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For steady electroconversion to value-added chemical products with high efficiency, electrocatalyst reconstruction during electrochemical reactions is a critical issue in catalyst design strategies. Here, we report a reconstruction-immunized catalyst system in which Cu nanoparticles are protected by a quasi-graphitic C shell. This C shell epitaxially grew on Cu with quasi-graphitic bonding via a gas–solid reaction governed by the CO (g) - CO 2 (g) - C (s) equilibrium. The quasi-graphitic C shell-coated Cu was stable during the CO 2 reduction reaction and provided a platform for rational material design. C 2+ product selectivity could be additionally improved by doping p -block elements. These elements modulated the electronic structure of the Cu surface and its binding properties, which can affect the intermediate binding and CO dimerization barrier. B-modified Cu attained a 68.1% Faradaic efficiency for C 2 H 4 at −0.55 V (vs RHE) and a C 2 H 4 cathodic power conversion efficiency of 44.0%. In the case of N-modified Cu, an improved C 2+ selectivity of 82.3% at a partial current density of 329.2 mA/cm 2 was acquired. Quasi-graphitic C shells, which enable surface stabilization and inner element doping, can realize stable CO 2 -to-C 2 H 4 conversion over 180 h and allow practical application of electrocatalysts for renewable energy conversion. Surface reconstruction of electrocatalysts is an important issue for electroconversion of carbon dioxide to value-added chemical products. Here the authors address this issue by using copper nanoparticles protected by self-formed quasi graphitic carbon shell for stable CO2 to C2H4 conversion.

Three-dimensional (3D) nanoframe structures are very appealing because their inner voids and ridges interact efficiently with light and analytes, allowing for effective optical-based sensing. However, the realization of complex nanoframe architecture with high yield is challenging because the systematic design of such a complicated nanostructure lacks an appropriate synthesis protocol. Here, we show the synthesis method for complex 3D nanoframes wherein two-dimensional (2D) dual-rim nanostructures are engraved on each facet of octahedral nanoframes. The synthetic scheme proceeds through multiple executable on-demand steps. With Au octahedral nanoparticles as a sacrificial template, sequential processes of edge-selective Pt deposition and inner Au etching lead to Pt octahedral mono-rim nanoframes. Then, adlayers of Au are grown on Pt skeletons via the Frank-van der Merwe mode, forming sharp and well-developed edges. Next, Pt selective deposition on both the inner and outer boundaries leads to tunable geometric patterning on Au. Finally, after the selective etching of Au, Pt octahedral dual-rim nanoframes with highly homogeneous size and shape are achieved. In order to endow plasmonic features, Au is coated around Pt frames while retaining their geometric shape. The resultant plasmonic dual-rim engraved nanoframes possess strong light entrapping capability verified by single-particle surface-enhanced Raman scattering (SERS) and show the potential of nanoprobes for biosensing through SERS-based immunoassay. Most SERS-active nanostructures suffer from low robustness against misalignment to field polarization. Here, the authors demonstrate three-dimensional nanoframes of octahedral geometry, with two rims engraved on each facet, as polarization-independent SERS nanoprobes.

Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuroscience. However, these models are complex and often hard to interpret, making it difficult to evaluate their neuroscientific validity and contribution to understanding the brain. For neuroimaging-based machine-learning models to be interpretable, they should (i) be comprehensible to humans, (ii) provide useful information about what mental or behavioral constructs are represented in particular brain pathways or regions, and (iii) demonstrate that they are based on relevant neurobiological signal, not artifacts or confounds. In this protocol, we introduce a unified framework that consists of model-, feature- and biology-level assessments to provide complementary results that support the understanding of how and why a model works. Although the framework can be applied to different types of models and data, this protocol provides practical tools and examples of selected analysis methods for a functional MRI dataset and multivariate pattern-based predictive models. A user of the protocol should be familiar with basic programming in MATLAB or Python. This protocol will help build more interpretable neuroimaging-based machine-learning models, contributing to the cumulative understanding of brain mechanisms and brain health. Although the analyses provided here constitute a limited set of tests and take a few hours to days to complete, depending on the size of data and available computational resources, we envision the process of annotating and interpreting models as an open-ended process, involving collaborative efforts across multiple studies and laboratories. Neuroimaging-based machine-learning models should be interpretable to neuroscientists and users in applied settings. This protocol describes how to assess the interpretability of models based on fMRI.

