Carbon-Extraction-Induced Biaxial Strain Tuning of Carbon-Intercalated Iridium Metallene for Hydrogen Evolution Catalysis.

Metallene materials with atomic thicknesses are receiving increasing attention in electrocatalysis due to ultrahigh surface areas and distinctive surface strain. However, the continuous strain regulation of metallene remains a grand challenge. Herein, taking advantage of autocatalytic reduction of Cu2+ on biaxially strained, carbon-intercalated Ir metallene, we achieve control over the carbon extraction kinetics, enabling fine regulation of carbon intercalation concentration and continuous tuning of (111) in-plane (-2.0%-2.6%) and interplanar (3.5%-8.8%) strains over unprecedentedly wide ranges. Electrocatalysis measurements reveal the strain-dependent activity toward hydrogen evolution reaction (HER), where weakly strained Ir metallene (w-Ir metallene) with the smallest lattice constant presents the highest mass activity of 2.89 A mg-1Ir at -0.02 V vs reversible hydrogen electrode (RHE). Theoretical calculations validated the pivotal role of lattice compression in optimizing H binding on carbon-intercalated Ir metallene surfaces by downshifting the d-band center, further highlighting the significance of strain engineering for boosted electrocatalysis.

Phase-Tailoring Wx V1-x O2 Meta-Nanofiber Enables Temperature-Editing Energy Control.

Room-temperature phase change materials (RTPCMs) exhibit promise to address challenges in thermal energy storage and release, greatly aiding in numerous domains of human existence and productivity. The conventional RTPCMs undergo inevitable volume expansion, structural collapse, and diffusion of active ingredients while maintaining desirable phase change enthalpy and ideal phase change temperature. Here, a sol-gel 1D-induced growth approach is presented to fabricate meta nanofibers (Meta-NFs) comprised of vanadium dioxide with monoclinic crystal structure, and further achieve the editable phase change temperature from 68 to 37 °C through W-doping, which allowed for tailored length variation of the zigzag V-V bond. Subsequently, Meta-NFs are assembled into 3D aerogels with self-standing architecture, thereby enabling the independent use of the RTPCMs. The obtained metamaterials demonstrate not only the temperature-editing solid-solid phase transition, but also the stiffness of the ceramic matrix, exhibiting the thermal energy control capability at room temperature (37 °C), thermal insulation properties, temperature resistance, and flame retardancy. The effective creation of these fascinating metamaterials might offer new insights for next-generation and self-standing solid-solid RTPCMs.

Binding peptide generation for MHC Class I proteins with deep reinforcement learning.

MOTIVATION: MHC Class I protein plays an important role in immunotherapy by presenting immunogenic peptides to anti-tumor immune cells. The repertoires of peptides for various MHC Class I proteins are distinct, which can be reflected by their diverse binding motifs. To characterize binding motifs for MHC Class I proteins, in vitro experiments have been conducted to screen peptides with high binding affinities to hundreds of given MHC Class I proteins. However, considering tens of thousands of known MHC Class I proteins, conducting in vitro experiments for extensive MHC proteins is infeasible, and thus a more efficient and scalable way to characterize binding motifs is needed. RESULTS: We presented a de novo generation framework, coined PepPPO, to characterize binding motif for any given MHC Class I proteins via generating repertoires of peptides presented by them. PepPPO leverages a reinforcement learning agent with a mutation policy to mutate random input peptides into positive presented ones. Using PepPPO, we characterized binding motifs for around 10 000 known human MHC Class I proteins with and without experimental data. These computed motifs demonstrated high similarities with those derived from experimental data. In addition, we found that the motifs could be used for the rapid screening of neoantigens at a much lower time cost than previous deep-learning methods. AVAILABILITY AND IMPLEMENTATION: The software can be found in https://github.com/minrq/pMHC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cross-Coupling of Benzylic and Aldehydic C-H Bonds via Photocatalytic Tandem Radical-Radical Coupling and Acceptorless Alcohol Dehydrogenation.

