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At least 1,531 records · Page 85

Active predation, phylogenetic diversity, and global prevalence of myxobacteria in wastewater treatment plants

The operation of modern wastewater treatment plants (WWTPs) is driven by activated sludge microbiota, a complex assemblage of trophically interacting microorganisms. Microbial predation is crucial to fundamental understanding of how biological interactions drive microbiome structuring and functioning of WWTPs. However, predatory bacteria have received little attention regarding their diversity, activity, and ecological function in activated sludge, limiting the exploitation of food web interactions for wastewater microbiome engineering. Here, by using rRNA-stable isotope probing of activated sludge microbiota with 13 C-labeled prey bacteria, we uncovered diverse as-yet-uncultivated putative predatory bacteria that actively incorporated 13 C-biomass. Myxobacteria, especially Haliangium and the mle1-27 clade, were found as the dominant active predators, refreshing conventional views based on a few predatory isolates of Bdellovibrionota from WWTPs. The identified predatory bacteria showed more selective predation on prey compared with the protists dominated by ciliates, providing in situ evidence for inter-domain predation behavior divergence in activated sludge. Putative predatory bacteria were tracked over a two-year microbiome monitoring effort at a local WWTP, revealing the predominance of Myxococcota (6.5 ± 1.3%) over Bdellovibrionota (1.0 ± 0.2%) lineages. Phylogenetic analysis unveiled highly diverse myxobacteria inhabiting activated sludge and suggested a habitat filtering effect in global WWTPs. Further mining of a global activated sludge microbiome dataset revealed the prevalence of Myxococcota (5.4 ± 0.1%) species and potential impacts of myxobacterial predation on process performance. Collectively, our findings provided unique insights into the predating activity, diversity, and prevalence of Myxococcota species in activated sludge, highlighting their links with wastewater treatment processes via trophic regulation of enteric and functional bacteria.

59 BASIC BIOLOGICAL SCIENCES↗

Sequence-selective dynamic covalent assembly of information-bearing oligomers

Relatively robust dynamic covalent interactions have been employed extensively to mediate molecular self-assembly reactions; however, these assembly processes often do not converge to a thermodynamic equilibrium, instead yielding mixtures of kinetically-trapped species. Here, we report a dynamic covalent self-assembly process that mitigates kinetic trapping such that multiple unique oligomers bearing covalently coreactive pendant groups are able to undergo simultaneous, sequence-selective hybridization with their complementary strands to afford biomimetic, in-registry molecular ladders with covalent rungs. Analogous to the thermal cycling commonly employed for nucleic acid melting and annealing, this is achieved by raising and lowering the concentration of a multi-role reagent to effect quantitative dissociation and subsequently catalyze covalent bond rearrangement, affording selective assembly of the oligomeric sequences. The hybridization specificity afforded by this process further enabled information encoded in oligomers to be retrieved through selective hybridization with complementary, mass-labeled sequences.

36 MATERIALS SCIENCE↗

The electron affinity of astatine

One of the most important properties influencing the chemical behavior of an element is the electron affinity (EA). Among the remaining elements with unknown EA is astatine, where one of its isotopes, 211 At, is remarkably well suited for targeted radionuclide therapy of cancer. With the At - anion being involved in many aspects of current astatine labeling protocols, the knowledge of the electron affinity of this element is of prime importance. Here we report the measured value of the EA of astatine to be 2.41578(7) eV. This result is compared to state-of-the-art relativistic quantum mechanical calculations that incorporate both the Breit and the quantum electrodynamics (QED) corrections and the electron–electron correlation effects on the highest level that can be currently achieved for many-electron systems. The developed technique of laser-photo detachment spectroscopy of radioisotopes opens the path for future EA measurements of other radioelements such as polonium, and eventually super-heavy elements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Hormonal contraception alters vaginal microbiota and cytokines in South African adolescents in a randomized trial

