Despite a rapid development of Nature-Based Solutions (NBS) for carbon removal in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatiotemporal variation of net ecosystem exchange from eddy-covariance stations. A Lagrangian particle dispersion model was used for upscaling satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land-cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution thanks to the use of Landsat imagery; and (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Stratification that uses separate Fluxnet stations for each fold of validation ensures that the models are accurate across the area covered by the Fluxnet sites. Overall, we present here a globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

In this article a soil-vegetation-atmosphere transfer model is implemented to estimate amongst other variables, sensible and latent heat fluxes, in crop fields and other vegetated areas. Two distinct sub-models for carbon fluxes were implemented. We compared the models with each other and with heat flux measurements over a soy field. The results show the impact of the different stomatal response implementations on the surface heat fluxes. We found that the model has good predictive potential for operational applications or for evaluation of potential climate changes resulting from land-use modifications.

Kawasaki disease (KD) is the most common acquired pediatric heart disease. We analyzed Whole Genome Sequences (WGS) from a 6-member African American family in which KD affected two of four children. We sought rare, potentially causative genotypes by sequentially applying the following WGS filters: sequence quality scores, inheritance model (recessive homozygous and compound heterozygous), predicted deleteriousness, allele frequency, genes in KD-associated pathways or with significant associations in published KD genome-wide association studies (GWAS), and with differential expression in KD blood transcriptomes. Biologically plausible genotypes were identified in twelve variants in six genes in the two affected children. The affected siblings were compound heterozygous for the rare variants p.Leu194Pro and p.Arg247Lys in Toll-like receptor 6 (TLR6), which affect TLR6 signaling. The affected children were also homozygous for three common, linked (r2 = 1) intronic single nucleotide variants (SNVs) in TLR6 (rs56245262, rs56083757 and rs7669329), that have previously shown association with KD in cohorts of European descent. Using transcriptome data from pre-treatment whole blood of KD subjects (n = 146), expression quantitative trait loci (eQTL) analyses were performed. Subjects homozygous for the intronic risk allele (A allele of TLR6 rs56245262) had differential expression of Interleukin-6 (IL-6) as a function of genotype (p = 0.0007) and a higher erythrocyte sedimentation rate at diagnosis. TLR6 plays an important role in pathogen-associated molecular pattern recognition, and sequence variations may affect binding affinities that in turn influence KD susceptibility. This integrative genomic approach illustrates how the analysis of WGS in multiplex families with a complex genetic disease allows examination of both the common disease-common variant and common disease-rare variant hypotheses.

Many sequence variants have been linked to complex human traits and diseases¹, but deciphering their biological functions remains challenging, as most of them reside in noncoding DNA. Here we have systematically assessed the binding of 270 human transcription factors to 95,886 noncoding variants in the human genome using an ultra-high-throughput multiplex protein-DNA binding assay, termed single-nucleotide polymorphism evaluation by systematic evolution of ligands by exponential enrichment (SNP-SELEX). The resulting 828 million measurements of transcription factor-DNA interactions enable estimation of the relative affinity of these transcription factors to each variant in vitro and evaluation of the current methods to predict the effects of noncoding variants on transcription factor binding. We show that the position weight matrices of most transcription factors lack sufficient predictive power, whereas the support vector machine combined with the gapped k-mer representation show much improved performance, when assessed on results from independent SNP-SELEX experiments involving a new set of 61,020 sequence variants. We report highly predictive models for 94 human transcription factors and demonstrate their utility in genome-wide association studies and understanding of the molecular pathways involved in diverse human traits and diseases.

AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux's diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team's quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.

In the United States, hundreds of thousands of undocumented orphan wells have been abandoned, leaving the burden of managing environmental hazards to governmental agencies or the public. These wells, a result of over a century of fossil fuel extraction without adequate regulation, lack basic information like location and depth, emit greenhouse gases, and leak toxic substances into groundwater. For most of these wells, basic information such as well location and depth is unknown or unverified. Addressing this issue necessitates innovative and interdisciplinary approaches for locating, characterizing, and mitigating their environmental impacts. Our survey of the United States revealed the need for tools to identify well locations and assess conditions, prompting the development of technologies including machine learning to automatically extract information from old records (95%+ accuracy), remote sensing technologies like aero-magnetometers to find buried wells, and cost-effective methods for estimating methane emissions. Notably, fixed-wing drones equipped with magnetometers have emerged as cost-effective and efficient for discovering unknown wells, offering advantages over helicopters and quadcopters. Efforts also involved leveraging local knowledge through outreach to state and tribal governments as well as citizen science initiatives. These initiatives aim to significantly contribute to environmental sustainability by reducing greenhouse gases and improving air and water quality.

Five years after the identification of Zika virus as a human teratogen, we reviewed the early clinical manifestations, collectively called congenital Zika syndrome (CZS). Children with CZS have a very poor prognosis with extremely low performance in motor, cognitive, and language development domains, and practically all feature severe forms of cerebral palsy. However, these manifestations are the tip of the iceberg, with some children presenting milder forms of deficits. Additionally, neurodevelopment can be in the normal range in the majority of the non-microcephalic children born without brain or eye abnormalities. Vertical transmission and the resulting disruption in development of the brain are much less frequent when maternal infection occurs in the second half of the pregnancy. Experimental studies have alerted to the possibility of other behavioral outcomes both in prenatally infected children and in postnatal and adult infections. Cofactors play a vital role in the development of CZS and involve genetic, environmental, nutritional, and social determinants leading to the asymmetric distribution of cases. Some of these social variables also limit access to multidisciplinary professional treatment.