The construction of a C-C bond by cross-coupling of two different C-H bonds with the release of hydrogen gas represents an ideal yet challenging bond formation strategy. Herein, we report a photocatalytic metal-free cross-coupling of benzylic and aldehydic C-H bonds by synergistic catalysis of organophotocatalyst 4CzIPN and a thiol, which affords the corresponding α-aryl ketones in acceptable yields along with hydrogen evolution. The mechanistic investigation indicates a radical-radical coupling to give an intermediary alcohol, followed by an acceptorless alcohol dehydrogenation.

Analysis of landfill leachate promoting efficient application of weathered coal anaerobic fermentation.

This research aimed to develop a new method for clean utilization and treatment of landfill leachate and solid waste weathered coal. Landfill leachate and weathered coal were adopted for combined anaerobic fermentation for methane production. The characteristics of microbial community, mechanism of biological methane production, and utilization characteristics of fermentation broth and solid residue for co-fermentation were analyzed through metagenomics, soluble organic matter detection and thermogravimetric (TG) analysis. The obtained results revealed that combined anaerobic fermentation increased methane production by 80.1%. Syntrophomonas, Salipiger, Methanosaeta and Methanothrix were highly correlated. Gene abundances of 2-oxoacid ferredoxin oxidoreductase and enolase were increased in methane conversion pathway mainly by acetic acid. Pyruvate-ferroredoxin oxidoreductase, 2-oxoglutarate synthase and succinate dehydrogenase acetate synthase intensified electron transfer pathways among microorganisms. Fulvic acid, tyrosine and tryptophan contents were high in fermentation broth. Volatile decomposition temperature, ignition point and residual char combustion temperature of residual coal were decreased and combustion was more stable. The obtained results showed that the co-fermentation of landfill leachate and weathered coal improved biological methane gas production, degraded weathered coal and improved combustion performance, which provided a new idea for weathered coal clean utilization.

Study on the mechanism of action of methane production by co-fermentation of sludge and lignite.

To improve the methanogenic efficiency of lignite anaerobic fermentation and explore innovative approaches to sludge utilization, a co-fermentation technique involving lignite and sludge was employed for converting biomass into biomethane. Volatile suspended solids were introduced as a native enrichment of the sludge and mixed with lignite for fermentation. The synergistic fermentation mechanism between sludge and lignite for biomethane production was analyzed through biochemical methane potential experiments, measurement of various parameters pre- and post-fermentation, observation of bacterial population changes during the peak of reaction, carbon migration assessment, and evaluation of rheological characteristics. The results showed that the addition of sludge in the anaerobic fermentation process improved the microorganisms' ability to degrade lignite and bolstered biomethane production. Notably, the maximum methane production recorded was 215.52 mL/g-volatile suspended solids, achieved at a sludge to coal ratio of 3:1, with a synergistic growth rate of 25.37%. Furthermore, the removal rates of total suspended solids, and total chemical oxygen demand exhibited an upward trend with an increasing percentage of sludge in the mixture. The relative abundance and activity of the methanogens population were found to increase with an appropriate ratio of sludge to lignite. This observation confirmed the migration of carbon between the solid-liquid-gas phases, promoting enhanced system affinity. Additionally, the changes in solid-liquid phase parameters before and after the reaction indicated that the addition of sludge improved the system's degradation capacity. The results of the study hold significant implications in realizing the resource utilization of sludge and lignite while contributing to environmental protection endeavors.

Phase-Engineered VO2 Metal Nanofiber Enables a Metal-Insulator Transition for Bidirectional Thermal Reallocation.

