Developing drought-resilient crops requires a precise understanding of molecular signalling in the root, the primary organ encountering drought. This study unravelled novel genetic loci regulating drought tolerance by exploiting the natural variation in seedling root growth of Arabidopsis thaliana under PEG-induced drought stress. Through a genome-wide association study of 207 worldwide A. thaliana ecotypes from regions with varied rainfall, 68 protein-coding genes were identified with the top 50 SNPs. Functional enrichment and network analyses demarcated key processes involved in stress tolerance, including DNA repair, tRNA editing, protein folding, cell cycle regulation, stress granule assembly and the pyridoxal 5'-phosphate (PLP) salvage pathway. Expression level polymorphisms, promoter cis-element variations and amino acid substitutions associated with phenotype and climate were identified. Reverse genetic evaluation using T-DNA insertion knockout/knockdown mutants confirmed the involvement of candidate genes: AT1G06690 (PLP pathway), AT4G26990, RBP45C (stress granules), ACD55.5 (protein folding), PCMP-A4 (AT1G14470; RNA editing), SKS6, ANAC094 (cell wall remodelling) and INCENP (cell cycle), with seedling drought tolerance. Specifically, knockdown of AT1G06690 resulted in higher root hydrogen peroxide accumulation, highlighting the importance of the PLP pathway in mitigating oxidative stress. These molecular insights offer new biotechnological and breeding tools to enhance crop drought tolerance by modulating root traits.

Cooperative emission of indistinguishable photons from multiple distant sources can enable quantum information processing, and low-density semiconductor quantum dots (QDs) embedded in metasurfaces hold promise to scale up this functionality. However, the inhomogeneity in size within QD ensembles and limited interresonator coupling in local metasurfaces make this effect highly unlikely. Here, we demonstrate a nonlocal metasurface platform with embedded GaAs QDs coupled to extended photonic modes with emission wavelength tunability and enhanced free-space emission outcoupling. Natural variation in the QD dipole moment allows us to tune two QDs into spectral alignment and resonance with selected modes. As a result, two distant QDs can produce same-wavelength photons with strongly improved outcoupling efficiency to free space. The nonlocal and periodic nature of the developed metasurface eliminates the need for precise placement of individual QDs and, although cooperative emission was not yet demonstrated, this metasurface platform opens doors for investigations of cooperative effects for quantum information system applications.

Fetal brain magnetic resonance imaging (MRI) has been recognized as a vital diagnostic tool for identifying neurological anomalies during pregnancy. Accurate classification of fetal MRI planes is essential for effective prenatal neurological assessment, yet this task remains challenging in clinical practice. Key obstacles include the reliance on manual identification by specialized neuroradiologists, resource-constraints, motion-induced artifacts from fetal movement, and insufficient clinical interpretability of automated methods. This study presents FetCAT (Fetal Cross-Attention Transformer), a novel hybrid architecture that integrates a pre-trained Swin Transformer with a custom AdaptiveMed-CNN model through cross-attention fusion mechanisms for automated fetal brain MRI plane classification. The proposed hybrid architecture combines the global contextual understanding capabilities of transformers with the local feature extraction strengths of CNN through a sophisticated cross-attention mechanism. The model was trained and tested with a large-scale dataset of 52,561 motion-degraded fetal MRI slices from 741 patients, encompassing three anatomical planes and a gestational age of 19-39 weeks. Comprehensive comparative analyses were conducted across pre-trained CNN architectures, baseline and pre-trained transformer models, and the proposed hybrid configurations to evaluate the efficacy. Systematic ablation studies were performed to evaluate the impact of domain-specific data augmentation strategies on model performance. Robust statistical evaluation, including mean, variance, confidence intervals, and McNemar's test, substantiated the significant performance advantage of the proposed architecture over all competing models. Additionally, Grad-CAM-based explainability analysis was implemented to provide visual interpretations of the model's decision-making process, thereby enhancing clinical interpretability. The proposed cross-attention based Swin-AdaptiveMedCNN model achieved superior performance with 98.64% accuracy without data augmentation, substantially outperforming standalone CNN models, baseline and pre-trained transformers. Explainability analysis using Grad-CAM visualization demonstrated that the model focuses on clinically relevant anatomical landmarks. Contrary to common assumptions, ablation studies revealed that data augmentation consistently reduced model performance rather than improving it. This result can be attributed to the inherent diversity and natural variability already present in the dataset, which rendered additional synthetic variations counterproductive. Moreover, the proposed FetCAT model also demonstrated strong generalization capability, maintaining superior and statistically significant performance on an unseen OpenNeuro MRI test dataset with 81.0% accuracy. Thus, this study establishes a benchmark for automated fetal brain MRI plane classification.

