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Structural transition in rare earth doped zirconium oxide: A positron annihilation study
Graphical abstract: New microstructural analysis and phase transition of rare earth doped mixed oxide compounds such as: Sm{sub 2−x}Dy{sub x}Zr{sub 2}O{sub 7} (where x = 0.0 ≤ x ≥ 2.0) that are potentially useful as solid oxide fuels, ionic conductors, optoelectronic materials and most importantly as radiation resistant host for high level rad-waste disposal, structural transition in the system is reported through positron annihilation spectroscopy as there is an indication in the X-ray diffraction analysis. Highlights: ► Zirconium oxide material doped with rare earth ions. ► The method of positron annihilation spectroscopy suggests a phase transition in the system. ► The crystal structure transformation from pure pyrochlore to defect fluorite type of structure is shown by X-ray diffraction results. -- Abstract: A series of compounds with the general composition Sm{sub 2−x}Dy{sub x}Zr{sub 2}O{sub 7} (where 0 ≤ x ≥ 2.0) were synthesized by chemical route and characterized by powder X-ray diffraction (XRD) analysis. The rare earth ion namely Sm{sup +3} in the compound was gradually replaced with another smaller and heavier ion, Dy{sup +3} of the 4f series, there by resulting in order–disorder structural transition, which has been studied by positron annihilation lifetime and Doppler broadening spectroscopy. This study reveals the subtle electronic micro environmental changes in the pyrochlore lattice (prevalent due to the oxygen vacancy in anti-site defect structure of the compound) toward its transformation to defect fluorite structure as found in Dy{sub 2}Zr{sub 2}O{sub 7}. A comparison of the changes perceived with PAS as compared to XRD analysis is critically assayed.
On improving the performance of nonphotochemical quenching in CP29 light-harvesting antenna complex
In this study, we model and simulate the performance of charge-transfer in nonphotochemical quenching (NPQ) in the CP29 light-harvesting antenna-complex associated with photosystem II (PSII). The model consists of five discrete excitonic energy states and two sinks, responsible for the potentially damaging processes and charge-transfer channels, respectively. We demonstrate that by varying (i) the parameters of the chlorophyll-based dimer, (ii) the resonant properties of the protein-solvent environment interaction, and (iii) the energy transfer rates to the sinks, one can significantly improve the performance of the NPQ. In conclusion, our analysis suggests strategies for improving the performance of the NPQ in response to environmental changes, and may stimulate experimental verification.
deadtrees.earth — An open-access and interactive database for centimeter-scale aerial imagery to uncover global tree mortality dynamics
Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, and may not be standardized or spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map overstory tree mortality in a consistent manner over space and time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Low altitude observation platforms (e.g., drones or airplanes) provide a cost-effective source of training data by capturing high-resolution orthophotos of overstory tree mortality events at centimeter-scale resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than two thousand centimeter-resolution orthophotos, covering more than 1,000,000 ha, of which more than 58,000 ha are manually annotated with live/dead tree classifications. This community-sourced and rigorously curated dataset can serve as a comprehensive reference dataset to uncover tree mortality patterns from local to global scales using space-based Earth observation data and machine learning models. This will provide the basis to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. The open nature of deadtrees.earth, together with its curation of high-quality, spatially representative, and ecologically diverse data will continuously increase our capacity to uncover and understand tree mortality dynamics.
From pixels to patterns: Coupling Optical Coherence Tomography and machine learning for monitoring coastal wetland root systems
Coastal wetlands are crucial in shoreline stabilization, carbon sequestration, and storm protection. Yet, due to limitations in traditional destructive sampling techniques, the belowground biomass (live root mass) and necromass (dead and decaying roots) remain difficult to assess in coastal wetlands, limiting our understanding on coastal resilience, nutrient cycling, and soil structure. This study employs Optical Coherence Tomography (OCT) as a high-resolution imaging technique to analyze root biomass and necromass in the Terrebonne Basin, Louisiana. A Random Forest (RF) model was developed to classify root health states based on OCT-derived features, achieving an accuracy of 70% in distinguishing live from dead root segments. The results demonstrate that OCT, combined with ML, offers a promising novel approach to root analysis, providing fine-scale insights into root morphology and decay patterns that are not easily captured by conventional methods. This research lays the foundation for future integration of OCT with complementary imaging modalities such as X-ray Computed Tomography (XCT) and advanced ML algorithms to enhance classification accuracy and scalability. Future work aims to expand the dataset diversity across different wetland types and apply the methodology for large-scale, repeatable assessments of root biomass turnover and accumulation, with important implications for wetland monitoring, conservation, and restoration under changing environmental conditions.
