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Inbred mice used for biomedical research display an underdeveloped immune system compared with adult humans, which is attributed in part to the artificial laboratory environment. Despite representing a central component of adaptive immunity, the impact of the laboratory environment on the B cell compartment has not been investigated in detail. Here, we performed an in-depth examination of B cells following rewilding, the controlled release of inbred laboratory mice into an outdoor enclosure. In rewilded mice, we observed B cells in circulation with increased signs of maturation, alongside heightened germinal center responses within secondary lymphoid organs. Rewilding also expanded B cells in the gut, which was accompanied by elevated systemic levels of immunoglobulin G (IgG) and IgM antibodies reactive to the microbiota. Our findings indicate that exposing laboratory mice to a more natural environment enhances B cell development to better reflect the immune system of free-living mammals. 

Abstract Use of single‐atom catalysts (SACs) has become a popular strategy for tuning activity and selectivity toward specific pathways. However, conventional SAC synthesis methods require high temperatures and pressures, complicated procedures, and expensive equipment. Recently, underpotential deposition (UPD) has been investigated as a promising alternative, yielding high‐loading SAC electrodes under ambient conditions and within minutes. Yet only few studies have employed UPD to synthesize SACs, and all have been limited to UPD of Cu. In this work, a flexible UPD approach for synthesis of mono‐ and bi‐metallic Cu, Fe, Co, and Ni SACs directly on oxidized, commercially available carbon electrodes is reported. The UPD mechanism is investigated using in situ X‐ray absorption spectroscopy and, finally, the catalytic performance of a UPD‐synthesized Co SAC is assessed for electrochemical nitrate reduction to ammonia. The findings expand upon the usefulness and versatility of UPD for SAC synthesis, with hopes of enabling future research toward realization of fast, reliable, and fully electrified SAC synthesis processes. 

As students read textbooks, they often highlight the material they deem to be most important. We analyze students’ highlights to predict their subsequent performance on quiz questions. Past research in this area has encoded highlights in terms of where the highlights appear in the stream of text—a positional representation. In this work, we construct a semantic representation based on a state-of-the-art deep-learning sentence embedding technique (SBERT) that captures the content-based similarity between quiz questions and highlighted (as well as non-highlighted) sentences in the text. We construct regression models that include latent variables for student skill level and question difficulty and augment the models with highlighting features. We find that highlighting features reliably boost model performance. We conduct experiments that validate models on held-out questions, students, and student-questions and find strong generalization for the latter two but not for held-out questions. Surprisingly, highlighting features improve models for questions at all levels of the Bloom taxonomy, from straightforward recall questions to inferential synthesis/evaluation/creation questions. 

Abstract System noise identification is crucial to the engineering of robust quantum systems. Although existing quantum noise spectroscopy (QNS) protocols measure an aggregate amount of noise affecting a quantum system, they generally cannot distinguish between the underlying processes that contribute to it. Here, we propose and experimentally validate a spin-locking-based QNS protocol that exploits the multi-level energy structure of a superconducting qubit to achieve two notable advances. First, our protocol extends the spectral range of weakly anharmonic qubit spectrometers beyond the present limitations set by their lack of strong anharmonicity. Second, the additional information gained from probing the higher-excited levels enables us to identify and distinguish contributions from different underlying noise mechanisms. 

Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical, machine vision and other fields. The rapidly increasing number of applications requires convenient easy-to-navigate software that can be used by new and experienced users to analyse data, and develop, apply and deploy novel algorithms. Herein, we present our platform, IDCube Lite, an Interactive Discovery Cube that performs essential operations in hyperspectral data analysis to realise the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimise parameters and obtain visual input for the user in a way not previously accessible with other software packages. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new, hidden features that allow users to integrate novel computational methods.