Drought is a major abiotic stress that affects the growth and yield of maize. Alternative transcripts are crucial in abiotic stress responses in plants. However, the genetic basis of alternative transcripts mediated drought response in maize remains largely unknown. We characterize thousands of drought-responsive genes based on the transcriptomic dataset of 197 maize association population under well-watered and drought-stressed conditions. We perform mRNA profiling of the seedlings at six-leaf stage under drought stress. Through co-expression analysis and experimental validation, we identify a splicing associated factor ZmMBF1, which positively regulates drought response in maize. We also detect thousands of alternative transcript QTLs (atQTLs) and expression QTLs (eQTLs), some of them are linked with stress responsive genes under well-watered and drought-stressed conditions, respectively. Co-localization analysis demonstrates that most of the natural variations in alternative transcripts and gene expression levels are regulated independently by different sequence variations. Variations in transposons, inverted repeats, and UA-rich sequences are significantly associated with atQTLs, suggesting important roles of these variations in regulating alternative transcripts and drought response. As proof of concept, we demonstrate that variations in UA-rich sequence of ZmPYL8 intron regulate drought resistance by affecting ZmPYL8 alternative transcripts, generating two transcripts that function antagonistically in regulating ABA signaling and drought response. This study reveals the response of maize alternative transcripts to drought at the population level, illustrating the pivotal roles of intron variations in regulating maize alternative transcripts and drought response. It also provides genetic resources and theoretical basis for breeding maize with drought-resistance.

Krill is a central organism in the food web of many marine ecosystems and eastern boundary current upwelling regions specifically. Here, a superensemble of climate and ecological models is used to determine drivers of future change, variability, and uncertainty in krill abundance for the California Current. While krill is projected to slowly decrease throughout the 21st century, the long-term trend consistently exceeds natural variability only under extreme warming. Similarly, unprecedented low krill years are expected to progressively increase, but their frequency of occurrence will depend on background abundances tied to low-frequency climate variability. The relative contributions of warming rate and ecological model formulation to projected uncertainty are comparable and reflect latitudinal changes in the magnitude of climate forcing and availability of empirical data to parameterize krill models. This finding highlights the fact that uncertainty in climate change impacts on coastal upwelling ecosystems may depend as strongly on model formulation as they do on anthropogenic forcing. Furthermore, the increasingly divergent krill model responses outside of the core domain for which they were originally implemented advocate for regionally tailored projections and models to reduce overall uncertainty. By identifying and quantifying uncertainty sources in future krill abundance across relevant time scales, the present study lays the foundation for understanding how the superposition of long-term trends, low-frequency variability, and extreme events may lead to unprecedented ecosystem states, and for assessing their broader impacts on altered presence, distribution, and recovery of species that directly or indirectly depend on krill.

Abstract. Atmospheric concentration of methane (CH4), a potent greenhouse gas, increased significantly since pre-industrial times, with anthropogenic emissions originating primarily from agriculture, fossil fuel sector and waste management. However, considerable uncertainties persist in the detection and quantification of anthropogenic CH4 emissions. In this study, we present first CH4 observations, plume detections and emission estimates from the new state-of-the-art Airborne Visible InfraRed Imaging Spectrometer 4 (AVIRIS-4), which participated in a blind controlled release experiment in September 2024 in southern France. We used an albedo-corrected matched filter to retrieve CH4 maps from the spectral images and estimated CH4 emission with the Integrated Mass Enhancement (IME) and Cross-Sectional Flux (CSF) methods. Our results demonstrate that AVIRIS-4 can reliably detect emissions as low as 5.5 kg CH4 h−1 under good weather conditions at low flight altitudes (< 1500 m) and 1.45 kg CH4 h−1 under ideal conditions. These low-altitude detection limits are substantially lower than published detection limits for the predecessor instrument AVIRIS-NG, which were in the order of 10–16 kg CH4 h−1 under comparable conditions. While AVIRIS-4 provides highly accurate CH4 maps at < 0.5 m resolution, emission estimation is limited by the accuracy of the effective wind speed, whose uncertainty and natural variability contribute substantially to the overall uncertainty. Using wind speed at source height performs well for small releases (below 20 kg CH4 h−1) (rRMSE =1.065; rMBE =0.361) and overall (rRMSE =0.702; rMBE =−0.204). Using literature-derived effective wind speeds improves the apparent fit between estimated and reported CH4 emissions, but degrades performance both in overall agreement (rRMSE =2.098; rMBE =0.964) and for low-emission events (rRMSE =2.367; rMBE =1.711). Interestingly, the high spatial resolution makes it possible to retrieve the cast shadow of the CH4 plume, which can be used to estimate source and plume height, and could provide an approach for better constraining the height-dependency of the effective wind speed. On the bottom line, the controlled release experiment provides critical insights into the sensor's capabilities and guides further improvements to detect and quantify low intensity sources in the fossil fuel and waste management sectors, with implications for more accurate global greenhouse gas monitoring.

