The dynamic mechanism of 4E-BP1 recognition and phosphorylation by mTORC1.

The activation of cap-dependent translation in eukaryotes requires multisite, hierarchical phosphorylation of 4E-BP by the 1 MDa kinase mammalian target of rapamycin complex 1 (mTORC1). To resolve the mechanism of this hierarchical phosphorylation at the atomic level, we monitored by NMR spectroscopy the interaction of intrinsically disordered 4E binding protein isoform 1 (4E-BP1) with the mTORC1 subunit regulatory-associated protein of mTOR (Raptor). The N-terminal RAIP motif and the C-terminal TOR signaling (TOS) motif of 4E-BP1 bind separate sites in Raptor, resulting in avidity-based tethering of 4E-BP1. This tethering orients the flexible central region of 4E-BP1 toward the mTORC1 kinase site for phosphorylation. The structural constraints imposed by the two tethering interactions, combined with phosphorylation-induced conformational switching of 4E-BP1, explain the hierarchy of 4E-BP1 phosphorylation by mTORC1. Furthermore, we demonstrate that mTORC1 recognizes both free and eIF4E-bound 4E-BP1, allowing rapid phosphorylation of the entire 4E-BP1 pool and efficient activation of translation. Finally, our findings provide a mechanistic explanation for the differential rapamycin sensitivity of the 4E-BP1 phosphorylation sites.

Lipidated variants of the antimicrobial peptide nisin produced via incorporation of methionine analogs for click chemistry show improved bioactivity.

The increase in antibiotic resistance calls for accelerated molecular engineering strategies to diversify natural products for drug discovery. The incorporation of non-canonical amino acids (ncAAs) is an elegant strategy for this purpose, offering a diverse pool of building blocks to introduce desired properties into antimicrobial lanthipeptides. We here report an expression system using Lactococcus lactis as a host for non-canonical amino acid incorporation with high efficiency and yield. We show that incorporating the more hydrophobic analog ethionine (instead of methionine) into nisin improves its bioactivity against several Gram-positive strains we tested. New-to-nature variants were further created by click chemistry. By azidohomoalanine (Aha) incorporation and subsequent click chemistry, we obtained lipidated variants at different positions in nisin or in truncated nisin variants. Some of them show improved bioactivity and specificity against several pathogenic bacterial strains. These results highlight the ability of this methodology for lanthipeptide multi-site lipidation, to create new-to-nature antimicrobial products with diverse features, and extend the toolbox for (lanthi)peptide drug improvement and discovery.

Multi-feature concatenation and multi-classifier stacking: An interpretable and generalizable machine learning method for MDD discrimination with rsfMRI.

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC's full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.

A simple Cox approach to estimating risk ratios without sharing individual-level data in multi-site studies.

Epidemiologic studies frequently use risk ratios to quantify associations between exposures and binary outcomes. When the data are physically stored at multiple data partners, it can be challenging to perform individual-level analysis if data cannot be pooled centrally due to privacy constraints. Existing methods either require multiple file transfers between each data partner and an analysis center (e.g., distributed regression) or only provide approximate estimation of the risk ratio (e.g., meta-analysis). Here we develop a practical method that requires a single transfer of eight summary-level quantities from each data partner. Our approach leverages an existing risk-set method and software originally developed for Cox regression. Sharing only summary-level information, the proposed method provides risk ratio estimates and confidence intervals identical to those that would be provided - if individual-level data were pooled - by the modified Poisson regression. We justify the method theoretically, confirm its performance using simulated data, and implement it in a distributed analysis of COVID-19 data from the U.S. Food and Drug Administration's Sentinel System.

Distributed Cox proportional hazards regression using summary-level information.

Individual-level data sharing across multiple sites can be infeasible due to privacy and logistical concerns. This article proposes a general distributed methodology to fit Cox proportional hazards models without sharing individual-level data in multi-site studies. We make inferences on the log hazard ratios based on an approximated partial likelihood score function that uses only summary-level statistics. This approach can be applied to both stratified and unstratified models, accommodate both discrete and continuous exposure variables, and permit the adjustment of multiple covariates. In particular, the fitting of stratified Cox models can be carried out with only one file transfer of summary-level information. We derive the asymptotic properties of the proposed estimators and compare the proposed estimators with the maximum partial likelihood estimators using pooled individual-level data and meta-analysis methods through simulation studies. We apply the proposed method to a real-world data set to examine the effect of sleeve gastrectomy versus Roux-en-Y gastric bypass on the time to first postoperative readmission.