Phase change materials (PCMs) are appealing for their fascinating capability of thermal reallocation, assisting widely in many areas of human productivity and life. However, it has remained a significant challenge to attain shape stability, temperature resistance, and microscale continuity in PCMs while maintaining sufficient phase change performance. Here we report a sol epitaxial fabrication strategy to create metal-insulator transition nanofibers (MIT-NFs) composed of monoclinic vanadium dioxide. The MIT-NFs are further assembled into self-standing two-dimensional membranes and three-dimensional aerogels with structural robustness. The resulting series of metal-insulator transition materials exhibits the integrated features of solid-solid phase change properties, shape stability, and thermal reallocation properties. The integral ceramic characteristic also provides the MIT-NFs with surface stiffness (54 GPa), temperature resistance (-196° to 330 °C), and thermal insulator properties. The successful fabrication of these captivating MIT materials may provide new perspectives for next-generation, shape-stable, and self-standing PCMs.

Low-Electronegativity Mn-Contraction of PtMn Nanodendrites Boosts Oxygen Reduction Durability.

Platinum metal (PtM, M=Ni, Fe, Co) alloys catalysts show high oxygen reduction reaction (ORR) activity due to their well-known strain and ligand effects. However, these PtM alloys usually suffer from a deficient ORR durability in acidic environment as the alloyed metal is prone to be dissolved due to its high electronegativity. Herein, we report a new class of PtMn alloy nanodendrite catalyst with low-electronegativity Mn-contraction for boosting the oxygen reduction durability of fuel cells. The moderate strain in PtMn, induced by Mn contraction, yields optimal oxygen reduction activity at 0.53 A mg-1 at 0.9 V versus reversible hydrogen electrode (RHE). Most importantly, we show that relative to well-known high-electronegativity Ni-based Pt alloy counterpart, the PtMn nanodendrite catalyst experiences less transition metals' dissolution in acidic solution and achieves an outstanding mass activity retention of 96 % after 10,000 degradation cycles. Density functional theory calculation reveals that PtMn alloys are thermodynamically more stable than PtNi alloys in terms of formation enthalpy and cohesive energy. The PtMn nanodendrite-based membrane electrode assembly delivers an outstanding peak power density of 1.36 W cm-2 at a low Pt loading and high-performance retention over 50 h operations at 0.6 V in H2 -O2 hydrogen fuel cells.

Chemoselective and Enantioselective Fluorescent Recognition of Prolinol.

A highly chemoselective and enantioselective fluorescent probe has been discovered for the recognition of prolinol among various primary and secondary amine-based amino alcohols. The mechanistic studies including 1D and 2D 1H/13C NMR and mass spectroscopic analyses and DFT calculations have shown that the aldehyde group of the probe can react with prolinol to generate a bicyclic oxazolidine unit which, through a possible intramolecular hydrogen bond interaction, will lead to highly selective fluorescence enhancement.

Rhodium(III)-catalyzed C-H alkylation of arylhydrophthalazinediones with α-Cl ketones as sp3-carbon alkylated agents.

A Rh(III)-catalyzed C-H bond direct alkylation between 2-arylphthalazine-1,4-diones and α-Cl ketones, which are sp3-carbon synthons, under mild conditions has been disclosed. The corresponding phthalazine derivatives are readily obtained in moderate to excellent yields with a wide range of substrates and high functional group tolerance. The practicality and utility of this method are demonstrated by the derivatization of the product.

Impact of Biomass Burning Organic Aerosol Volatility on Smoke Concentrations Downwind of Fires.

Biomass burning particulate matter (BBPM) affects regional air quality and global climate, with impacts expected to continue to grow over the coming years. We show that studies of North American fires have a systematic altitude dependence in measured BBPM normalized excess mixing ratio (NEMR; ΔPM/ΔCO), with airborne and high-altitude studies showing a factor of 2 higher NEMR than ground-based measurements. We report direct airborne measurements of BBPM volatility that partially explain the difference in the BBPM NEMR observed across platforms. We find that when heated to 40-45 °C in an airborne thermal denuder, 19% of lofted smoke PM1 evaporates. Thermal denuder measurements are consistent with evaporation observed when a single smoke plume was sampled across a range of temperatures as the plume descended from 4 to 2 km altitude. We also demonstrate that chemical aging of smoke and differences in PM emission factors can not fully explain the platform-dependent differences. When the measured PM volatility is applied to output from the High Resolution Rapid Refresh Smoke regional model, we predict a lower PM NEMR at the surface compared to the lofted smoke measured by aircraft. These results emphasize the significant role that gas-particle partitioning plays in determining the air quality impacts of wildfire smoke.