Orange carotenoid proteins: structural understanding of evolution and function
Cyanobacteria uniquely contain a primitive water-soluble carotenoprotein, the orange carotenoid protein (OCP). Nearly all extant cyanobacterial genomes contain genes for the OCP or its homologs, implying an evolutionary constraint for cyanobacteria to conserve its function. Genes encoding the OCP and its two constituent structural domains, the N-terminal domain, helical carotenoid proteins (HCPs), and its C-terminal domain, are found in the most basal lineages of extant cyanobacteria. These three carotenoproteins exemplify the importance of the protein for carotenoid properties, including protein dynamics, in response to environmental changes in facilitating a photoresponse and energy quenching. Furthermore, we review new structural insights for these carotenoproteins and situate the role of the protein in what is currently understood about their functions.
Coarse-grained resource allocation modeling for decoding and rewiring microbial metabolism
Microbial metabolism is a complex, emergent system driven by the coordinated interplay of intricate and dynamic molecular processes. To elucidate cellular behavior and enable biotechnological applications, quantitative models that address the inherent complexity of metabolism have been developed from a resource allocation perspective. Here, we synthesize recent advances in coarse-grained resource allocation frameworks and their applications in understanding microbial physiology and guiding gene circuit design. Here, these frameworks reveal global regulatory constraints and predict cellular adaptation to nutrient and environmental changes. In addition, they enable the quantification of metabolic costs, the dissection of circuit–host interactions, and the development of strategies for burden mitigation. Collectively, these modeling frameworks provide a powerful platform for uncovering quantitative principles of microbial growth and engineering robust synthetic biological systems.
Carbon/Nitrogen Metabolic Balance: Lessons from Cyanobacteria
Carbon and nitrogen are the two most abundant nutrient elements for all living organisms, and their metabolism is tightly coupled. What are the signaling mechanisms that cells use to sense and control the carbon/nitrogen (C/N) metabolic balance following environmental changes? Based on studies in cyanobacteria, it was found that 2-phosphoglycolate derived from the oxygenase activity of Rubisco (ribulose-1,5-bisphosphate carboxylase/oxygenase) and 2-oxoglutarate from the Krebs cycle act as the carbon- and nitrogen-starvation signals, respectively, and their concentration ratio likely reflects the status of the C/N metabolic balance. We will present and discuss the regulatory principles underlying the signaling mechanisms, which are likely to be conserved in other photosynthetic organisms. These concepts may also contribute to developments in the field of biofuel engineering or improvements in crop productivity.
Distinct mechanisms enable inward or outward budding from late endosomes/multivesicular bodies
Highlights: • We reconstituted inward and outward budding events from late endosome/MVB membranes. • Inward and outward budding events from MVBs require cytosolic proteins. • Inward and outward budding events from MVBs are mechanistically distinct. • Dynamin is necessary for efficient outward budding. Regulating the residence time of membrane proteins on the cell surface can modify their response to extracellular cues and allow for cellular adaptation in response to changing environmental conditions. The fate of membrane proteins that are internalized from the plasma membrane and arrive at the limiting membrane of the late endosome/multivesicular body (MVB) is dictated by whether they remain on the limiting membrane, bud into internal MVB vesicles, or bud outwardly from the membrane. The molecular details underlying the disposition of membrane proteins that transit this pathway and the mechanisms regulating these trafficking events are unclear. We established a cell-free system that reconstitutes budding of membrane protein cargo into internal MVB vesicles and onto vesicles that bud outwardly from the MVB membrane. Both budding reactions are cytosol-dependent and supported by Saccharomyces cerevisiae (yeast) cytosol. We observed that inward and outward budding from the MVB membrane are mechanistically distinct but may be linked, such that inhibition of inward budding triggers a re-routing of cargo from inward to outward budding vesicles, without affecting the number of vesicles that bud outwardly from MVBs.