A practical guide to cross-cultural and multi-sited data collection in the biological and behavioural sciences.

Researchers in the biological and behavioural sciences are increasingly conducting collaborative, multi-sited projects to address how phenomena vary across ecologies. These types of projects, however, pose additional workflow challenges beyond those typically encountered in single-sited projects. Through specific attention to cross-cultural research projects, we highlight four key aspects of multi-sited projects that must be considered during the design phase to ensure success: (1) project and team management; (2) protocol and instrument development; (3) data management and documentation; and (4) equitable and collaborative practices. Our recommendations are supported by examples from our experiences collaborating on the Evolutionary Demography of Religion project, a mixed-methods project collecting data across five countries in collaboration with research partners in each host country. To existing discourse, we contribute new recommendations around team and project management, introduce practical recommendations for exploring the validity of instruments through qualitative techniques during piloting, highlight the importance of good documentation at all steps of the project, and demonstrate how data management workflows can be strengthened through open science practices. While this project was rooted in cross-cultural human behavioural ecology and evolutionary anthropology, lessons learned from this project are applicable to multi-sited research across the biological and behavioural sciences.

T1 and T2 measurements across multiple 0.55T MRI systems using open-source vendor-neutral sequences.

PURPOSE: To compare T1 and T2 measurements across commercial and prototype 0.55T MRI systems in both phantom and healthy participants using the same vendor-neutral pulse sequences, reconstruction, and analysis methods. METHODS: Standard spin echo measurements and abbreviated protocol measurements of T1, B1, and T2 were made on two prototype 0.55 T systems and two commercial 0.55T systems using an ISMRM/NIST system phantom. Additionally, five healthy participants were imaged at each system using the abbreviated protocol for T1, B1, and T2 measurement. The phantom measurements were compared to NMR-based reference measurements to determine accuracy, and both phantom and in vivo measurements were compared to assess reproducibility and differences between the prototype and commercial systems. RESULTS: Vendor-neutral sequences were implemented across all four systems, and the code for pulse sequences and reconstruction is freely available. For participants, there was no difference in the mean T1 and T2 relaxation times between the prototype and commercial systems. In the phantom, there were no significant differences between the prototype and commercial systems for T1 and T2 measurements using the abbreviated protocol. CONCLUSION: Quantitative T1 and T2 measurements at 0.55T in phantom and healthy participants are not statistically different across the prototype and commercial systems.

Expression of regional brain amyloid-ß deposition with [18F]Flutemetamol in Centiloid scale -a multi-site study.

PURPOSE: The Centiloid project helps calibrate the quantitative amyloid-ß (Aß) load into a unified Centiloid (CL) scale that allows data comparison across multi-site. How the smaller regional amyloid converted into CL has not been attempted. We first aimed to express regional Aß deposition in CL using [18F]Flutemetamol and evaluate regional Aß deposition in CL with that in standardized uptake value ratio (SUVr). Second, we aimed to determine the presence or absence of focal Aß deposition by measuring regional CL in equivocal cases showing negative global CL. METHODS: Following the Centiloid project pipeline, Level-1 replication, Level-2 calibration, and quality control were completed to generate corresponding Centiloid conversion equations to convert SUVr into Centiloid at regional levels. In equivocal cases, the regional CL was compared with visual inspection to evaluate regional Aß positivity. RESULTS: 14 out of 16 regional conversions from [18F]Flutemetamol SUVr to Centiloid successfully passed the quality control, showing good reliability and relative variance, especially precuneus/posterior cingulate and prefrontal regions with good stability for Centiloid scaling. The absence of focal Aß deposition could be detected by measuring regional CL, showing a high agreement rate with visual inspection. The regional Aß positivity in the bilateral anterior cingulate cortex was most prevalent in equivocal cases. CONCLUSION: The expression of regional brain Aß deposition in CL with [18F]Flutemetamol has been attempted in this study. Equivocal cases had focal Aß deposition that can be detected by measuring regional CL.