Root lodging is a major problem limiting crop yield and seed quality. Here, we showed that Root Lodging Resistance 1 (ZmRLR1) is specifically expressed in maize roots. The overexpression of ZmRLR1 increased the rhizosphere soil weight, stem pulling force of the internode 50cm from the ground, total root length, and root volume. In contrast, the rhizosphere soil weight, stem pulling force of the internode 50cm from the ground, total root length, and root volume were reduced in the Zmrlr1ems and Zmrlr1 mutants. Natural variations in the promoter of ZmRLR1 are significantly associated with the root lodging resistance of maize. ZmRLR1 positively regulates the ascorbate (AsA) content in maize roots. We further demonstrated that ZmRLR1 improved the interaction between the ZmAP2 σ subunit and ZmCHC2. The internalization of the endocytic tracer FM4-64 is substantially reduced in the Zmrlr1ems and Zmrlr1 mutants, while auxin distribution and ZmPIN1a-YFP localization are altered in Zmrlr1ems. The defect in the total root length of Zmrlr1ems is rescued by the exogenous application of 10µmol L-1 AsA plus 0.01µmol L-1 1-naphthaleneacetic acid. Taken together, these results suggest that ZmRLR1 affects the root lodging resistance of maize by regulating root AsA and auxin homeostasis.

Abstract Binary supermassive black holes (SMBHs) are consequences of galaxy mergers and dominate the low-frequency gravitational-wave background. Finding binary SMBHs in existing time-domain observations has proven difficult, as their periodic, electromagnetic signals can be confused with the natural variability of single quasars. In this work, we investigate the effects of host-galaxy contamination and survey design (cadence and duration) on the detectability of binary SMBHs with the upcoming Rubin Observatory Legacy Survey of Space and Time (LSST). We simulate millions of LSST light curves of single and binary quasars, with a distribution of quasar and host-galaxy properties motivated by empirical observations and the anticipated LSST detection space. We then apply simple sinusoidal curve fits as a potential computationally inexpensive detection method. We find that host-galaxy contamination will increase false-positive rates and decrease binary parameter recovery rates. Lower-mass, lower-luminosity binary systems are most likely to be negatively affected by host-galaxy contamination. We also find that monitoring duration affects binary detection more than survey effective cadence for this detection method. As the light-curve duration increases, false-positive rates are suppressed and binary parameter recovery rates, especially for binary periods, are improved. Increasing the light-curve duration from 5 to 10 yr shows the most dramatic improvement for successful binary detection and false-positive rejection, with additional improvement from extending the light-curve duration to 20 yr. The observation duration increase is especially critical for recovering binary periods that are longer than a decade.

Natural variability in stable isotope ratios provides critical constraints on elemental cycling in nature without the need for the introduction of artificial tracers. While such data are widely used in environmental studies, they are not as widely employed in biomedical research, despite vast potential. One critical hurdle to the adoption of such techniques in biomedical studies is sample throughput. Elemental purification via ion-exchange chromatography and isotopic analysis via multiple collector inductively coupled plasma mass spectrometry (MC-ICP-MS) are time-consuming, requiring long hours from experienced researchers to generate datasets. Here we present new methods to improve the throughput of both elemental purification and sample introduction to mass spectrometers. We use an automated, low-pressure ion exchange chromatography system to isolate purified fractions of potassium, magnesium, and calcium from one sample in a single sequence with high yields (80-100%) and low blanks (<0.5% carryover). Modification of flow rates and column volumes also enables recovery of purified strontium, lithium, and sodium in the same routine. Solutions are introduced to the MC-ICP-MS via syringe injection and with automated removal of vial caps to minimize evaporation. We find that syringe injection from capped vials gives a >10 × more stable signal (0.7% RSD) over a 9-h sequence than self-aspirated, uncapped solutions (8.0% RSD). Syringe injection also enables modification of signal intensity by changing the injection rate, with a linear response of signal to flow rate. We demonstrate the potential of these methods by analyzing calcium, magnesium, and potassium isotope ratios at high precision (<0.1 ‰) from single 0.5mL aliquots of urine samples from individuals with chronic kidney disease. These data show a change in calcium reabsorption, highlighting avenues for further research as well as the value of these multi-isotopic analysis methods.

