated with a longer average time to first metastasis compared with GNAQ/11-BAP1 mutations (99.7 vs 38.5 months, P = .047). Three patients with BAP1 mutations received trametinib; two are still alive (15 months; 23 months), and one died (32 months). In non-UMs, only 4.2% (n = 236) had BAP1 and 3.8% had SF3B1 mutations; none had coexisting GNAQ/11 mutations. CONCLUSIONS: Coexisting BAP1/SF3B1 and GNAQ/11 mutations were unique to UM. SF3B1 mutations were reported to be UM-specific in melanoma and associated with rare/no metastasis. The finding of mutated SF3B1 in 14.8% (n = 27) of UMs suggests its role should be further evaluated. The correlation of BAP1/SF3B1 mutation with survival also warrants investigation.

Validation of Optical Genome Mapping for the Molecular Diagnosis of Facioscapulohumeral Muscular Dystrophy.

The molecular diagnosis of facioscapulohumeral muscular dystrophy (FSHD) relies on detecting contractions of the unique D4Z4 repeat array at the chromosome 4q35 locus in the presence of a permissive 4q35A haplotype. Long, intact DNA molecules are required for accurate sizing of D4Z4 repeats. We validated the use of optical genome mapping to determine size and haplotype of D4Z4 alleles for FSHD analysis. The cohort included 36 unique DNA specimens from fresh blood samples or archived agarose plugs. High-molecular- weight DNA underwent sequence-specific labeling followed by separation and image analysis with data collection on the Saphyr system. D4Z4 allele sizes were calculated and haplotypes determined from the labeling patterns. Each specimen had previous diagnostic testing using restriction enzyme digests with EcoRI, EcoRI/BlnI, XapI, or HindIII, followed by pulsed field gel electrophoresis and Southern blot analysis with appropriate probes. Optical genome mapping detected 4q35 and 10q26 alleles ranging from 1 to 79 D4Z4 repeats and showed strong correlation with Southern blot allele sizing (R2 = 0.95) and haplotyping (133 of 134; 99.4% haplotype match). Analysis of inter-assay and intra-assay runs showed high reproducibility (0.03 to 0.94 %CV). Subsequent optical genome mapping for routine clinical testing from 315 clinical FSHD cases compared favorably with historical result trends. Optical genome mapping is an accurate and highly reproducible method for chromosomal abnormalities associated with FSHD.

A validated surrogate analyte LC-MS/MS assay for quantification of endogenous cortisol in human whole blood.

Cortisol is a steroid hormone that is frequently measured as a marker of stress, inflammation, and immune function. While commonly analyzed in saliva, hair, blood plasma and urine, a recent trend towards whole blood-based at-home collection devices has emerged, which necessitates development of more sensitive assays for cortisol in whole blood. To support the implementation of a patient-centric sampling approach in a drug development program, a fit-for-purpose surrogate analyte-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for cortisol in whole blood was developed using 13C3-cortisol as a surrogate analyte and cortisol-d6 as the internal standard. The surrogate analyte approach was chosen due to a lack of available cortisol-free whole blood and the absence of appropriately representative surrogate matrices. Samples were prepared using supported liquid extraction, and the LC-MS/MS analysis consisted of a 4.00 min analytical run. The method demonstrated linearity between 0.500 and 500 ng/mL of 13C3-cortisol, and accuracy, precision and robustness were all acceptable per current regulatory guidance for bioanalytical method validation of chromatographic assays for cortisol- and 13C3-cortisol-based quality control (QC) samples when quantified against a 13C3-cortisol calibration curve. The acceptable robustness of cortisol-based QCs when quantified against a 13C3-cortisol-based calibration curve also suggests parallelism between the analytes. These results indicate a viable surrogate analyte method, that is fit-for-purpose to analyze whole blood cortisol levels using a surrogate analyte LC-MS/MS approach. Evaluation of patient samples showed very promising comparability between whole blood and plasma cortisol concentrations, suggesting that whole blood could be used in place of or in addition to a plasma-based sampling protocol in clinical trials analyzing cortisol. Overall, this method presents a novel tool that is a first step in supporting the trend towards sample miniaturization and at-home sample collection, and may be readily used in clinical and diagnostic settings.