Placing the keystone: the LIMBIC Military and Tactical Athlete Research Study.

OBJECTIVE: The LIMBIC Military and Tactical Athletic Research Study (MATARS) framework was established to confirm and extend understanding of concussion with initial studies driven by clinical data collected between 2015 and 2020 in a collegiate sports setting. The LIMBIC MATARS framework will be leveraged to apply gold-standard and innovative research designs to advance the science of concussion. This manuscript provides the background, methodology, and initial demographic data associated with the LIMBIC MATARS. METHODS: Consensus-based common data elements were used to conduct a retrospective chart review, specific to collegiate athletes diagnosed with concussions between 2015 and 2020 at 11 universities. RESULTS: A final sample of 1,311 (47.8% female) concussions were diagnosed during the five-year study period from athletes participating in a variety of National Collegiate Athlete Association (NCAA) sports. The LIMBIC MATARS demographic data, align with the NCAA and other pioneering multi-site concussion-related studies in terms of biological sex, race and ethnicity, and sport participation. CONCLUSION: This pragmatic, methodological approach was used to address several a priori hypotheses related to concussion, align with other multi-site studies of concussion, and establish a consortium for future investigations.

Structure-function coupling reveals the brain hierarchical structure dysfunction in Alzheimer's disease: A multicenter study.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative condition characterized by cognitive decline. To date, the specific dysfunction in the brain's hierarchical structure in AD remains unclear. METHODS: We introduced the structural decoupling index (SDI), based on a multi-site data set comprising functional and diffusion-weighted magnetic resonance imaging data from 793 subjects, to assess their brain hierarchy. RESULTS: Compared to normal controls (NCs), individuals with AD exhibited increased SDI within the posterior superior temporal sulcus, insular gyrus, precuneus, hippocampus, amygdala, postcentral gyrus, and cingulate gyrus; meanwhile, the patients with AD demonstrated decreased SDI in the frontal lobe. The SDI in those regions also showed a significant correlation with cognitive ability. Moreover, the SDI was a robust AD neuroimaging biomarker capable of accurately distinguishing diagnostic status (area under the curve [AUC] = 0.86). DISCUSSION: Our findings revealed the dysfunction of the brain's hierarchical structure in AD. Furthermore, the SDI could serve as a promising neuroimaging biomarker for AD. HIGHLIGHTS: This study utilized multi-center, multi-modal data from East Asian populations. We found an increased spatial gradient of the structure decoupling index (SDI) from sensory-motor to higher-order cognitive regions. Changes in SDI are associated with energy metabolism and mitochondria. SDI can identify Alzheimer's disease (AD) and further uncover the disease mechanisms of AD.

Multi-site and long-term estimation of phosphorus dynamics for paddy surface water in China based on new MLEpaddy-P.

Paddy surface water serves as the primary source of artificial drainage and rainfall runoff leading to phosphorus (P) loss from paddy fields. The quantification of P dynamics in paddy surface water on a large scale is challenging due to the spatiotemporal heterogeneity of influencing factors and the limitations of field measurements. Based on 1226 data sets from 33 field sites covering the three main rice-growing regions of China (the Southeast Coast, the Yangtze River Basin, and the Northeast Plain), we analyzed the spatiotemporal characteristics of P attenuation in paddy surface water and its influencing factors. A new multi-site and long-term phosphorus estimation model for paddy (MLEpaddy-P) was proposed to evaluate the total phosphorus (TP) dynamics at national scale by improving the initial concentration (C0) and attenuation coefficient (k) of the first-order kinetic model (Ct=C0∙e-k(t-1)). Our study showed that: (1) Fertilizer amounts, soil organic matter content, soil Olsen-P content, soil pH, and soil total phosphorus are the primary factors affecting the variation of C0 and k; (2) Yangtze River Basin possessed the highest C0 (6.87 ± 12.97 mg/L) and high k ≤ 7 (0.262 in 1-7 days after fertilization), followed by Southeast Coast (4.15 ± 5.33 mg/L; 0.263) and Northeast Plain (1.33 ± 1.50 mg/L; 0.239), respectively; (3) MLEpaddy-P performed well in daily TP dynamics estimation at national scale with R2 of 0.74-0.85; (4) Middle and lower reaches of the Yangtze River Basin were the critical regions with high TP concentration due to high fertilizer amount and soil Olsen-P content. The new universal model realizes the multi-site and long-term estimation of P dynamics while greatly saving multi-site monitoring costs. This study provides a basis for early warning and targeted control of P loss from paddies.

