Pseudomonas aeruginosa (P. aeruginosa) is a high-priority opportunistic pathogen responsible for severe healthcare-associated infections exhibiting multidrug resistance, emphasizing the urgent need for alternative therapeutic strategies. Monoclonal antibodies (mAbs) targeting the highly conserved outer membrane protein OprF represent a promising approach to mitigate its infectivity. OprF, the major and highly conserved outer membrane protein of P. aeruginosa, plays key roles in the pathogenesis of this bacterium, including biofilm formation, host cell adhesion, immune sensing, and resistance to macrophage clearance, making it a crucial factor in virulence and a promising immunotherapeutic target. Here, we report the preclinical evaluation of EPY001, an anti-OprF mAb generated by immunization of a macaque with OprF-containing proteoliposomes. EPY001 exhibited strong nanomolar binding to OprF. Epitope mapping suggests recognition of a conformational epitope, underscoring the value of proteoliposome-based immunization for membrane protein targets. Functional assays provide insights into OprF’s role in biofilm formation, pyocyanin production, and antibiotic resistance. However, in vivo studies revealed that targeting OprF alone is insufficient to protect mice from lethal infection. These findings contribute to ongoing efforts to develop effective alternatives to conventional antibiotics against this resilient pathogen.

Predictive modelling of galactic star and planet formation This article details advancements in our understanding of star and planet formation within galaxies, emphasising the transition from steady-state models to recognising the dynamic nature of the interstellar medium (ISM) in these processes. Modern astronomy has revealed that the Universe began in an extraordinarily simple state – almost perfectly uniform, with only minute fluctuations in density. Today, more than 13 billion years after the Big Bang, the cosmos is richly structured on every scale, from vast galaxy clusters down to stars, planets, and even complex organic molecules. In this cosmic hierarchy, galaxies – along with the stars and planets they contain – serve as fundamental building blocks. Understanding their origins and the physical processes that drive their evolution is one of the most significant challenges in contemporary astrophysics.

In this work, we demonstrate a substantial enhancement in photoluminescence from semiconducting single-walled carbon nanotubes through integration with a small-mode-volume silicon photonic crystal nanobeam cavity. Our design approach enables precise control of the cavity resonance over a wavelength range exceeding 30 nm, effectively covering the emission spectrum of semiconducting single-walled carbon nanotubes while maintaining stable optical performance. The fabricated nanobeam cavities, embedded with polymer-sorted semiconducting single-walled carbon nanotubes, exhibit low modal volumes of V = 0.07(λ/n)3, facilitating strong light-matter interaction characterized by high coupling efficiency and a Purcell factor on the order of 10000(λ/n)3 at a wavelength of 1570 nm. This hybrid integration exploits the robust light-matter interaction properties of the cavity, leading to a pronounced increase in emission intensity from the carbon nanotubes.

Dissolved organic matter (DOM) reference materials are critical to ensuring reliable comparability of measurements across experiments and between labs. TRM-0522, isolated in 2022 from 45 m deep seawater off of Sweden’s west coast, fills the niche of a previously unavailable coastal marine DOM reference material. After its isolation, we detailed a limited number of metrics for TRM-0522, initially disclosing Orbitrap high-resolution mass spectrometry, nuclear magnetic resonance, carbon proportion, absorbance, and fluorescence data. With TRM-0522 becoming more widely used, a variety of labs have generated different metrics that help to characterize and define the chemical properties of this coastal reference material. Here, we disclose TRM-0522 data from elemental analysis, isotopic analysis (δ13C and Δ14C signatures), condensed aromatic “black” carbon characterization, ultra-HRMS (10-21 Tesla FT-ICR-MS and Orbitrap Fusion Lumos), CD3 labelling, and contribution of carbonyls in absorbance and fluorescence after NaBH4 reduction, pH titration, and nitroxide quenching. This greatly expanded dataset reinforces TRM-0522’s suitability as a coastal reference material, and provides metrics for future use between labs and for inevitable additional large-scale collections of material from this site.

Abstract In Bragg Coherent Diffraction Imaging (BCDI), Phase Retrieval of \textit{highly} strained crystals is often challenging with standard iterative algorithms. This computational obstacle limits the potential of the technique as it precludes the reconstruction of physically interesting highly-strained particles. Here, we propose a novel approach to this problem using a supervised Convolutional Neural Network (CNN) trained on 3D simulated diffraction data to predict the corresponding \textit{reciprocal space phase}. This method allows to fully exploit the potential of the CNN by mapping functions within the same space and leveraging structural similarities between input and output. The final object is obtained by the inverse Fourier transform of the retrieved complex diffracted amplitude and is then further refined with iterative algorithms. We demonstrate that our model outperforms standard algorithms on highly strained simulated data not included in the training set, as well as on experimental data.