New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests.

Assessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10-15 min. Our analysis of NDVI data collected over the Amazon during 2018-19 shows that ABI provides 21-35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

Targeted broad-based genetic testing by next-generation sequencing informs diagnosis and facilitates management in patients with kidney diseases.

BACKGROUND: The clinical diagnosis of genetic renal diseases may be limited by the overlapping spectrum of manifestations between diseases or by the advancement of disease where clues to the original process are absent. The objective of this study was to determine whether genetic testing informs diagnosis and facilitates management of kidney disease patients. METHODS: We developed a comprehensive genetic testing panel (KidneySeq) to evaluate patients with various phenotypes including cystic diseases, congenital anomalies of the kidney and urinary tract (CAKUT), tubulointerstitial diseases, transport disorders and glomerular diseases. We evaluated this panel in 127 consecutive patients ranging in age from newborns to 81 years who had samples sent in for genetic testing. RESULTS: The performance of the sequencing pipeline for single-nucleotide variants was validated using CEPH (Centre de'Etude du Polymorphism) controls and for indels using Genome-in-a-Bottle. To test the reliability of the copy number variant (CNV) analysis, positive samples were re-sequenced and analyzed. For patient samples, a multidisciplinary review board interpreted genetic results in the context of clinical data. A genetic diagnosis was made in 54 (43%) patients and ranged from 54% for CAKUT, 53% for ciliopathies/tubulointerstitial diseases, 45% for transport disorders to 33% for glomerulopathies. Pathogenic and likely pathogenic variants included 46% missense, 11% nonsense, 6% splice site variants, 23% insertion-deletions and 14% CNVs. In 13 cases, the genetic result changed the clinical diagnosis. CONCLUSION: Broad genetic testing should be considered in the evaluation of renal patients as it complements other tests and provides insight into the underlying disease and its management.

Biophysical impacts of Earth greening largely controlled by aerodynamic resistance.

Satellite observations show widespread increasing trends of leaf area index (LAI), known as the Earth greening. However, the biophysical impacts of this greening on land surface temperature (LST) remain unclear. Here, we quantify the biophysical impacts of Earth greening on LST from 2000 to 2014 and disentangle the contributions of different factors using a physically based attribution model. We find that 93% of the global vegetated area shows negative sensitivity of LST to LAI increase at the annual scale, especially for semiarid woody vegetation. Further considering the LAI trends (P ≤ 0.1), 30% of the global vegetated area is cooled by these trends and 5% is warmed. Aerodynamic resistance is the dominant factor in controlling Earth greening's biophysical impacts: The increase in LAI produces a decrease in aerodynamic resistance, thereby favoring increased turbulent heat transfer between the land and the atmosphere, especially latent heat flux.

ANT2681: SAR Studies Leading to the Identification of a Metallo-ß-lactamase Inhibitor with Potential for Clinical Use in Combination with Meropenem for the Treatment of Infections Caused by NDM-Producing Enterobacteriaceae.

The clinical effectiveness of the important ß-lactam class of antibiotics is under threat by the emergence of resistance, mostly due to the production of acquired serine- (SBL) and metallo-ß-lactamase (MBL) enzymes. To address this resistance issue, multiple ß-lactam/ß-lactamase inhibitor combinations have been successfully introduced into the clinic over the past several decades. However, all of those combinations contain SBL inhibitors and, as yet, there are no MBL inhibitors in clinical use. Consequently, there exists an unaddressed yet growing healthcare problem due to the rise in recent years of highly resistant strains which produce New Delhi metallo (NDM)-type metallo-carbapenemases. Previously, we reported the characterization of an advanced MBL inhibitor lead compound, ANT431. Herein, we discuss the completion of a lead optimization campaign culminating in the discovery of the preclinical candidate ANT2681, a potent NDM inhibitor with strong potential for clinical development.

Novel LC-MS/MS method for the determination of selumetinib (AZD6244) in whole blood collected with volumetric absorptive microsampling.