Predictors of climate change literacy in the era of global boiling: a cross-sectional survey of Egyptian nursing students.

BACKGROUND: Climate changes have led to health and environmental risks, so it has become essential to measure climate change literacy among the entire population, especially nursing students. The significant role of nursing students in raising public awareness and future healthcare roles emphasizes assessing the predictors of climate change literacy among nursing students. AIMS: This study seeks to identify the predictors of climate change literacy among nursing students in A Multi-Site Survey. DESIGN: A multi-site descriptive cross-sectional study adheres to the guidelines outlined in A Consensus-Based Checklist for Reporting Survey Studies collected for five months, from the 1st of July 2023 to November 2023. The study participants comprise 10,084 nursing students from all 27 governments in Egypt. The researcher used the Predictors of Nursing Students' Climate Change Literacy scale in this study. Data was collected, with 25 min average time to complete. Backward multiple linear regression was used to identify these predictors. RESULTS: In the current study, nursing students demonstrated a moderate understanding of climate science (mean score 14.38), communication and advocacy skills (mean score 14.41), and knowledge of adaptation and mitigation strategies (mean score 13.33). Climate health impacts (mean score 17.72) emerged as the domain with the highest level of knowledge. No significant differences in climate literacy were observed across diverse student backgrounds (all p-values were > 0.05). Perceived faculty knowledge of climate change positively correlated with all four domains of climate literacy and emerged as a significant predictor in multiple linear regression analyses (all p-values were < 0.001). IMPLICATION: While our findings highlight significant predictors of climate literacy, it is essential to recognize that these results identify associations rather than causal relationships. Based on these associations, it is recommended that nursing professionals be equipped with comprehensive knowledge of climate adaptation strategies to better advocate for and implement effective public health measures.

Improvement of palliative care for people with intellectual disabilities: A multi-site evaluation.

BACKGROUND: To improve the quality of palliative care, six evidence-based tools were implemented in 10 care services specialised in care for people with intellectual disabilities. Contextual differences were taken into account by using a participatory action research approach. METHOD: The RE-AIM framework (Reach, Effectiveness, Adoption, Implementation and Maintenance) structured the evaluation. Data sources were online questionnaires completed by 299 professionals at baseline (response 45.2%) and 250 professionals after 2.5 years (35.1%), 11 semi-structured group interviews with 43 professionals, field notes and implementation plans. RESULTS: A total of 767 professionals and 43 teams were reached. The effectiveness of the intervention was demonstrated in an improved knowledge of palliative care policy and increased competences among professionals. 79% of the professionals adopted tools in the toolbox. The participatory action research method was perceived as valuable in driving change. CONCLUSIONS: Improving palliative care needs a context-specific, flexible approach, with involvement of all stakeholders.

A Computation-Guided Design of Highly Defined and Dense Bimetallic Active Sites on a Two-Dimensional Conductive Metal-Organic Framework for Efficient H2O2 Electrosynthesis.

