Tropical forests play a pivotal role in regulating the global carbon cycle. However, the response of these forests to changes in absorbed solar energy and water supply under the changing climate is highly uncertain. Three-year (2018-2021) spaceborne high-resolution measurements of solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) provide a new opportunity to study the response of gross primary production (GPP) and more broadly tropical forest carbon dynamics to differences in climate. SIF has been shown to be a good proxy for GPP on monthly and regional scales. Combining tropical climate reanalysis records and other contemporary satellite products, we find that on the seasonal timescale, the dependence of GPP on climate variables is highly heterogeneous. Following the principal component analyses and correlation comparisons, two regimes are identified: water limited and energy limited. GPP variations over tropical Africa are more correlated with water-related factors such as vapor pressure deficit (VPD) and soil moisture, while in tropical Southeast Asia, GPP is more correlated with energy-related factors such as photosynthetically active radiation (PAR) and surface temperature. Amazonia is itself heterogeneous: with an energy-limited regime in the north and water-limited regime in the south. The correlations of GPP with climate variables are supported by other observation-based products, such as Orbiting Carbon Observatory-2 (OCO2) SIF and FluxSat GPP. In each tropical continent, the coupling between SIF and VPD increases with the mean VPD. Even on the interannual timescale, the correlation of GPP with VPD is still discernable, but the sensitivity is smaller than the intra-annual correlation. By and large, the dynamic global vegetation models in the TRENDY v8 project do not capture the high GPP seasonal sensitivity to VPD in dry tropics. The complex interactions between carbon and water cycles in the tropics illustrated in this study and the poor representation of this coupling in the current suite of vegetation models suggest that projections of future changes in carbon dynamics based on these models may not be robust.

Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial- and time-varying patterns of ambient pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO2), maximum daily 8-h average ozone (MDA8 O-3), and fine particulate matter (PM2.5) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM2.5 levels exceeding limits due to heavy traffic activities and providing an "out-of-limit map" tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents' exposure to PM2.5. The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.

TurboID-based proximity labeling coupled to mass spectrometry (PL-MS) has emerged as a powerful tool for mapping protein-protein interactions in both plant and animal systems. Despite advances in sensitivity, PL-MS studies can still suffer from false negatives, especially when dealing with low abundance bait proteins and their transient interactors. Protein-level enrichment for biotinylated proteins is well developed and popular, but direct detection of biotinylated proteins by peptide-level enrichment and the difference in results between direct and indirect detection remain underexplored. To address this gap, we compared and improved enrichment and data analysis methods using TurboID fused to SPY, a low-abundance O-fucose transferase, using an AAL-enriched SPY target library for cross-referencing. Our results showed that MyOne and M280 streptavidin beads significantly outperformed antibody beads for peptide-level enrichment, with M280 performing best. In addition, while a biotin concentration ≤ 50 muM is recommended for protein-level enrichment in plants, higher biotin concentrations can be used for peptide-level enrichment, allowing us to improve detection and data quality. FragPipe's MSFragger protein identification and quantification software outperformed Maxquant and Protein Prospector for SPY interactome enrichment due to its superior detection of biotinylated peptides. Our improved washing protocols for protein-level enrichment mitigated bead collapse issues, improving data quality, and reducing experimental time. We found that the two enrichment methods provided complementary results and identified a total of 160 SPY-TurboID-enriched interactors, including 60 previously identified in the AAL-enriched SPY target list and 100 additional novel interactors. SILIA quantitative proteomics comparing WT and spy-4 mutants showed that SPY affects the protein levels of some of the identified interactors, such as nucleoporin proteins. We expect that our improvement will extend beyond TurboID to benefit other PL systems and hold promise for broader applications in biological research.

Gene expression data from isolated stele cells after roots were treated with 140 mM NaCl for 32 hour Data quality was examined using the signal distribution of Affymetrix built-in controls (Spike-in and hybridization controls) using Expression Console software (Affymetrix) and AffyQCReport. GCRMA in R/Bioconductor was used for data normalization.

Gene expression data from isolated stele cells Data quality was examined using the signal distribution of Affymetrix built-in controls (Spike-in and hybridization controls) using Expression Console software (Affymetrix) and AffyQCReport. GCRMA in R/Bioconductor was used for data normalization.

Gene expression data from isolated stele cells Data quality was examined using the signal distribution of Affymetrix built-in controls (Spike-in and hybridization controls) using Expression Console software (Affymetrix) and AffyQCReport. GCRMA in R/Bioconductor was used for data normalization.

Gene expression data from isolated stele cells Data quality was examined using the signal distribution of Affymetrix built-in controls (Spike-in and hybridization controls) using Expression Console software (Affymetrix) and AffyQCReport. GCRMA in R/Bioconductor was used for data normalization.

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.

Across continental Africa, more than 300 new hydropower projects are under consideration to meet the future energy demand that is expected based on the growing population and increasing energy access. Yet large uncertainties associated with hydroclimatic and socioeconomic changes challenge hydropower planning. In this work, we show that only 40 to 68% of the candidate hydropower capacity in Africa is economically attractive. By analyzing the African energy systems' development from 2020 to 2050 for different scenarios of energy demand, land-use change, and climate impacts on water availability, we find that wind and solar outcompete hydropower by 2030. An additional 1.8 to 4% increase in annual continental investment ensures reliability against future hydroclimatic variability. However, cooperation between countries is needed to overcome the divergent spatial distribution of investment costs and potential energy deficits.

Carbon-emitting technologies often cost less than carbon-emission-free alternatives; this difference in cost is known as the Green Premium. Innovations that decrease the Green Premium contribute to achieving climate goals, but a conceptual framework to quantify that contribution has been lacking. Here, we devise a framework to translate reductions in the Green Premium into equivalent reductions in carbon emissions. We introduce a new integrated assessment model designed for teaching and communication, the Climate Optimized INvestment model, to facilitate transparent investigation of cost-saving innovation. We look at consequences of introducing a new technology with potential for learning and improvement for scenarios with three levels of stringency of carbon constraint: an Unlimited budget scenario in which carbon emissions abatement is determined only by balancing marginal costs; a Large budget scenario with a maximum budget for future cumulative emissions equivalent to 50 times the initial-year emissions; and a Small budget scenario with a maximum budget for future cumulative emissions equivalent to 15 times the initial-year emissions. At all of these stringency levels, we find the least-cost solutions involve investing in a learning subsidy to bring the cost of the new technology down the learning curve. Reducing the Green Premium can lead to enhanced carbon abatement, lower abatement costs even after reaching net-zero emissions, less climate damage, and increased net-present-value of consumption. We find both the value of Green Premium reductions and the value of carbon dioxide removal are greater under more stringent mitigation targets. Our study suggests a crucial role for both public and private sectors in promoting and developing innovations that can contribute to achieving zero emissions goals.