Acetylation by Eis and Deacetylation by Rv1151c of Mycobacterium tuberculosis HupB: Biochemical and Structural Insight
Bacterial nucleoid-associated proteins (NAPs) are critical to genome integrity and chromosome maintenance. Post-translational modifications of bacterial NAPs appear to function similarly to their better studied mammalian counterparts. The histone-like NAP HupB from Mycobacterium tuberculosis (Mtb) was previously observed to be acetylated by the acetyltransferase Eis, leading to genome reorganization. We report biochemical and structural aspects of acetylation of HupB by Eis. We also found that the SirT-family NAD + -dependent deacetylase Rv1151c from Mtb deacetylated HupB in vitro and characterized the deacetylation kinetics. Here, we propose that activities of Eis and Rv1151c could regulate the acetylation status of HupB to remodel the mycobacterial chromosome in response to environmental changes.
Effect of metal ion intercalation on the structure of MXene and water dynamics on its internal surfaces
MXenes are a recently discovered class of 2D materials with an excellent potential for energy storage applications. Because MXene surfaces are hydrophilic and attractive interaction forces between the layers are relatively weak, water molecules can spontaneously intercalate at ambient humidity and significantly influence the key properties of this 2D material. Using complementary X-ray and neutron scattering techniques, we demonstrate that intercalation with potassium cations significantly improves structural homogeneity and water stability in MXenes. Furthermore, in agreement with molecular dynamics simulations, intercalated potassium ions reduce the water self-diffusion coefficient by 2 orders of magnitude, suggesting greater stability of hydrated MXene against changing environmental conditions.
Characterization of Sequentially Extracted Soil Organic Matter by Electrospray Ionization and Atmospheric Pressure Photoionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry
Soil organic matter (SOM) is a complex mixture of small molecules and biopolymers that are active in various biogeochemical processes. However, the chemical diversity of biopolymer-derived SOM remains poorly explored. Identifying this diversity is important because global environmental changes may well alter SOM chemistry, as field experiments are beginning to show. Here, organic solvent-extractable (DcMe-SOM), base-hydrolyzable (KOH Hy -SOM), and CuO-oxidizable (CuO Ox -SOM) SOM fractions from a forest with a long-term nitrogen addition experiment were sequentially extracted and characterized by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) coupled with negative-ion electrospray ionization (ESI) or atmospheric pressure photoionization (APPI). From DcMe-SOM to CuO Ox -SOM, the total number of assigned formulas, average O/C ratio, aromaticity, and unsaturation degree of SOM continuously increased, while the average m/z and H/C ratio decreased. Moreover, the dominant chemical category shifted from lipid-like components to phytochemical- and protein-like components. Complementary to ESI, APPI effectively facilitated detection of additional compounds with low polarity. With long-term nitrogen addition, the average m/z, unsaturation degree, aromaticity, and oxidation state of SOM increased, and more aromatic nitrogen-containing formulas were detected in CuOOx-SOM. Our study demonstrates that chronic nitrogen deposition in forests alters both the small molecules and biopolymers of SOM fundamentally.