Abstract Internal modes of climate variability, such as El Niño and the North Atlantic Oscillation (NAO), can have strong influences upon distant weather patterns, effects that are referred to as “teleconnections.” The extent to which anthropogenic climate change has and will continue to affect these teleconnections, however, remains uncertain. Here, we employ a covariance fingerprinting approach to demonstrate that shifts in teleconnection patterns affecting monthly temperatures between the periods 1960–1990 and 1990–2020 are attributable to anthropogenic forcing. We further apply multilinear regression to assess the regional contributions and statistical significance of changes in five key climate modes: the El Niño‐Southern Oscillation, NAO, Southern Annular Mode, Indian Ocean Dipole, and the Pacific Decadal Oscillation. In many regions, observed changes exceed what would be expected from natural variability alone, further implicating an anthropogenic influence. Finally, we provide projections of how these teleconnections will alter in response to further changes in climate.

MECP2 duplication syndrome (MDS) is an ultrarare, X-linked neurodevelopmental disorder that is poorly understood in terms of its natural history and phenotypic variability. There is limited information on how individuals with MDS acquire, retain or lose fundamental functional skills (gross motor, purposeful hand function and communication) - that of which this study aimed to better characterise in the largest case series to date.For 160 individuals with MDS (median age 9.06 y, range: 0.57-51.63 y; 84% male), we report that phenotypic penetrance in females can, in some, result in a similar functional skill deficits to males. However, a higher proportion of females acquired gross motor and fine motor skills compared to males. Use of words was the most common parent-reported skill regression (34/90 [38%]) followed by fine motor/hand function (26/90 [29%]), independent walking (25/90 [28%]) and feeding (25/90 [28%]). Additionally, lower proportions of functional ability were present in those with seizures compared to those without. A general trend was also observed for decreasing functional skills with increasing age. Additionally, those with a larger duplication length (1 + Mb) were less likely to be able to acquire independent walking compared with those with less than a 1 + Mb duplication (p < 0.001).This is the first study to comprehensively map the developmental trajectory of functional skills in MDS and provides a seminal baseline for better characterising the natural history of this disorder. Further investigations are required to understand the importance of interventional therapy on the retainment of functional skills.

A recent study by R. Cheng, T. Lv, P. Ji, B. Ma, et al. (Appl Environ Microbiol 91:e01685-25, 2025, https://doi.org/10.1128/aem.01685-25) used multi-omics analysis to reveal the molecular map of pathogen virulence differentiation driven by natural variation. Building on this work, this article examines how natural variation shapes pathogen virulence and disease prevalence and explores the use of multi-omics approaches to uncover associated molecular mechanisms. The opportunities and challenges of applying multi-omics technologies in plant disease management are also discussed in this article.

ABSTRACT This study aimed to demonstrate the efficacy of the ordinary kriging interpolation method as a data augmentation technique to enhance the accuracy of models that predict the water quality parameter total dissolved solids (TDS). For this purpose, the total dissolved solids parameter was utilized as a case study. The models employed seven spectral indices derived from Sentinel-2 images as predictor variables, recognized as effective in predicting TDS: Green, Ferdous, SI-3, SI-5, TDS-1, and TDS-5. Three types of models were compared: (1) simple linear regression models trained with the original field samples; (2) simple linear regression models trained with an augmented dataset of 1000 data points, generated from a geostatistical kriging surface derived from the in-situ data; and (3) multilayer neural network models trained with the same augmented dataset of 1000 data points. It was concluded that the ordinary kriging method is an effective data augmentation technique, as there is a statistically significant difference between the models trained with field samples and those trained with augmented data, with the latter demonstrating greater explanatory power. Additionally, in the validation of the prediction surfaces derived from the models trained with the augmented dataset, it was observed that the mean absolute error (MAE) and the root mean square error (RMSE) were below the natural variability of the original in-situ data, thereby confirming the high predictive capacity of the models trained with the augmented dataset. This research is of significant importance, as it innovatively integrates geostatistical techniques, satellite remote sensing, and artificial intelligence to effectively address the critical issue of data scarcity in water quality modelling, taking TDS as a test parameter. The results of this research establish a robust foundation for the application of artificial intelligence in water quality modelling, facilitating precise predictions at specific locations and times.