A color-change fluorescence sensor for oleanolic acid based on chiral camphanic decorated bis-cyanostilbene.

Although various fluorescent sensors for biomolecules had been extensively reported, the effective fluorescent sensor was seldom reported for detecting oleanolic acid up to now. This work reports the first color-change fluorescence sensor for oleanolic acid based on a bridging bis-cyanostilbene derivative with chiral camphanic groups (C-BCS). C-BCS possessed the chartreuse fluorescence in aqueous media, which transferred to strong blue fluorescence in the presence of oleanolic acid. This sensing ability of C-BCS for oleanolic acid exhibited the high selectivity among all kinds of biomolecules and ions. The good linearity between the fluorescence intensity and concentration of oleanolic acid was acquired in the range of 0.2 × 10-6 to 8.0 × 10-6 M with the detecting limitation of 0.0582 µM. The 1:1 binding process was clarified as oleanolic acid located in the opening cavity composed of two bridging cyanostilbene units and two chiral camphanic groups based on multiple hydrogen bonds and hydrophobic interaction. The detecting ability of C-BCS was applied on sensing oleanolic acid in thin-layer chromatography analysis, imprinting experiment, tap water, and tea samples, suggesting the effective on-site sensing abilities of C-BCS for oleanolic acid in real samples and daily life.

Applications of radiomics-based analysis pipeline for predicting epidermal growth factor receptor mutation status.

BACKGROUND: This study aimed to develop a pipeline for selecting the best feature engineering-based radiomic path to predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). METHODS: The study enrolled 115 lung adenocarcinoma patients with EGFR mutation status from June 2016 and September 2017. We extracted radiomics features by delineating regions-of-interest around the entire tumor in 18F-FDG PET/CT images. The feature engineering-based radiomic paths were built by combining various methods of data scaling, feature selection, and many methods for predictive model-building. Next, a pipeline was developed to select the best path. RESULTS: In the paths from CT images, the highest accuracy was 0.907 (95% confidence interval [CI]: 0.849, 0.966), the highest area under curve (AUC) was 0.917 (95% CI: 0.853, 0.981), and the highest F1 score was 0.908 (95% CI: 0.842, 0.974). In the paths based on PET images, the highest accuracy was 0.913 (95% CI: 0.863, 0.963), the highest AUC was 0.960 (95% CI: 0.926, 0.995), and the highest F1 score was 0.878 (95% CI: 0.815, 0.941). Additionally, a novel evaluation metric was developed to evaluate the comprehensive level of the models. Some feature engineering-based radiomic paths obtained promising results. CONCLUSIONS: The pipeline is capable of selecting the best feature engineering-based radiomic path. Combining various feature engineering-based radiomic paths could compare their performances and identify paths built with the most appropriate methods to predict EGFR-mutant lung adenocarcinoma in 18FDG PET/CT. The pipeline proposed in this work can select the best feature engineering-based radiomic path.

Metagenomic characterization of biomethane transformation by lipid-catalyzed anaerobic fermentation of lignite.

The present study aims at using lipid in a novel way to improve the efficiency of methane production from lignite anaerobic digestion. The obtained results showed an increase by 3.13 times of the cumulative biomethane content of lignite anaerobic fermentation, when 1.8 g lipid was added. The gene expression of functional metabolic enzymes was also found to be enhanced during the anaerobic fermentation. Moreover, the enzymes related to fatty acid degradation such as long-chain Acyl-CoA synthetase and Acyl-CoA dehydrogenase were increased by 1.72 and 10.48 times, respectively, which consequently, accelerated the conversion of fatty acid. Furthermore, the addition of lipid enhanced the carbon dioxide trophic and acetic acid trophic metabolic pathways. Hence, the addition of lipids was argued to promote the production of methane from lignite anaerobic fermentation, which provided a new insight for the conversion and utilization of lipid waste.