Young women in sub-Saharan Africa are disproportionally affected by HIV infection and unintended pregnancies. However, hormonal contraceptive (HC) use may influence HIV risk through changes in genital tract microbiota and inflammatory cytokines. To investigate this, 130 HIV negative adolescent females aged 15–19 years were enrolled into a substudy of UChoose, an open-label randomized crossover study (NCT02404038), comparing acceptability and contraceptive product preference as a proxy for HIV prevention delivery methods. Participants were randomized to injectable norethisterone enanthate (Net-En), combined oral contraceptives (COC) or etonorgesterol/ethinyl estradiol combined contraceptive vaginal ring (CCVR) for 16 weeks, then crossed over to another HC for 16 weeks. Cervicovaginal samples were collected at baseline, crossover and exit for characterization of the microbiota and measurement of cytokine levels; primary endpoints were cervical T cell activation, vaginal microbial diversity and cytokine concentrations. Adolescents randomized to COCs had lower vaginal microbial diversity and relative abundance of HIV risk-associated taxa compared to Net-En or CCVR. Cervicovaginal inflammatory cytokine concentrations were significantly higher in adolescents randomized to CCVR compared to COC and Net-En. This suggests that COC use may induce an optimal vaginal ecosystem by decreasing bacterial diversity and inflammatory taxa, while CCVR use is associated with genital inflammation.

59 BASIC BIOLOGICAL SCIENCES↗

Adsorption separation of heavier isotope gases in subnanometer carbon pores

Isotopes of heavier gases including carbon ( 13 C/ 14 C), nitrogen ( 13 N), and oxygen ( 18 O) are highly important because they can be substituted for naturally occurring atoms without significantly perturbing the biochemical properties of the radiolabelled parent molecules. These labelled molecules are employed in clinical radiopharmaceuticals, in studies of brain disease and as imaging probes for advanced medical imaging techniques such as positron-emission tomography (PET). Established distillation-based isotope gas separation methods have a separation factor ( S ) below 1.05 and incur very high operating costs due to high energy consumption and long processing times, highlighting the need for new separation technologies. Here, we show a rapid and highly selective adsorption-based separation of 18 O 2 from 16 O 2 with S above 60 using nanoporous adsorbents operating near the boiling point of methane (112 K), which is accessible through cryogenic liquefied-natural-gas technology. A collective-nuclear-quantum effect difference between the ordered 18 O 2 and 16 O 2 molecular assemblies confined in subnanometer pores can explain the observed equilibrium separation and is applicable to other isotopic gases.

36 MATERIALS SCIENCE↗

Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning

Label-free vibrational imaging by stimulated Raman scattering (SRS) provides unprecedented insight into real-time chemical distributions. Specifically, SRS in the fingerprint region (400–1800 cm -1 ) can resolve multiple chemicals in a complex bio-environment. However, due to the intrinsic weak Raman cross-sections and the lack of ultrafast spectral acquisition schemes with high spectral fidelity, SRS in the fingerprint region is not viable for studying living cells or large-scale tissue samples. Here, we report a fingerprint spectroscopic SRS platform that acquires a distortion-free SRS spectrum at 10 cm -1 spectral resolution within 20 µs using a polygon scanner. Meanwhile, we significantly improve the signal-to-noise ratio by employing a spatial-spectral residual learning network, reaching a level comparable to that with 100 times integration. Collectively, our system enables high-speed vibrational spectroscopic imaging of multiple biomolecules in samples ranging from a single live microbe to a tissue slice.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Designing efficient genetic code expansion in Bacillus subtilis to gain biological insights

Abstract Bacillus subtilis is a model gram-positive bacterium, commonly used to explore questions across bacterial cell biology and for industrial uses. To enable greater understanding and control of proteins in B. subtilis , here we report broad and efficient genetic code expansion in B. subtilis by incorporating 20 distinct non-standard amino acids within proteins using 3 different families of genetic code expansion systems and two choices of codons. We use these systems to achieve click-labelling, photo-crosslinking, and translational titration. These tools allow us to demonstrate differences between E. coli and B. subtilis stop codon suppression, validate a predicted protein-protein binding interface, and begin to interrogate properties underlying bacterial cytokinesis by precisely modulating cell division dynamics in vivo. We expect that the establishment of this simple and easily accessible chemical biology system in B. subtilis will help uncover an abundance of biological insights and aid genetic code expansion in other organisms.