Electrochemical synthesis of hydrogen peroxide (H2O2) via the two-electron oxygen reduction reaction (2e--ORR) provides an alternative method to the energy-intensive anthraquinone method. Metal macrocycles with precise coordination are widely used for 2e--ORR electrocatalysis, but they have to be commonly loaded on conductive substrates, thus exposing a large number of 2e--ORR-inactive sites that result in poor H2O2 production rate and efficiency. Herein, guided by first-principle predictions, a substrate-free and two-dimensional conductive metal-organic framework (Ni-TCPP(Co)), composed of CoN4 sites in porphine(Co) centers and Ni2O8 nodes, is designed as a multi-site catalyst for H2O2 electrosynthesis. The approperiate distance between the CoN4 and Ni2O8 sites in Ni-TCPP(Co) weakens the electron transfer between them, thus ensuring their inherent activities and creating high-density active sites. Meanwhile, the intrinsic electronic conductivity and porosity of Ni-TCPP(Co) further facilitate rapid reaction kinetics. Therefore, outstanding 2e--ORR electrocatalytic performance has been achieved in both alkaline and neutral electrolytes (>90 %/85 % H2O2 selectivity within 0-0.8 V vs. RHE and >18.2/18.0 mol g-1 h-1 H2O2 yield under alkaline/neutral conditions), with confirmed feasibility for water purification and disinfection applications. This strategy thus provides a new avenue for designing catalysts with precise coordination and high-density active sites, promoting high-efficiency electrosynthesis of H2O2 and beyond.

Efficient Hydrogen Generation from Ammonia Borane Hydrolysis on a Tandem Ruthenium-Platinum-Titanium Catalyst.

Hydrolysis of ammonia borane (NH3BH3, AB) involves multiple undefined steps and complex adsorption and activation, so single or dual sites are not enough to rapidly achieve the multi-step catalytic processes. Designing multi-site catalysts is necessary to enhance the catalytic performance of AB hydrolysis reactions but revealing the matching reaction mechanisms of AB hydrolysis is a great challenge. In this work, we propose to construct RuPt-Ti multi-site catalysts to clarify the multi-site tandem activation mechanism of AB hydrolysis. Experimental and theoretical studies reveal that the multi-site tandem mode can respectively promote the activation of NH3BH3 and H2O molecules on the Ru and Pt sites as well as facilitate the fast transfer of *H and the desorption of H2 on Ti sites at the same time. RuPt-Ti multi-site catalysts exhibit the highest turnover frequency (TOF) of 1293 min-1 for AB hydrolysis reaction, outperforming the single-site Ru, dual-site RuPt and Ru-Ti catalysts. This study proposes a multi-site tandem concept for accelerating the dehydrogenation of hydrogen storage material, aiming to contribute to the development of cleaner, low-carbon, and high-performance hydrogen production systems.

Multisite Fine-Tuning in Hybrid Cadmium Halides Enables Wide Range Emissions for Anti-Counterfeiting.

Achieving tunable emissions spanning the spectrum, from blue to near-infrared (NIR) light, within a single component is a formidable challenge with significant implication, particularly in tailoring multicolor luminescence for anti-counterfeiting purposes. In this study, we demonstrate a broad spectrum of emissions, covering blue to red and extending into NIR light in [BPy]2CdX4 : xSb3+ (BPy=Butylpyridinium; X=Cl, Br; x=0 to 0.08) through precise multisite structural fine-tuning. Notably, the multicolor emissions from [BPy]2CdBr4 : Sb3+ manifest a distinctive pattern, transitioning from blue to yellow in tandem with the host [BPy]2CdBr4 and further extending from yellow to NIR with its homologous [BPy]2CdCl4 : Sb3+, resulting in the simultaneous presence of intersecting and independent emission colors. Detailed modulation of chemical composition enables partial luminescence switching, facilitating the creation of diverse patterns with multicolor luminescence by employing [BPy]2CdX4 : xSb3+ as phosphors. This study for the first time successfully implements several groups of tunable emission colors in a single matrix via multisite fine-tuning. Such an effective strategy not only develops the specific relationships between tunable emissions and adjustable compositions, but also introduces a cost-effective and straightforward approach to achieving unique, high-level, plentiful-color and multiple-information-storage labels for advanced anti-counterfeiting applications.

Comprehensive evaluation of harmonization on functional brain imaging for multisite data-fusion.