Ecosystem carbon storage capacity as affected by disturbance regimes: A general theoretical model
Disturbances have been recognized as a key factor shaping terrestrial ecosystem states and dynamics. A general model that quantitatively describes the relationship between carbon storage and disturbance regime is critical for better understanding large scale terrestrial ecosystem carbon dynamics. We developed a model (REGIME) to quantify ecosystem carbon storage capacities (E[x]) under varying disturbance regimes with an analytical solution E[x] = U {center_dot} {tau}{sub E} {center_dot} {lambda}{lambda} + s {tau} 1, where U is ecosystem carbon influx, {tau}{sub E} is ecosystem carbon residence time, and {tau}{sub 1} is the residence time of the carbon pool affected by disturbances (biomass pool in this study). The disturbance regime is characterized by the mean disturbance interval ({lambda}) and the mean disturbance severity (s). It is a Michaelis-Menten-type equation illustrating the saturation of carbon content with mean disturbance interval. This model analytically integrates the deterministic ecosystem carbon processes with stochastic disturbance events to reveal a general pattern of terrestrial carbon dynamics at large scales. The model allows us to get a sense of the sensitivity of ecosystems to future environmental changes just by a few calculations. According to the REGIME model, for example, approximately 1.8 Pg C will be lost in the high-latitude regions of North America (>45{sup o} N) if fire disturbance intensity increases around 5.7 time the current intensity to the end of the twenty-first century, which will require around 12% increases in net primary productivity (NPP) to maintain stable carbon stocks. If the residence time decreased 10% at the same time additional 12.5% increases in NPP are required to keep current C stocks. The REGIME model also lays the foundation for analytically modeling the interactions between deterministic biogeochemical processes and stochastic disturbance events.
An Integrative Model for Soil Biogeochemistry and Methane Processes: I. Model Structure and Sensitivity Analysis
Abstract Environmental changes are anticipated to generate substantial impacts on carbon cycling in peatlands, affecting terrestrial‐climate feedbacks. Understanding how peatland methane (CH 4 ) fluxes respond to these changing environments is critical for predicting the magnitude of feedbacks from peatlands to global climate change. To improve predictions of CH 4 fluxes in response to changes such as elevated atmospheric CO 2 concentrations and warming, it is essential for Earth system models to include increased realism to simulate CH 4 processes in a more mechanistic way. To address this need, we incorporated a new microbial‐functional group‐based CH 4 module into the Energy Exascale Earth System land model (ELM) and tested it with multiple observational data sets at an ombrotrophic peatland bog in northern Minnesota. The model is able to simulate observed land surface CH 4 fluxes and fundamental mechanisms contributing to these throughout the soil profile. The model reproduced the observed vertical distributions of dissolved organic carbon and acetate concentrations. The seasonality of acetoclastic and hydrogenotrophic methanogenesis—two key processes for CH 4 production—and CH 4 concentration along the soil profile were accurately simulated. Meanwhile, the model estimated that plant‐mediated transport, diffusion, and ebullition contributed to ∼23.5%, 15.0%, and 61.5% of CH 4 transport, respectively. A parameter sensitivity analysis showed that CH 4 substrate and CH 4 production were the most critical mechanisms regulating temporal patterns of surface CH 4 fluxes both under ambient conditions and warming treatments. This knowledge will be used to improve Earth system model predictions of these high‐carbon ecosystems from plot to regional scales.
Achievements and Prospects of Global Broadband Seismographic Networks After 30 Years of Continuous Geophysical Observations
Global seismographic networks (GSNs) emerged during the late nineteenth and early twentieth centuries, facilitated by seminal international developments in theory, technology, instrumentation, and data exchange. The mid- to late-twentieth century saw the creation of the World-Wide Standardized Seismographic Network (1961) and International Deployment of Accelerometers (1976), which advanced global geographic coverage as seismometer bandwidth increased greatly allowing for the recording of the Earth's principal seismic spectrum. The modern era of global observations and rapid data access began during the 1980s, and notably included the inception of the GEOSCOPE initiative (1982) and GSN (1988). Through continual improvements, GEOSCOPE and the GSN have realized near-real time recording of ground motion with state-of-art data quality, dynamic range, and timing precision to encompass 180 seismic stations, many in very remote locations. Data from GSNs are increasingly integrated with other geophysical data (e.g., space geodesy, infrasound and Interferometric Synthetic Aperture Radar). Globally distributed seismic data are critical to resolving crust, mantle, and core structure; illuminating features of the plate tectonic and mantle convection system; rapid characterization of earthquakes; identification of potential tsunamis; global nuclear test verification; and provide sensitive proxies for environmental changes. As the global geosciences community continues to advance our understanding of Earth structure and processes controlling elastic wave propagation, GSN infrastructure offers a springboard to realize increasingly multi-instrument geophysical observatories. Here, we review the historical, scientific, and monitoring heritage of GSNs, summarize key discoveries, and discuss future associated opportunities for Earth Science.