59 BASIC BIOLOGICAL SCIENCES↗

Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes

Accurate 3D representations of lithium-ion battery electrodes, in which the active particles, binder and pore phases are distinguished and labeled, can assist in understanding and ultimately improving battery performance. Here, we demonstrate a methodology for using deep-learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast. We implement the 3D U-Net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how synthetic learning data, consisting of realistic artificial electrode structures and their tomographic reconstructions, can be generated and used to enhance network performance. We apply our method to segment x-ray tomographic microscopy images of graphite-silicon composite electrodes and show it is accurate across standard metrics. We then apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation.

25 ENERGY STORAGE↗

Skeletal Ni electrode-catalyzed C-O cleavage of diaryl ethers entails direct elimination via benzyne intermediates

Diaryl ethers undergo electrocatalytic hydrogenolysis (ECH) over skeletal Ni cathodes in a mild, aqueous process that achieves direct C-O cleavage without initial benzene ring saturation. Mechanistic studies find that aryl phenyl ethers with a single para or meta functional group (methyl, methoxy, or hydroxy) are selectively cleaved to the substituted benzene and phenol, in contrast to recently reported homogeneous catalytic cleavage processes. Ortho positioning of substituents reverses this C-O bond selectivity, except for the 2-phenoxyphenol case. Together with isotope labeling and co-solvent studies, these results point to two distinct cleavage mechanisms: (a) dual-ring coordination and C-H activation, leading to vicinal elimination to form phenol and a surface-bound aryne intermediate which is then hydrogenated and released as the arene; and (b) surface binding in keto form by the phenolic ring of the hydroxy-substituted substrates, followed by direct displacement of the departing phenol. Notably, acetone inhibits the well-known reduction of phenol to cyclohexanol, affording control of product ring saturation. A byproduct of this work is the discovery that the ECH treatment completely defluorinates substrates bearing aromatic C-F and C-CF 3 groupings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Untangling the threads of cellulose mercerization

Naturally occurring plant cellulose, our most abundant renewable resource, consists of fibers of long polymer chains that are tightly packed in parallel arrays in either of two crystal phases collectively referred to as cellulose I. During mercerization, a process that involves treatment with sodium hydroxide, cellulose goes through a conversion to another crystal form called cellulose II, within which every other chain has remarkably changed direction. We designed a neutron diffraction experiment with deuterium labelling in order to understand how this change of cellulose chain direction is possible. Here we show that during mercerization of bacterial cellulose, chains fold back on themselves in a zigzag pattern to form crystalline anti-parallel domains. This result provides a molecular level understanding of one of the most widely used industrial processes for improving cellulosic materials.

59 BASIC BIOLOGICAL SCIENCES↗

Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning

Proton exchange membrane fuel cells, consuming hydrogen and oxygen to generate clean electricity and water, suffer acute liquid water challenges. Accurate liquid water modelling is inherently challenging due to the multi-phase, multi-component, reactive dynamics within multi-scale, multi-layered porous media. In addition, currently inadequate imaging and modelling capabilities are limiting simulations to small areas (<1 mm 2 ) or simplified architectures. Herein, an advancement in water modelling is achieved using X-ray micro-computed tomography, deep learned super-resolution, multi-label segmentation, and direct multi-phase simulation. The resulting image is the most resolved domain (16 mm 2 with 700 nm voxel resolution) and the largest direct multi-phase flow simulation of a fuel cell. This generalisable approach unveils multi-scale water clustering and transport mechanisms over large dry and flooded areas in the gas diffusion layer and flow fields, paving the way for next generation proton exchange membrane fuel cells with optimised structures and wettabilities.