The global decline of honeybee colonies represents a major ecological concern, primarily attributed to simultaneous exposure to multiple stressors. These include biotic pressures, such as parasitic infections, and abiotic pressures, such as exposure to ionizing radiation, which remains poorly understood. Assessing their combined effects provides novel insights into how biological and radiological stressors interact within the organism. Here, we investigated the individual and combined effects of Vairimorpha ceranae (formerly Nosema ceranae) infection and chronic gamma irradiation (14 µGy/h or 14 × 10³ µGy/h) on honeybee health. Measurements included survival, syrup consumption, spore load, and biomarkers related to energy metabolism, antioxidant defenses, immunity, detoxification, and neural enzyme activity. Two successive experiments, conducted at different collection periods, allowed us to account for biological variability between bee cohorts. Infection by V. ceranae caused high mortality and major impairments in metabolic, antioxidant, and immune functions. Ionizing radiation induced more moderate effects, characterized by redox imbalance and reduced detoxification capacity, which varied with dose rate. Under combined exposure, the two stressors produced mainly antagonistic interactions affecting antioxidant, immune, and detoxification systems. However, a synergistic effect was observed on ATP production, suggesting an energetic compensation mechanism. These findings highlight complex physiological disturbances, revealing the multifactorial vulnerability of honeybees and emphasizing the need to integrate interactions between multiple stressors and natural biological variability into ecotoxicological assessments.

According to neural oscillatory accounts, periodicity at the syllabic scale enhances speech comprehension through theta brain rhythms. Natural speech, however, is not strictly periodic and stronger periodicity, such as under conditions of fast speech, may hinder comprehension. Using magnetoencephalography, we investigate how natural variation in syllabic-level periodicity affects comprehension and auditory-motor coupling in brain areas related to temporal speech processing. We model speech periodicity and rate independently. Theta-band phase coupling between the posterior superior temporal gyrus (pSTG) and speech motor areas is assessed using Gaussian-Copula Mutual Information (GCMI). We find that faster syllabic rates and lower periodicity are associated with stronger coupling between the pSTG and inferior precentral gyrus, but also inferior frontal gyrus and supplementary motor areas. Comprehension improves with lower periodicity and declines at faster rates. The syllabic rate and periodicity moderate the coupling-comprehension relationship, possibly reflecting a complex interplay of lower-level auditory processing and higher-level prediction from the speech motor cortices. These findings suggest a sweet spot for natural, less periodic speech rhythms that support optimal processing and emphasize the necessity to investigate natural speech.

Accurate antigenic characterization of influenza viruses is essential for vaccine strain selection, yet routine isolation of viruses in cell culture can introduce genetic changes that obscure the properties of circulating strains. By combining deep sequencing with serological analysis of clinical specimens and cultured isolates, we demonstrate that virus propagation of human seasonal A(H3N2) in MDCK cells imposes strong purifying selection and alters antigenic profiles. Furthermore, we show that minor amino acid polymorphisms present in clinical samples can generate measurable antigenic diversity, emphasizing that natural intrahost variation shapes antigenic outcomes. These findings reveal a critical source of bias in antigenic characterization workflows and underscore the importance of directly assessing uncultured clinical material. Improved understanding of how culture adaptation and intrahost genetic diversity influence antigenic data will advance knowledge of antigenic evolution in circulating influenza viruses and improve the accuracy of vaccine strain selection for human seasonal influenza.