Aim: A method has been developed and validated for quantitation of selumetinib in human whole blood collected using a Mitra™ volumetric absorptive microsampling device. This device is patient-friendly, affording less-invasive sampling with broad applicability to clinical and diagnostic applications - specifically in pediatric populations. Materials & methods: In this method, drug is extracted from the Mitra device via sonication in methanol: Ammonium hydroxide, then analyzed by LC-MS/MS. The linear range for selumetinib analysis is 2.00-2000 ng/ml. Results: All validation parameters met acceptance criteria established in agreement with current regulatory guidance for bioanalytical method validation. The stability of selumetinib in Mitra tips was established at both ambient and frozen conditions. Conclusion: A simple method has been developed and validated for determination of selumetinib from human whole blood, collected using volumetric absorptive microsampling and analyzed by LC-MS/MS.

Infection-induced plasmablasts are a nutrient sink that impairs humoral immunity to malaria.

Plasmodium parasite-specific antibodies are critical for protection against malaria, yet the development of long-lived and effective humoral immunity against Plasmodium takes many years and multiple rounds of infection and cure. Here, we report that the rapid development of short-lived plasmablasts during experimental malaria unexpectedly hindered parasite control by impeding germinal center responses. Metabolic hyperactivity of plasmablasts resulted in nutrient deprivation of the germinal center reaction, limiting the generation of memory B cell and long-lived plasma cell responses. Therapeutic administration of a single amino acid to experimentally infected mice was sufficient to overcome the metabolic constraints imposed by plasmablasts and enhanced parasite clearance and the formation of protective humoral immune memory responses. Thus, our studies not only challenge the current model describing the role and function of blood-stage Plasmodium-induced plasmablasts but they also reveal new targets and strategies to improve anti-Plasmodium humoral immunity.

Changes in timing of seasonal peak photosynthetic activity in northern ecosystems.

Seasonality in photosynthetic activity is a critical component of seasonal carbon, water, and energy cycles in the Earth system. This characteristic is a consequence of plant's adaptive evolutionary processes to a given set of environmental conditions. Changing climate in northern lands (>30°N) alters the state of climatic constraints on plant growth, and therefore, changes in the seasonality and carbon accumulation are anticipated. However, how photosynthetic seasonality evolved to its current state, and what role climatic constraints and their variability played in this process and ultimately in carbon cycle is still poorly understood due to its complexity. Here, we take the "laws of minimum" as a basis and introduce a new framework where the timing (day of year) of peak photosynthetic activity (DOYPmax ) acts as a proxy for plant's adaptive state to climatic constraints on its growth. Our analyses confirm that spatial variations in DOYPmax reflect spatial gradients in climatic constraints as well as seasonal maximum and total productivity. We find a widespread warming-induced advance in DOYPmax (-1.66 ± 0.30 days/decade, p < 0.001) across northern lands, indicating a spatiotemporal dynamism of climatic constraints to plant growth. We show that the observed changes in DOYPmax are associated with an increase in total gross primary productivity through enhanced carbon assimilation early in the growing season, which leads to an earlier phase shift in land-atmosphere carbon fluxes and an increase in their amplitude. Such changes are expected to continue in the future based on our analysis of earth system model projections. Our study provides a simplified, yet realistic framework based on first principles for the complex mechanisms by which various climatic factors constrain plant growth in northern ecosystems.

China and India lead in greening of the world through land-use management.

Satellite data show increasing leaf area of vegetation due to direct (human land-use management) and indirect factors (climate change, CO2 fertilization, nitrogen deposition, recovery from natural disturbances, etc.). Among these, climate change and CO2 fertilization effect seem to be the dominant drivers. However, recent satellite data (2000-2017) reveal a greening pattern that is strikingly prominent in China and India, and overlapping with croplands world-wide. China alone accounts for 25% of the global net increase in leaf area with only 6.6% of global vegetated area. The greening in China is from forests (42%) and croplands (32%), but in India is mostly from croplands (82%) with minor contribution from forests (4.4%). China is engineering ambitious programs to conserve and expand forests with the goal of mitigating land degradation, air pollution and climate change. Food production in China and India has increased by over 35% since 2000 mostly due to increasing harvested area through multiple cropping facilitated by fertilizer use and surface/ground-water irrigation. Our results indicate that the direct factor is a key driver of the "Greening Earth", accounting for over a third, and likely more, of the observed net increase in green leaf area. They highlight the need for realistic representation of human land-use practices in Earth system models.