This strengthened germinal center response promoted greater antibody affinity maturation, resulting in a more than 1000-fold increase in antigen-specific antibody affinity in comparison to bolus immunization. In summary, this work introduces a simple and effective vaccine delivery platform that increases the potency and durability of subunit vaccines.Controlled site-specific bioconjugation through chemical methods to native DNA remains an unanswered challenge. Herein, we report a simple solution to achieve this conjugation through the tactical combination of two recently developed technologies one for the manipulation of DNA in organic media and another for the chemoselective labeling of alcohols. Reversible adsorption of solid support (RASS) is employed to immobilize DNA and facilitate its transfer into dry acetonitrile. Subsequent reaction with P(V)-based Ψ reagents takes place in high yield with exquisite selectivity for the exposed 3' or 5' alcohols on DNA. This two-stage process, dubbed SENDR for Synthetic Elaboration of Native DNA by RASS, can be applied to a multitude of DNA conformations and sequences with a variety of functionalized Ψ reagents to generate useful constructs.The development of a fluorescent probe for a specific metal has required exquisite design, synthesis, and optimization of fluorogenic molecules endowed with chelating moieties with heteroatoms. These probes are generally chelation- or reactivity-based. Catalysis-based fluorescent probes have the potential to be more sensitive; however, catalytic methods with a biocompatible fluorescence turn-on switch are rare. Here, we have exploited ligand-accelerated metal catalysis to repurpose known fluorescent probes for different metals, a new approach in probe development. We used the cleavage of allylic and propargylic ethers as platforms that were previously designed for palladium. After a single experiment that combinatorially examined >800 reactions with two variables (metal and ligand) for each ether, we discovered a platinum- or copper-selective method with the ligand effect of specific phosphines. Both metal-ligand systems were previously unknown and afforded strong signals owing to catalytic turnover. The fluorometric technologies were applied to geological, pharmaceutical, serum, and live cell samples and were used to discover that platinum accumulates in lysosomes in cisplatin-resistant cells in a manner that appears to be independent of copper distribution. The use of ligand-accelerated catalysis may present a new blueprint for engineering metal selectivity in probe development.The photo-/electrocatalytic nitrogen reduction reaction (NRR) is an up and coming method for sustainable NH3 production; however, its practical application is impeded by poor Faradaic efficiency originating from the competing hydrogen evolution reaction (HER) and the inert N≡N triple bond activation. In this work, we put forth a method to boost NRR through construction of donor-acceptor couples of dual-metal sites. The synergistic effect of dual active sites can potentially break the metal-based activity benchmark toward efficient NRR. By systematically evaluating the stability, activity, and selectivity of 28 heteronuclear dual-atom catalysts (DACs) of M1M2/g-C3N4 candidates, FeMo/g-C3N4 is screened out as an effective electrocatalyst for NRR with a particularly low limiting potential of -0.23 V for NRR and a rather high potential of -0.79 V for HER. Meanwhile, TiMo/g-C3N4, NiMo/g-C3N4, and MoW/g-C3N4 with suitable band edge positions and visible light absorption can be applied to NRR as photocatalysts. The excellent catalytic activity is attributed to the tunable composition of metal dimers, which play an important role in modulating the binding strength of the target intermediates. This work may pave a new way for the rational design of heteronuclear DACs with high activity and stability for NRR, which may also apply to other reactions.We report the identification of three cyclic peptide ligands of K-Ras(G12D) using an integrated in vitro translation-mRNA display selection platform. These cyclic peptides show preferential binding to the GTP-bound state of K-Ras(G12D) over the GDP-bound state and block Ras-Raf interaction. PF-03084014 A co-crystal structure of peptide KD2 with K-Ras(G12D)·GppNHp reveals that this peptide binds in the Switch II groove region with concomitant opening of the Switch II loop and a 40° rotation of the α2 helix, and that a threonine residue (Thr10) on KD2 has direct access to the mutant aspartate (Asp12) on K-Ras. Replacing this threonine with non-natural amino acids afforded peptides with improved potency at inhibiting the interaction between Raf1-RBD and K-Ras(G12D) but not wildtype K-Ras. The union of G12D over wildtype selectivity and GTP state/GDP state selectivity is particularly desirable, considering that oncogenic K-Ras(G12D) exists predominantly in the GTP state in cancer cells, and wildtype K-Ras signaling is important for the maintenance of healthy cells.This work presents a machine learning approach for the computer vision-based recognition of materials inside vessels in the chemistry lab and other settings. In addition, we release a data set associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their contents is essential for performing this task. Modern machine-vision methods learn recognition tasks by using data sets containing a large number of annotated images. This work presents the Vector-LabPics data set, which consists of 2187 images of materials within mostly transparent vessels in a chemistry lab and other general settings. The images are annotated for both the vessels and the individual material phases inside them, and each instance is assigned one or more classes (liquid, solid, foam, suspension, powder, ...). The fill level, labels, corks, and parts of the vessel are also annotated. Several convolutional nets for semantic and instance segmentation were trained on this data set. The trained neural networks achieved good accuracy in detecting and segmenting vessels and material phases, and in classifying liquids and solids, but relatively low accuracy in segmenting multiphase systems such as phase-separating liquids.