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Single-site isolated rhodium species anchored on zeolites or titanium dioxide are shown to catalyse the direct conversion of methane to methanol and acetic acid, using oxygen and carbon monoxide under mild conditions. Catalysing methane conversion The direct conversion of methane into liquid chemicals could be very valuable, but no catalysts are known that can do this efficiently and under mild conditions. Junjun Shan et al . now show that mononuclear rhodium species anchored on zeolite or titanium dioxide supports catalyse the direct conversion of methane to methanol and acetic acid, using oxygen and carbon monoxide and under mild conditions. Methanol and acetic acid, which are both widely used in the chemical industry, form through different pathways, so methane conversion can be tuned to produce either compound with selectivities of 70%–100%. The remarkable activity of the catalysts is still far from being technologically relevant, but could guide the development of next-generation catalysts and processes for directly converting methane into useful chemicals. An efficient and direct method of catalytic conversion of methane to liquid methanol and other oxygenates would be of considerable practical value. However, it remains an unsolved problem in catalysis, as typically it involves expensive 1 , 2 , 3 , 4 or corrosive oxidants or reaction media 5 , 6 , 7 , 8 that are not amenable to commercialization. Although methane can be directly converted to methanol using molecular oxygen under mild conditions in the gas phase, the process is either stoichiometric (and therefore requires a water extraction step) 9 , 10 , 11 , 12 , 13 , 14 , 15 or is too slow and low-yielding 16 to be practical. Methane could, in principle, also be transformed through direct oxidative carbonylation to acetic acid, which is commercially obtained through methane steam reforming, methanol synthesis, and subsequent methanol carbonylation on homogeneous catalysts 17 , 18 . However, an effective catalyst for the direct carbonylation of methane to acetic acid, which might enable the economical small-scale utilization of natural gas that is currently flared or stranded, has not yet been reported. Here we show that mononuclear rhodium species, anchored on a zeolite or titanium dioxide support suspended in aqueous solution, catalyse the direct conversion of methane to methanol and acetic acid, using oxygen and carbon monoxide under mild conditions. We find that the two products form through independent pathways, which allows us to tune the conversion: three-hour-long batch-reactor tests conducted at 150 degrees Celsius, using either the zeolite-supported or the titanium-dioxide-supported catalyst, yield around 22,000 micromoles of acetic acid per gram of catalyst, or around 230 micromoles of methanol per gram of catalyst, respectively, with selectivities of 60–100 per cent. We anticipate that these unusually high activities, despite still being too low for commercial application, may guide the development of optimized catalysts and practical processes for the direct conversion of methane to methanol, acetic acid and other useful chemicals.

The centenary of insulin discovery represents an important opportunity to transform diabetes from a fatal diagnosis into a medically manageable chronic condition. Insulin is a key peptide hormone and mediates the systemic glucose metabolism in different tissues. Insulin resistance (IR) is a disordered biological response for insulin stimulation through the disruption of different molecular pathways in target tissues. Acquired conditions and genetic factors have been implicated in IR. Recent genetic and biochemical studies suggest that the dysregulated metabolic mediators released by adipose tissue including adipokines, cytokines, chemokines, excess lipids and toxic lipid metabolites promote IR in other tissues. IR is associated with several groups of abnormal syndromes that include obesity, diabetes, metabolic dysfunction-associated fatty liver disease (MAFLD), cardiovascular disease, polycystic ovary syndrome (PCOS), and other abnormalities. Although no medication is specifically approved to treat IR, we summarized the lifestyle changes and pharmacological medications that have been used as efficient intervention to improve insulin sensitivity. Ultimately, the systematic discussion of complex mechanism will help to identify potential new targets and treat the closely associated metabolic syndrome of IR.

Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution. We present GraphST, a graph self-supervised contrastive learning method that fully exploits spatial transcriptomics data to outperform existing methods. It combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. We demonstrated GraphST on multiple tissue types and technology platforms. GraphST achieved 10% higher clustering accuracy and better delineated fine-grained tissue structures in brain and embryo tissues. GraphST is also the only method that can jointly analyze multiple tissue slices in vertical or horizontal integration while correcting batch effects. Lastly, GraphST demonstrated superior cell-type deconvolution to capture spatial niches like lymph node germinal centers and exhausted tumor infiltrating T cells in breast tumor tissue. Advances in spatial transcriptomics technologies have enabled the gene expression profiling of tissues while retaining spatial context. Here the authors present GraphST, a graph self-supervised contrastive learning method that learns informative and discriminative spot representations from spatial transcriptomics data.

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR’s ability to impute and denoise gene expression (URL: https://github.com/JinmiaoChenLab/SEDR/ ).

Semantic segmentation is a crucial task in computer vision with broad applications in autonomous driving, intelligent surveillance, drone vision, and other fields. The current high-precision segmentation models generally suffer from large parameter sizes, high computational complexity, and substantial memory consumption, which limits their efficient deployment in embedded systems and resource-constrained environments. In addition, traditional methods exhibit significant limitations in handling multi-scale targets and object boundaries, particularly during deep feature extraction, where the loss of shallow spatial information often results in blurred boundaries and reduced segmentation accuracy. To address these challenges, we propose EfficientSegNet, a lightweight and efficient semantic segmentation network. This network features an innovative architecture that integrates the Cascade-Attention Dense Field (CADF) module and the Dynamic Weighting Feature Fusion (DWF) module, effectively reducing computational resource requirements while balancing global semantic information and local detail recovery. Experimental results demonstrate that EfficientSegNet achieves an excellent balance between segmentation accuracy and computational efficiency on multiple public datasets, providing robust support for real-time segmentation tasks and applications on resource-constrained devices.

To study the influence of the geometric structure of the probe coil on the electromagnetic characteristics of the eddy current probe in the process of eddy current testing, based on the principle of eddy current testing, different probe coil models were established using finite element software. These geometric structure parameters include the difference between the inner and outer radius, thickness, and equivalent radius. The magnetic field distribution around the probe is simulated and analyzed under different parameters, and the detection performance of the probe is judged in combination with the change rate of the magnetic field around the probe coil. The simulation results show that at a closer position, increasing the difference between the inner and outer radii, reducing the thickness, and reducing the equivalent radius are beneficial to improve the resolution of the probe coil. At a far position, reducing the difference between the inner and outer radii, increasing the thickness, and reducing the equivalent radius are beneficial to improve the resolution of the probe coil. At the same time, the accuracy of the simulation data is verified by comparing the theoretical values with the simulated values under different conditions. Therefore, the obtained conclusions can provide a reference and basis for the optimal design of the probe structure.

Complete and accurate reference genomes and annotations provide fundamental resources for functional genomics and crop breeding. Here we report a de novo assembly and annotation of a pea cultivar ZW6 with contig N50 of 8.98 Mb, which features a 243-fold increase in contig length and evident improvements in the continuity and quality of sequence in complex repeat regions compared with the existing one. Genome diversity of 118 cultivated and wild pea demonstrated that Pisum abyssinicum is a separate species different from P. fulvum and P. sativum within Pisum. Quantitative trait locus analyses uncovered two known Mendel’s genes related to stem length (Le/le) and seed shape (R/r) as well as some candidate genes for pod form studied by Mendel. A pan-genome of 116 pea accessions was constructed, and pan-genes preferred in P. abyssinicum and P. fulvum showed distinct functional enrichment, indicating the potential value of them as pea breeding resources in the future.