Complex substances such as multi-constituent substances and 'substances of unknown or variable composition, complex reaction products and biological materials' (UVCBs) usually result from the industrial processing, or extraction of natural substances, or from chemical reactions. Because of the variable and complex nature of source materials and the potential variability inherent to production processes, these substances can contain many, sometimes uncharacterized, constituents whose concentrations may vary between production batches. UVCBs make up approximately 20-25% of substances registered under regulatory frameworks globally. To identify and advance the various challenges associated with UVCB testing and assessment, the Health and Environmental Sciences Institute (HESI) organized an international workshop on Exploring the complexities of UVCB testing and risk assessment. The HESI UVCB workshop was aimed at initiating multi-sectoral, tripartite discussions on the advantages and disadvantages of the whole substance vs. representative constituent testing and assessment approaches, at identifying further research needs, and at establishing potential consensus for solutions for UVCB environmental risk assessment. Ultimately, the insight from the workshop contributed to the further refinement and strengthening of the exposure-centric tiered approach developed previously for consideration in the environmental risk assessment of UVCBs and multi-constituent substances. More specifically, it contributed to developing a systematic process to efficiently balance the characterization and testing of the whole substance and representative constituents to ensure the assessment of UVCBs is fit for purpose.

The hypocotyl length and elongation is an important characteristic that affect the soybean seedling emergence and photosynthesis. However, the basic genetic mechanism of this feature remains incompletely understood. In this study, the hypocotyl length of four-day germinated soybean seedlings was evaluated before and after 24h cultivation to assess hypocotyl elongation (HE) in 330 soybean accessions. Five quantitative trait loci (QTLs) that significantly associated with HE trait were detected by genome-wide association study (GWAS) in two models, and they are located on chromosome (Chr.) 2, 3, 11, 15, and 17, respectively. A total of 84 gene models have been found in HE QTLs candidate regions, and with a large proportion enriched in the biological processes of photosynthesis and cell differentiation. A CCCH zinc finger protein gene of GmZFP1 (Glyma.15G262900) was identified as the candidate in the major locus qHE_8 through the analysis of linkage disequilibrium (LD) blocks, gene expression patterns, and natural variation. Three SNPs substantially associated with HE in the GmZFP1 area resulted in 12 haplotypes (Hap 1-12) and four haplotype groups (Hap Ⅰ-Ⅳ). Soybean accessions carrying superior Hap Ⅲ showed significantly higher HE than the soybean lines containing Hap I, and the Hap Ⅲ made up 13.4% of the G. soja subpopulation and 61.98% of the G. max subpopulation, respectively. In genetic diversity and molecular evolution analysis, the GmZFP1 was also located in the genome selective sweep region during soybean domestication. Five QTLs were mapped by GWAS in both EMMAX and TASSEL models that significantly associated with soybean hypocotyl elongation (HE). The major candidate gene GmZFP1 underlying the qHE_8 locus was identified, and the superior haplotypes and selective sweep signals were also detected in the GmZFP1 region. The QTLs and GmZFP1 discovered in this study provided potential genetic resources for the soybean molecular breeding in the future.

Prolactin and the shared regulation of parental care and cooperative helping behavior in white-browed sparrow weaver societies.

The rise of atmospheric oxygen during the Great Oxidation Event (GOE) (ca. 2.5 to 2.1 billion years ago) permanently transformed Earth's biogeochemical cycles. The chemistry of contemporaneous marine carbonates provides a window into operation of the carbon cycle across this transition. Specifically, carbonate rocks co-eval with the GOE preserve a large and long-lived positive carbon isotope ([Formula: see text]C) excursion, the Lomagundi-Jatuli excursion (LJE), that canonically is interpreted as an increase in organic matter burial linked to the oxygenation of Earth's surface. However, the cause, synchroneity, and global nature of the LJE remain contentious due to significant uncertainties in the excursion's timing and magnitude. These uncertainties stem from the incomplete, time-uncertain, and spatially variable nature of the shallow-water sedimentary record. Here, we use Bayesian inference to reconstruct Paleoproterozoic [Formula: see text]C from globally distributed stratigraphic observations. Our inference reaffirms that the LJE is a global excursion, although its expression varies locally, and provides revised estimates for its timing and magnitude. We find that [Formula: see text]C most likely began to increase at 2,445 Ma, subsequently returning to baseline values at 2,018 Ma. The most likely excursion peak occurs at 2,130 Ma, and it is very unlikely (5% probability) that peak [Formula: see text]C values exceeded 9.1[Formula: see text]. Altogether, our results indicate the LJE has an earlier onset, longer duration, and lower magnitude than previously thought. The initial [Formula: see text]C increase occurs before or contemporaneously with both the earliest rise of atmospheric O2 and Paleoproterozoic "snowball" glaciations, hinting at a mechanistic link among the LJE, the GOE, and climate.