Properties of protein unfolded states suggest broad selection for expanded conformational ensembles.

Much attention is being paid to conformational biases in the ensembles of intrinsically disordered proteins. However, it is currently unknown whether or how conformational biases within the disordered ensembles of foldable proteins affect function in vivo. Recently, we demonstrated that water can be a good solvent for unfolded polypeptide chains, even those with a hydrophobic and charged sequence composition typical of folded proteins. These results run counter to the generally accepted model that protein folding begins with hydrophobicity-driven chain collapse. Here we investigate what other features, beyond amino acid composition, govern chain collapse. We found that local clustering of hydrophobic and/or charged residues leads to significant collapse of the unfolded ensemble of pertactin, a secreted autotransporter virulence protein from Bordetella pertussis, as measured by small angle X-ray scattering (SAXS). Sequence patterns that lead to collapse also correlate with increased intermolecular polypeptide chain association and aggregation. Crucially, sequence patterns that support an expanded conformational ensemble enhance pertactin secretion to the bacterial cell surface. Similar sequence pattern features are enriched across the large and diverse family of autotransporter virulence proteins, suggesting sequence patterns that favor an expanded conformational ensemble are under selection for efficient autotransporter protein secretion, a necessary prerequisite for virulence. More broadly, we found that sequence patterns that lead to more expanded conformational ensembles are enriched across water-soluble proteins in general, suggesting protein sequences are under selection to regulate collapse and minimize protein aggregation, in addition to their roles in stabilizing folded protein structures.

Quantitative detection of iodine in the stratosphere.

Oceanic emissions of iodine destroy ozone, modify oxidative capacity, and can form new particles in the troposphere. However, the impact of iodine in the stratosphere is highly uncertain due to the lack of previous quantitative measurements. Here, we report quantitative measurements of iodine monoxide radicals and particulate iodine (Iy,part) from aircraft in the stratosphere. These measurements support that 0.77 ± 0.10 parts per trillion by volume (pptv) total inorganic iodine (Iy) is injected to the stratosphere. These high Iy amounts are indicative of active iodine recycling on ice in the upper troposphere (UT), support the upper end of recent Iy estimates (0 to 0.8 pptv) by the World Meteorological Organization, and are incompatible with zero stratospheric iodine injection. Gas-phase iodine (Iy,gas) in the UT (0.67 ± 0.09 pptv) converts to Iy,part sharply near the tropopause. In the stratosphere, IO radicals remain detectable (0.06 ± 0.03 pptv), indicating persistent Iy,part recycling back to Iy,gas as a result of active multiphase chemistry. At the observed levels, iodine is responsible for 32% of the halogen-induced ozone loss (bromine 40%, chlorine 28%), due primarily to previously unconsidered heterogeneous chemistry. Anthropogenic (pollution) ozone has increased iodine emissions since preindustrial times (ca. factor of 3 since 1950) and could be partly responsible for the continued decrease of ozone in the lower stratosphere. Increasing iodine emissions have implications for ozone radiative forcing and possibly new particle formation near the tropopause.

Genetic diversity and phylogenetic relationship estimation of Shanxi indigenous goat breeds using microsatellite markers.

The objective of this study was to assess the genetic diversity, phylogenetic relationship and population structure of five goat breeds in Shanxi, China. High genetic diversities were found in the five populations, among which, Licheng big green goat (LCBG) has the highest genetic diversity, while Jinlan cashmere goat (JLCG) population has the lowest genetic diversity. Bottleneck analysis showed the absence of recent genetic bottlenecks in the five goat populations. Genetic differentiation analysis shows that the closest genetic relationship between LCBG and LLBG (Lvliang black goat) was found, and the genetic distance between JLCG and the other four populations is the largest. The population structure of JLCG is different from the other four populations with K = 2, while LCBG and LLBG have high similarity population structure as the K value changes. Knowledge about genetic diversity and population structure of indigenous goats is essential for genetic improvement, understanding of environmental adaptation as well as utilization and conservation of goat breeds.