A Differentiable Hydrology Approach for Modeling With Time-Varying Parameters
In the analysis of observational data with mathematical models, it is often desirable to let one or more model parameters vary with time to account for changing environmental conditions or to offer more flexibility. A standard approach for dynamic models is to allow for the number of parameters to grow as a function of time, leading to a high-dimensional inference problem for long data records. We propose using Hamiltonian Monte Carlo, a gradient-based Markov chain Monte Carlo (MCMC) method, to perform Bayesian inference for time-varying parameters (TVP) in hydrology models. As derivatives of model error functions with regard to parameters are not available in closed form, we implemented the GR4J rainfall-runoff models in Theano/PyMC3, and Jax/NumPyro, allowing for the application of automatic differentiation techniques. A simulation experiment assessing the viability of these methods for recovering underlying temporal variation in parameters indicates that a discrete Gaussian random walk-based prior appears to be best suited across a range of scenarios while the continuous-time Gaussian process compares relatively poorly. In an analysis of 20 years of daily real-world streamflow records from the Model Parameter Estimation Experiment (MOPEX) hydrology data set with increasing data sparsity and at multiple temporal resolutions, we find that TVP inference with gradient-based MCMC is a flexible and reliable approach for analyzing hydrology models with dynamic parameter sets.
ELM‐MOSART‐DOC: A Large‐Scale Riverine Dissolved Organic Carbon Model and Its Application Over the United States
Riverine dissolved organic carbon (DOC), primarily sourced from soil organic carbon (SOC), plays a crucial role in regional and global carbon cycles. However, the complexities of the underlying mechanisms and limited observations present significant challenges for predictive understanding of DOC at regional or larger scales. Recently, we developed a machine learning‐based (ML) map of DOC transformation rates, bridging the gap between SOC and DOC leaching flux and simplifying terrestrial DOC representation. Building on this advancement, we introduce ELM‐MOSART‐DOC, a DOC module integrated into the riverine component of the Energy Exascale Earth System Model (E3SM)—the Model for Scale Adaptive River Transport (MOSART). ELM‐MOSART‐DOC simulates DOC transport and transformation across both headwater streams and river networks, including those managed. Model validation demonstrates the ability of ELM‐MOSART‐DOC to accurately capture long‐term average DOC concentrations, with Kling‐Gupta Efficiency (KGE) scores of 0.58 and 0.76 at large and local stations, respectively. We further assess the impact of reservoirs through different simulation schemes, revealing that reservoirs significantly alter DOC fluxes by regulating streamflow patterns and promoting DOC mineralization. Model simulations indicate that reservoirs reduce total DOC flux from the Mississippi River into the ocean by 7.5%, with the long‐term average annual export decreasing from 3.34 to 3.14 teragrams (Tg) per year. ELM‐MOSART‐DOC integrates process‐based modeling with ML parameterization to enhance the predictive understanding of riverine biogeochemical processes. This approach reduces uncertainties in modeling regional and global carbon cycle ESMs and provides new insights into carbon cycling and its implications for global environmental change.
Indirect and suboptimal control of gene expression is widespread in bacteria
Gene regulation in bacteria is usually described as an adaptive response to an environmental change so that genes are expressed when they are required. We instead propose that most genes are under indirect control: their expression responds to signal(s) that are not directly related to the genes’ function. Indirect control should perform poorly in artificial conditions, and we show that gene regulation is often maladaptive in the laboratory. In Shewanella oneidensis MR-1, 24% of genes are detrimental to fitness in some conditions, and detrimental genes tend to be highly expressed instead of being repressed when not needed. In diverse bacteria, there is little correlation between when genes are important for optimal growth or fitness and when those genes are upregulated. Two common types of indirect control are constitutive expression and regulation by growth rate; these occur for genes with diverse functions and often seem to be suboptimal. Because genes that have closely related functions can have dissimilar expression patterns, regulation may be suboptimal in the wild as well as in the laboratory.