To embrace big-data neuroimaging, harmonizing the site effect in resting-state functional magnetic resonance imaging (R-fMRI) data fusion is a fundamental challenge. A comprehensive evaluation of potentially effective harmonization strategies, particularly with specifically collected data, has been scarce, especially for R-fMRI metrics. Here, we comprehensively assess harmonization strategies from multiple perspectives, including tests on residual site effect, individual identification, test-retest reliability, and replicability of group-level statistical results, on widely used R-fMRI metrics across various datasets, including data obtained from participants with repetitive measures at different scanners. For individual identifiability (i.e., whether the same subject could be identified across R-fMRI data scanned across different sites), we found that, while most methods decreased site effects, the Subsampling Maximum-mean-distance based distribution shift correction Algorithm (SMA) and parametric unadjusted CovBat outperformed linear regression models, linear mixed models, ComBat series and invariant conditional variational auto-encoder in clustering accuracy. Test-retest reliability was better for SMA and parametric adjusted CovBat than unadjusted ComBat series and parametric unadjusted CovBat in the number of overlapped voxels. At the same time, SMA was superior to the latter in replicability in terms of the Dice coefficient and the scale of brain areas showing sex differences reproducibly observed across datasets. Furthermore, SMA better detected reproducible sex differences of ALFF under the site-sex confounded situation. Moreover, we designed experiments to identify the best target site features to optimize SMA identifiability, test-retest reliability, and stability. We noted both sample size and distribution of the target site matter and introduced a heuristic formula for selecting the target site. In addition to providing practical guidelines, this work can inform continuing improvements and innovations in harmonizing methodologies for big R-fMRI data.

Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data.

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realise the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonisation methods are the two primary methods used to eliminate scanner/site effects. Unfortunately, there are challenges with both ICA-based and GLM-based harmonisation methods to remove site effects completely when the signals of interest and scanner/site effects-related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonisation strategy that implements dual projection (DP) theory based on ICA to remove the scanner/site effects more completely. This method can separate the signal effects correlated with site variables from the identified site effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a travelling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of a DP-based ICA harmonisation method. Results show that DP-based ICA harmonisation has superior performance for removing site effects and enhancing the sensitivity to detect signals of interest as compared with GLM-based and conventional ICA harmonisation methods.

Brain metabolites measured with magnetic resonance spectroscopy in pediatric concussion and orthopedic injury: An Advancing Concussion Assessment in Pediatrics (A-CAP) study.

Millions of children sustain a concussion annually. Concussion disrupts cellular signaling and neural pathways within the brain but the resulting metabolic disruptions are not well characterized. Magnetic resonance spectroscopy (MRS) can examine key brain metabolites (e.g., N-acetyl Aspartate (tNAA), glutamate (Glx), creatine (tCr), choline (tCho), and myo-Inositol (mI)) to better understand these disruptions. In this study, we used MRS to examine differences in brain metabolites between children and adolescents with concussion versus orthopedic injury. Children and adolescents with concussion (n = 361) or orthopedic injury (OI) (n = 184) aged 8 to 17 years were recruited from five emergency departments across Canada. MRS data were collected from the left dorsolateral prefrontal cortex (L-DLPFC) using point resolved spectroscopy (PRESS) at 3 T at a mean of 12 days post-injury (median 10 days post-injury, range 2-33 days). Univariate analyses for each metabolite found no statistically significant metabolite differences between groups. Within each analysis, several covariates were statistically significant. Follow-up analyses designed to account for possible confounding factors including age, site, scanner, vendor, time since injury, and tissue type (and interactions as appropriate) did not find any metabolite group differences. In the largest sample of pediatric concussion studied with MRS to date, we found no metabolite differences between concussion and OI groups in the L-DLPFC. We suggest that at 2 weeks post-injury in a general pediatric concussion population, brain metabolites in the L-DLPFC are not specifically affected by brain injury.

Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions.

Wetlands are the largest natural source of methane (CH4 ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH4 , but interpreting its spatiotemporal variations is challenging due to the co-occurrence of CH4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data-model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data-constrained model-iPEACE-reasonably reproduced CH4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH4 production appeared to be the most important process, followed by oxidation in explaining inter-site variations in CH4 emissions. Based on a sensitivity analysis, CH4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant-mediated transport appeared to be the major pathway for CH4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH4 production and CH4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH4 production, plant-mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH4 emissions across biomes. These processes and associated parameters for CH4 emissions among and within the wetlands provide useful insights for interpreting observed net CH4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH4 fluxes.