Oxidative dispersion has been widely used in regeneration of sintered metal catalysts and fabrication of single atom catalysts, which is attributed to an oxidation-induced dispersion mechanism. However, the interplay of gas-metal-support interaction in the dispersion processes, especially the gas-metal interaction has not been well illustrated. Here, we show dynamic dispersion of silver nanostructures on silicon nitride surface under reducing/oxidizing conditions and during carbon monoxide oxidation reaction. Utilizing environmental scanning (transmission) electron microscopy and near-ambient pressure photoelectron spectroscopy/photoemission electron microscopy, we unravel a new adsorption-induced dispersion mechanism in such a typical oxidative dispersion process. The strong gas-metal interaction achieved by chemisorption of oxygen on nearly-metallic silver nanoclusters is the internal driving force for dispersion. In situ observations show that the dispersed nearly-metallic silver nanoclusters are oxidized upon cooling in oxygen atmosphere, which could mislead to the understanding of oxidation-induced dispersion. We further understand the oxidative dispersion mechanism from the view of dynamic equilibrium taking temperature and gas pressure into account, which should be applied to many other metals such as gold, copper, palladium, etc. and other reaction conditions. Designing ultra small metal nanoclusters or single atoms with metallic state is a challenge. Here, the authors demonstrate the stabilization of ultra small silver clusters in the nearly-metallic state by oxygen adsorption at high temperature, using in situ spectroscopy and microscope technologies.

Background Although, micropeptides encoded by non-coding RNA have been shown to have an important role in a variety of tumors processes, there have been no reports on micropeptide in renal cell carcinoma (RCC). Based on the micropeptide MIAC (micropeptide inhibiting actin cytoskeleton) discovered and named in the previous work, this study screened its tumor spectrum, and explored its mechanism of action and potential diagnosis and treatment value in the occurrence and development of renal carcinoma. Methods The clinical significance of MIAC in RCC was explored by bioinformatics analysis through high-throughput RNA-seq data from 530 patients with kidney renal clear cell carcinoma (KIRC) in the TCGA database, and the detection of clinical samples of 70 cases of kidney cancer. In vitro and in vivo experiments to determine the role of MIAC in renal carcinoma cell growth and metastasis; High-throughput transcriptomics, western blotting, immunoprecipitation, molecular docking, affinity experiments, and Streptavidin pulldown experiments identify MIAC direct binding protein and key regulatory pathways. Results The analysis of 600 renal carcinoma samples from different sources revealed that the expression level of MIAC is significantly decreased, and corelated with the prognosis and clinical stage of tumors in patients with renal carcinoma. Overexpression of MIAC in renal carcinoma cells can significantly inhibit the proliferation and migration ability, promote apoptosis of renal carcinoma cells, and affect the distribution of cells at various stages. After knocking down MIAC, the trend is reversed. In vivo experiments have found that MIAC overexpression inhibit the growth and metastasis of RCC, while the synthetized MIAC peptides can significantly inhibit the occurrence and development of RCC in vitro and in vivo. Further mechanistic studies have demonstrated that MIAC directly bind to AQP2 protein, inhibit EREG/EGFR expression and activate downstream pathways PI3K/AKT and MAPK to achieve anti-tumor effects. Conclusions This study revealed for the first time the tumor suppressor potential of the lncRNA-encoded micropeptide MIAC in RCC, which inhibits the activation of the EREG/EGFR signaling pathway by direct binding to AQP2 protein, thereby inhibiting renal carcinoma progression and metastasis. This result emphasizes that the micropeptide MIAC can provide a new strategy for the diagnosis and treatment of RCC.

For the state estimation problem of a multi-source localization nonlinear system with unknown and bounded noise, a distributed sequential ellipsoidal intersection fusion estimation algorithm based on the dual set-membership filtering method is proposed to ensure the reliability of the localization system. First, noise with unknown and bounded characteristics is modeled by using bounded ellipsoidal regions. At the same time, local estimators are designed at the sensor link nodes to filter out the noise interference in the localization system. The local estimator is designed using the dual set-membership filtering algorithm. It uses the dual principle to find the minimizing ellipsoid that can contain the nonlinear function by solving the optimization problem with semi-infinite constraints, and a first-order conditional gradient algorithm is used to solve the optimization problem with a low computational complexity. Meanwhile, the communication confusion among multiple sensors causes the problem of unknown correlation. The obtained estimates of local filters are fused at the fusion center by designing a distributed sequential ellipsoid intersection fusion estimation algorithm to obtain more accurate fusion localization results with lower computational cost. Finally, the stability and reliability of the proposed distributed fusion algorithm are verified by designing a simulation example of a multi-source nonlinear system.