Single nucleotide polymorphism (SNP) genotyping has revolutionized crop improvement by enabling high-resolution genomic analyses and accelerating breeding programs. In soybean (Glycine max (L.) Merr.), a globally important legume crop, existing genotyping data for 180,961 SNP markers from the Korean soybean core collection were generated using the outdated Williams 82 reference genome version 1 (Wm82.v1), which contains numerous assembly gaps and misassemblies that limit genomic resolution. While high-quality reference genomes including Wm82.v4 and Wm82.v6 (telomere-to-telomere assembly) are now available, the valuable existing SNP array data have not been integrated with these improved genomic resources. Here we show successful remapping of the 180K SNP array data to both Wm82.v4 and Wm82.v6 reference genomes through sequence-based alignment of flanking regions. We extracted flanking sequences from SNP marker positions in Wm82.v1 and mapped them to the newer reference versions based on sequence similarity, excluding markers with mapping failures, allele mismatches, low-identity alignments, or multiple mappings, which resulted in the successful mapping of 175,202 and 175,763 markers to Wm82.v4 and Wm82.v6, respectively. We also remapped genotype data from 927 soybean accessions (497 USDA-GRIN accessions and 430 Korean core collection accessions) to both reference versions. This updated SNP dataset provides the soybean research community with a comprehensive genomic resource that leverages both existing genotyping investments and state-of-the-art reference genome assemblies for enhanced crop improvement and genomic studies.

Key messageGenomic regions associated with seed protein, oil and amino acid contents were identified by genome-wide association analyses. Geographic distributions of haplotypes indicate scope of improvement of these traits.Soybean [Glycine max (L.) Merr.] protein and oil are used worldwide in feed, food and industrial materials. Increasing seed protein and oil contents is important; however, protein content is generally negatively correlated with oil content. We conducted a genome-wide association study using phenotypic data collected from five environments for 621 accessions in maturity groups I–IV and 34,014 markers to identify quantitative trait loci (QTL) for seed content of protein, oil and several essential amino acids. Three and five genomic regions were associated with seed protein and oil contents, respectively. One, three, one and four genomic regions were associated with cysteine, methionine, lysine and threonine content (g kg−1 crude protein), respectively. As previously shown, QTL on chromosomes 15 and 20 were associated with seed protein and oil contents, with both exhibiting opposite effects on the two traits, and the chromosome 20 QTL having the most significant effect. A multi-trait mixed model identified trait-specific QTL. A QTL on chromosome 5 increased oil with no effect on protein content, and a QTL on chromosome 10 increased protein content with little effect on oil content. The chromosome 10 QTL co-localized with maturity gene E2/GmGIa. Identification of trait-specific QTL indicates feasibility to reduce the negative correlation between protein and oil contents. Haplotype blocks were defined at the QTL identified on chromosomes 5, 10, 15 and 20. Frequencies of positive effect haplotypes varied across maturity groups and geographic regions, providing guidance on which alleles have potential to contribute to soybean improvement for specific regions.

Mutations in IDH1 and IDH2, the genes coding for isocitrate dehydrogenases 1 and 2, are common in several human cancers, including leukemias, and result in overproduction of the (R)-enantiomer of 2-hydroxyglutarate [(R)-2HG]. Elucidation of the role of IDH mutations and (R)-2HG in leukemogenesis has been hampered by a lack of appropriate cell-based models. Here, we show that a canonical IDH1 mutant, IDH1 R132H, promotes cytokine independence and blocks differentiation in hematopoietic cells. These effects can be recapitulated by (R)-2HG, but not (S)-2HG, despite the fact that (S)-2HG more potently inhibits enzymes, such as the 5'-methylcytosine hydroxylase TET2, that have previously been linked to the pathogenesis of IDH mutant tumors. We provide evidence that this paradox relates to the ability of (S)-2HG, but not (R)-2HG, to inhibit the EglN prolyl hydroxylases. Additionally, we show that transformation by (R)-2HG is reversible.

Characterizing cerebral contributions to individual variability in pain processing is crucial for personalized pain medicine, but has yet to be done. In the present study, we address this problem by identifying brain regions with high versus low interindividual variability in their relationship with pain. We trained idiographic pain-predictive models with 13 single-trial functional MRI datasets (n = 404, discovery set) and quantified voxel-level importance for individualized pain prediction. With 21 regions identified as important pain predictors, we examined the interindividual variability of local pain-predictive weights in these regions. Higher-order transmodal regions, such as ventromedial and ventrolateral prefrontal cortices, showed larger individual variability, whereas unimodal regions, such as somatomotor cortices, showed more stable pain representations across individuals. We replicated this result in an independent dataset (n = 124). Overall, our study identifies cerebral sources of individual differences in pain processing, providing potential targets for personalized assessment and treatment of pain.Pain experience is highly individual, but its individual-specific brain features remain unclear. The authors identify brain regions with consistent versus variable representations of pain across a large sample of individuals.