Analysis on methane production from various coal slime fermentations based on metagenomics.

Metagenomic sequencing technology was applied to evaluate differences in the anaerobic fermentation process of coal slimes by analyzing microbial diversity, functional activity structure, and cooperative relationship during the anaerobic fermentation of coal slimes with different coal ranks. The obtained results showed that the production of biomethane from coal slime was decreased by increasing metamorphism degree. Internal reason was higher abundance of microbial community in low rank coal slimes compared to that in high rank coal which had higher activity in the gene expression of key steps such as hydrolysis and acidification, methanation and the production of hydrogen and acetic acid. Acetic acid decarboxylation and CO2 reduction are two key pathways of methanation process. At the same time, K11261 (formylmethanofuran dehydrogenase subunit) and K01499 (methenyltetrahydromethanopterin cyclohydrolase) genes were further enriched in low rank slime systems, which enhanced the proportion of CO2 reduction in methanation pathway and was beneficial to biomethane production. Research revealed the roles of different coal slime ranks in biomethane production process and is considered as an important reference significance for further exploration of coal slime resource utilization.

[Deep learning method for magnetic resonance imaging fluid-attenuated inversion recovery image synthesis].

Magnetic resonance imaging(MRI) can obtain multi-modal images with different contrast, which provides rich information for clinical diagnosis. However, some contrast images are not scanned or the quality of the acquired images cannot meet the diagnostic requirements due to the difficulty of patient's cooperation or the limitation of scanning conditions. Image synthesis techniques have become a method to compensate for such image deficiencies. In recent years, deep learning has been widely used in the field of MRI synthesis. In this paper, a synthesis network based on multi-modal fusion is proposed, which firstly uses a feature encoder to encode the features of multiple unimodal images separately, and then fuses the features of different modal images through a feature fusion module, and finally generates the target modal image. The similarity measure between the target image and the predicted image in the network is improved by introducing a dynamic weighted combined loss function based on the spatial domain and K-space domain. After experimental validation and quantitative comparison, the multi-modal fusion deep learning network proposed in this paper can effectively synthesize high-quality MRI fluid-attenuated inversion recovery (FLAIR) images. In summary, the method proposed in this paper can reduce MRI scanning time of the patient, as well as solve the clinical problem of missing FLAIR images or image quality that is difficult to meet diagnostic requirements.

Airborne Emission Rate Measurements Validate Remote Sensing Observations and Emission Inventories of Western U.S. Wildfires.

Carbonaceous emissions from wildfires are a dynamic mixture of gases and particles that have important impacts on air quality and climate. Emissions that feed atmospheric models are estimated using burned area and fire radiative power (FRP) methods that rely on satellite products. These approaches show wide variability and have large uncertainties, and their accuracy is challenging to evaluate due to limited aircraft and ground measurements. Here, we present a novel method to estimate fire plume-integrated total carbon and speciated emission rates using a unique combination of lidar remote sensing aerosol extinction profiles and in situ measured carbon constituents. We show strong agreement between these aircraft-derived emission rates of total carbon and a detailed burned area-based inventory that distributes carbon emissions in time using Geostationary Operational Environmental Satellite FRP observations (Fuel2Fire inventory, slope = 1.33 ± 0.04, r2 = 0.93, and RMSE = 0.27). Other more commonly used inventories strongly correlate with aircraft-derived emissions but have wide-ranging over- and under-predictions. A strong correlation is found between carbon monoxide emissions estimated in situ with those derived from the TROPOspheric Monitoring Instrument (TROPOMI) for five wildfires with coincident sampling windows (slope = 0.99 ± 0.18; bias = 28.5%). Smoke emission coefficients (g MJ-1) enable direct estimations of primary gas and aerosol emissions from satellite FRP observations, and we derive these values for many compounds emitted by temperate forest fuels, including several previously unreported species.