N₂ Electroreduction to NH₃ by Selenium Vacancy‐Rich ReSe₂ Catalysis at an Abrupt Interface

Vacancy engineering has been proved repeatedly as an adoptable strategy to boost electrocatalysis, while its poor selectivity restricts the usage in nitrogen reduction reaction (NRR) as overwhelming competition from hydrogen evolution reaction (HER). Revealed by density functional theory calculations, the selenium vacancy in ReSe₂ crystal can enhance its electroactivity for both NRR and HER by shifting the d‐band from −4.42 to −4.19 eV. To restrict the HER, we report a novel method by burying selenium vacancy‐rich ReSe₂@carbonized bacterial cellulose (V_{r}‐ReSe₂@CBC) nanofibers between two CBC layers, leading to boosted Faradaic efficiency of 42.5 % and ammonia yield of 28.3 μg h^{-1} cm^{-2} at a potential of −0.25 V on an abrupt interface. As demonstrated by the nitrogen bubble adhesive force, superhydrophilic measurements, and COMSOL Multiphysics simulations, the hydrophobic and porous CBC layers can keep the internal V_{r}‐ReSe₂@CBC nanofibers away from water coverage, leaving more unoccupied active sites for the N₂ reduction (especially for the potential determining step of proton‐electron coupling and transferring processes as *NN → *NNH)

Phosphorylation decelerates conformational dynamics in bacterial translation elongation factors

Bacterial protein synthesis is intricately connected to metabolic rate. One of the ways in which bacteria respond to environmental stress is through posttranslational modifications of translation factors. Translation elongation factor Tu (EF-Tu) is methylated and phosphorylated in response to nutrient starvation upon entering stationary phase, and its phosphorylation is a crucial step in the pathway toward sporulation. We analyze how phosphorylation leads to inactivation of Escherichia coli EF-Tu. We provide structural and biophysical evidence that phosphorylation of EF-Tu at T382 acts as an efficient switch that turns off protein synthesis by decoupling nucleotide binding from the EF-Tu conformational cycle. Direct modifications of the EF-Tu switch I region or modifications in other regions stabilizing the β-hairpin state of switch I result in an effective allosteric trap that restricts the normal dynamics of EF-Tu and enables the evasion of the control exerted by nucleotides on G proteins. These results highlight stabilization of a phosphorylation-induced conformational trap as an essential mechanism for phosphoregulation of bacterial translation and metabolism. We propose that this mechanism may lead to the multisite phosphorylation state observed during dormancy and stationary phase

Image based machine learning for identification of macrophage subsets

Macrophages play a crucial rule in orchestrating immune responses against pathogens and foreign materials. Macrophages have remarkable plasticity in response to environmental cues and are able to acquire a spectrum of activation status, best exemplified by pro-inflammatory (M1) and anti-inflammatory (M2) phenotypes at the two ends of the spectrum. Characterisation of M1 and M2 subsets is usually carried out by quantification of multiple cell surface markers, transcription factors and cytokine profiles. These approaches are time consuming, require large numbers of cells and are resource intensive. In this study, we used machine learning algorithms to develop a simple and fast imaging-based approach that enables automated identification of different macrophage functional phenotypes using their cell size and morphology. Fluorescent microscopy was used to assess cell morphology of different cell types which were stained for nucleus and actin distribution using DAPI and phalloidin respectively. By only analysing their morphology we were able to identify M1 and M2 phenotypes effectively and could distinguish them from naïve macrophages and monocytes with an average accuracy of 90%. Thus we suggest high-content and automated image analysis can be used for fast phenotyping of functionally diverse cell populations with reasonable accuracy and without the need for using multiple markers

Liposomal targeting of glucocorticoid in experimental arthritis

Contains fulltext : 108521.pdf (Publisher’s version ) (Open Access)Radboud Universiteit Nijmegen, 09 mei 2012Promotores : Berg, W.B. van den, Storm, G. Co-promotor : Lent, P.L.E.M. va