25 ENERGY STORAGE↗

Electrosynthesis of ethylene glycol from C 1 feedstocks in a flow electrolyzer

Ethylene glycol is a widely utilized commodity chemical, the production of which accounts for over 46 million tons of CO 2 emission annually. Here we report a paired electrocatalytic approach for ethylene glycol production from methanol. Carbon catalysts are effective in reducing formaldehyde into ethylene glycol with a 92% Faradaic efficiency, whereas Pt catalysts at the anode enable formaldehyde production through methanol partial oxidation with a 75% Faradaic efficiency. With a membrane-electrode assembly configuration, we show the feasibility of ethylene glycol electrosynthesis from methanol in a single electrolyzer. The electrolyzer operates a full cell voltage of 3.2 V at a current density of 100 mA cm -2 , with a 60% reduction in energy consumption. Further investigations, using operando flow electrolyzer mass spectroscopy, isotopic labeling, and density functional theory (DFT) calculations, indicate that the desorption of a *CH 2 OH intermediate is the crucial step in determining the selectively towards ethylene glycol over methanol.

09 BIOMASS FUELS↗

Highly active, ultra-low loading single-atom iron catalysts for catalytic transfer hydrogenation

Highly effective and selective noble metal-free catalysts attract significant attention. Here, a single-atom iron catalyst is fabricated by saturated adsorption of trace iron onto zeolitic imidazolate framework-8 (ZIF-8) followed by pyrolysis. Its performance toward catalytic transfer hydrogenation of furfural is comparable to state-of-the-art catalysts and up to four orders higher than other Fe catalysts. Isotopic labeling experiments demonstrate an intermolecular hydride transfer mechanism. First principles simulations, spectroscopic calculations and experiments, and kinetic correlations reveal that the synthesis creates pyrrolic Fe(II)-plN 3 as the active center whose flexibility manifested by being pulled out of the plane, enabled by defects, is crucial for collocating the reagents and allowing the chemistry to proceed. The catalyst catalyzes chemoselectively several substrates and possesses a unique trait whereby the chemistry is hindered for more acidic substrates than the hydrogen donors. This work paves the way toward noble-metal free single-atom catalysts for important chemical reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Composition and metabolism of microbial communities in soil pores

Delineation of microbial habitats within the soil matrix and characterization of their environments and metabolic processes are crucial to understand soil functioning, yet their experimental identification remains persistently limited. We combined single- and triple-energy X-ray computed microtomography with pore specific allocation of 13 C labeled glucose and subsequent stable isotope probing to demonstrate how long-term disparities in vegetation history modify spatial distribution patterns of soil pore and particulate organic matter drivers of microbial habitats, and to probe bacterial communities populating such habitats. Here we show striking differences between large (30-150 µm Ø) and small (4-10 µm Ø) soil pores in (i) microbial diversity, composition, and life-strategies, (ii) responses to added substrate, (iii) metabolic pathways, and (iv) the processing and fate of labile C. We propose a microbial habitat classification concept based on biogeochemical mechanisms and localization of soil processes and also suggests interventions to mitigate the environmental consequences of agricultural management.

59 BASIC BIOLOGICAL SCIENCES↗

Global protein turnover quantification in Escherichia coli reveals cytoplasmic recycling under nitrogen limitation

Protein turnover is critical for proteostasis, but turnover quantification is challenging, and even in well-studied E. coli, proteome-wide measurements remain scarce. Here, we quantify the turnover rates of ~3200 E. coli proteins under 13 conditions by combining heavy isotope labeling with complement reporter ion quantification and find that cytoplasmic proteins are recycled when nitrogen is limited. We use knockout experiments to assign substrates to the known cytoplasmic ATP-dependent proteases. Surprisingly, none of these proteases are responsible for the observed cytoplasmic protein degradation in nitrogen limitation, suggesting that a major proteolysis pathway in E. coli remains to be discovered. Lastly, we show that protein degradation rates are generally independent of cell division rates. Thus, we present broadly applicable technology for protein turnover measurements and provide a rich resource for protein half-lives and protease substrates in E. coli, complementary to genomics data, that will allow researchers to study the control of proteostasis.