The ( R )-enantiomer of 2-hydroxyglutarate, which is produced when IDH is mutated in human tumours, is shown to stimulate the activity of the EGLN prolyl 4-hydroxylases, leading to diminished levels of HIF and enhanced human astrocyte proliferation. Cancer induction by isocitrate dehydrogenase mutation Mutations in the isocitrate dehydrogenase genes IDH1 and IDH2 have been identified in gliomas, the most common form of brain tumour, and in other cancers including leukaemias. The mutated enzymes produce 2-hydroxyglutarate (2HG), which is a potential oncometabolite. Three papers in this issue of Nature examine the mechanisms through which IDH mutations promote cancers. Lu et al . show that 2HG-producing IDH mutants can prevent the histone demethylation that is required for progenitor cells to differentiate, potentially contributing to tumour-cell accumulation. Turcan et al . show that IDH1 mutation in primary human astrocytes induces DNA hypermethylation and reshapes the methylome to resemble that of the CIMP phenotype, a common feature of gliomas and other solid tumours. Koivunen et al . show that the ( R )-enantiomer of 2HG (but not the ( S )-enantiomer) can stimulate the activity of the EGLN prolyl 4-hydroxylases, leading to diminished levels of hypoxia-inducible factor (HIF), which in turn can enhance cell proliferation. These papers establish a framework for understanding gliomagenesis and highlight the interplay between genomic and epigenomic changes in human cancers. The identification of succinate dehydrogenase (SDH), fumarate hydratase (FH) and isocitrate dehydrogenase (IDH) mutations in human cancers has rekindled the idea that altered cellular metabolism can transform cells. Inactivating SDH and FH mutations cause the accumulation of succinate and fumarate, respectively, which can inhibit 2-oxoglutarate (2-OG)-dependent enzymes, including the EGLN prolyl 4-hydroxylases that mark the hypoxia inducible factor (HIF) transcription factor for polyubiquitylation and proteasomal degradation 1 . Inappropriate HIF activation is suspected of contributing to the pathogenesis of SDH-defective and FH-defective tumours but can suppress tumour growth in some other contexts. IDH1 and IDH2, which catalyse the interconversion of isocitrate and 2-OG, are frequently mutated in human brain tumours and leukaemias. The resulting mutants have the neomorphic ability to convert 2-OG to the ( R )-enantiomer of 2-hydroxyglutarate (( R )-2HG) 2 , 3 . Here we show that ( R )-2HG, but not ( S )-2HG, stimulates EGLN activity, leading to diminished HIF levels, which enhances the proliferation and soft agar growth of human astrocytes. These findings define an enantiomer-specific mechanism by which the ( R )-2HG that accumulates in IDH mutant brain tumours promotes transformation and provide a justification for exploring EGLN inhibition as a potential treatment strategy.

Background Additional to total protein content, the amino acid (AA) profile is important to the nutritional value of soybean seed. The AA profile in soybean seed is a complex quantitative trait controlled by multiple interconnected genes and pathways controlling the accumulation of each AA. With a total of 621 soybean germplasm, we used three genome-wide association study (GWAS)-based approaches to investigate the genomic regions controlling the AA content and profile in soybean. Among those approaches, the GWAS network analysis we implemented takes advantage of the relationships between specific AAs to identify the genetic control of AA profile. Results For Approach I, GWAS were performed for the content of 24 single AAs under all environments combined. Significant SNPs grouping into 16 linkage disequilibrium (LD) blocks from 18 traits were identified. For Approach II, the individual AAs were grouped by five families according to their metabolic pathways and were examined based on the sum, ratios, and interactions of AAs within the same biochemical family. Significant SNPs grouping into 35 LD blocks were identified, with SNPs associated with traits from the same biochemical family often positioned on the same LD blocks. Approach III, a correlation-based network analysis, was performed to assess the empirical relationships among AAs. Two groups were described by the network topology, Group 1: Ala, Gly, Lys, available Lys (Alys), and Thr and Group 2: Ile and Tyr. Significant SNPs associated with a ratio of connected AAs or a ratio of a single AA to its fully or partially connected metabolic groups were identified within 9 LD blocks for Group 1 and 2 LD blocks for Group 2. Among 40 identified QTL for AA or AA-derived traits, three genomic regions were novel in terms of seed composition traits (oil, protein, and AA content). An additional 24 regions had previously not been specifically associated with the AA content. Conclusions Our results confirmed loci identified from previous studies but also suggested that network approaches for studying AA contents in soybean seed are valuable. Three genomic regions (Chr 5: 41,754,397–41,893,109 bp, Chr 9: 1,537,829–1,806,586 bp, and Chr 20: 31,554,795–33,678,257 bp) were significantly identified by all three approaches. Yet, the majority of associations between a genomic region and an AA trait were approach- and/or environment-specific. Using a combination of approaches provides insights into the genetic control and pleiotropy among AA contents, which can be applied to mechanistic understanding of variation in AA content as well as tailored nutrition in cultivars developed from soybean breeding programs.