Novel bacterial group potentially dominates sulfur cycling in the dark ocean
Baltar F +10 more
europepmc +1 more source
Single-cell bacterial transcription measurements reveal the importance of dimethylsulfoniopropionate (DMSP) hotspots in ocean sulfur cycling. [PDF]
Gao C +8 more
europepmc +1 more source
Physical Origin of Temperature Induced Activation Energy Switching in Electrically Conductive Cement
The temperature‐induced Arrhenius activation energy switching phenomenon of electrical conduction in electrically conductive cement originates from structural degradation within the biphasic ionic‐electronic conduction architecture and shows percolation‐governed characteristics: pore network opening dominates the low‐percolation regime with downward ...
Jiacheng Zhang +7 more
wiley +1 more source
Sulfur cycling likely obscures dynamic biologically-driven iron redox cycling in contemporary methane seep environments. [PDF]
Baker IR, Girguis PR.
europepmc +1 more source
Cross Kingdom Metabolic Engineering Paradigm Elevating Sustainable Protein Production
ABSTRACT Confronting the dual crisis of escalating global protein demand and unsustainable agriculture necessitates transformative solutions. Here, we pioneer evolutionary insights from maize nitrogen optimization via asparagine synthetase (ASNS) to rewire metabolism in Pichia pastoris.
Yuanyuan Du +4 more
wiley +1 more source
<i>Arcobacteraceae</i> are ubiquitous mixotrophic bacteria playing important roles in carbon, nitrogen, and sulfur cycling in global oceans. [PDF]
Li J +8 more
europepmc +1 more source
Fluorescent BODIPY‐conjugated thiosemicarbazone ligands and their Ga(III), In(III), and Fe(III) complexes, inspired by Triapine, are developed as theranostic agents. Multiphoton FLIM and confocal microscopy in cancer cells and zebrafish reveal real‐time uptake, mitochondrial localisation, and whilst spectroscopic assays indicated preserved complex ...
Megan J. Green +15 more
wiley +1 more source
An Integrated NLP‐ML Framework for Property Prediction and Design of Steels
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju +5 more
wiley +1 more source
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo +6 more
wiley +1 more source
Legacy copper/nickel mine tailings potentially harbor novel iron/sulfur cycling microorganisms within highly variable communities. [PDF]
Chen M +4 more
europepmc +1 more source