59 BASIC BIOLOGICAL SCIENCES↗

Missing microbial eukaryotes and misleading meta-omic conclusions

Meta-omics is commonly used for large-scale analyses of microbial eukaryotes, including species or taxonomic group distribution mapping, gene catalog construction, and inference on the functional roles and activities of microbial eukaryotes in situ. Here, we explore the potential pitfalls of common approaches to taxonomic annotation of protistan meta-omic datasets. We re-analyze three environmental datasets at three levels of taxonomic hierarchy in order to illustrate the crucial importance of database completeness and curation in enabling accurate environmental interpretation. We show that taxonomic membership of sequence clusters estimates community composition more accurately than returning exact sequence labels, and overlap between clusters can address database shortcomings. Clustering approaches can be applied to diverse environments while continuing to exploit the wealth of annotation data collated in databases, and selecting and evaluating these databases is a critical part of correctly annotating protistan taxonomy in environmental datasets. We argue that ongoing curation of genetic resources is crucial in accurately annotating protists in in situ meta-omic datasets. Moreover, we propose that precise taxonomic annotation of meta-omic data is a clustering problem rather than a feasible alignment problem.

59 BASIC BIOLOGICAL SCIENCES↗

Emergence of lignin-carbohydrate interactions during plant stem maturation visualized by solid-state NMR

Lignification waterproofs and strengthens secondary plant cell walls but increases the energy cost of sugar release for biofuels. The physical association between lignin and the carbohydrate scaffold that accommodates lignin polymerization, along with the distinct roles of lignin units and carbohydrate partners during lignification, remain unclear. Here, we map lignin-carbohydrate spatial proximity by solid-state NMR in 13 C-labeled Arabidopsis inflorescence stems during secondary cell wall formation. Analyses include wild-type plants and mutants that selectively or globally disrupt lignin biosynthesis. Mature walls in basal regions show enrichment of S-lignin and dense carbohydrate-lignin packing. Acetylated xylan predominantly associates with S-lignin, while methylated pectin unexpectedly interacts with G-lignin during early-stage lignification. The importance of S-lignin in stabilizing the carbohydrate-lignin interface is highlighted by weak lignin-carbohydrate contacts and compromised mechanical properties in the low-S fah1 mutant, whereas the ref3 mutant, despite reduced lignin content, remains unaffected due to a high S/G ratio. Thus, molecular mixing patterns, rather than lignin content, critically determine the structure and properties of lignocellulosic materials.

59 BASIC BIOLOGICAL SCIENCES↗

Selective semihydrogenation of acetylene in ethylene using defect-rich boron nitride catalyst from flux reconstruction

Efficient removal of trace acetylene from ethylene streams is essential for producing polymer-grade ethylene, yet achieving highly selective semihydrogenation without over-hydrogenation remains a long-standing challenge. A key barrier is the lack of a simple, low-cost catalyst that can activate hydrogen effectively while preventing ethylene from reacting further. Here we show that defect-rich boron nitride, prepared through a straightforward flux reconstruction method, serves as a highly selective and metal-free catalyst for acetylene semihydrogenation. The catalyst contains abundant open boron and nitrogen sites that enable efficient hydrogen activation and rapid release of ethylene, thereby avoiding over-hydrogenation. Experiments combined with isotope labeling and theoretical analysis reveal that these defects lower the energy barrier for hydrogen activation while accelerating product desorption. Our findings demonstrate a scalable strategy for defect engineering in boron nitride and highlight its potential as a robust, sustainable alternative to metal-based catalysts in industrial ethylene purification.

Wang, Tao [Oak Ridge National Laboratory (ORNL), O↗