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Abstract An accurate description of low-density nuclear matter is crucial for explaining the physics of neutron star crusts. In the density range between approximately 0.01 fm⁻³and 0.1 fm⁻³, matter transitions from neutron-rich nuclei to various higher-density pasta shapes, before ultimately reaching a uniform liquid. In this work, we introduce a variational Monte Carlo method based on a neural Pfaffian-Jastrow quantum state, which allows us to model the transition from the liquid phase to neutron-rich nuclei microscopically. At low densities, nuclear clusters dynamically emerge from the microscopic interactions among protons and neutrons, which we model based on pionless effective field theory. Our variational Monte Carlo approach represents a significant improvement over the state-of-the-art auxiliary-field diffusion Monte Carlo method, which is severely hindered by the fermion-sign problem in this low-density regime and cannot capture the onset of clusters. In addition to computing the energy per particle of symmetric nuclear matter and pure neutron matter, we analyze an intermediate isospin-asymmetry configuration to elucidate the formation of nuclear clusters. We also provide evidence that the presence of such nuclear clusters influences the amount of protons in the crust compared to protons in beta-equilibrated, neutrino-transparent matter. 

Summary Immunofocusing on conserved, subdominant epitopes is critical for vaccines against highly diverse viruses such as HIV-1, influenza, and SARS-CoV-2. The eight-residue N-terminus of the HIV-1 fusion peptide (FP) is one such example of a promising yet small target. We developed new FP immunogens using three alphavirus-like particles (VLPs) and introduced additional glycans to mask shared carrier-specific epitopes. In two independent guinea pig studies, sequential immunization with heterologous carriers enhanced FP-directed antibody titers, which were further improved with glycan engineering. Separately, using diverse FP variants sharing the same N-terminal six amino acids increased neutralizing antibody titers. When combined, these two strategies led to higher FP-directed titers and, after Env trimer boosting, induced FP-directed neutralizing antibodies against multi-clade wild-type HIV-1 in nearly all animals. These findings established the importance of minimizing recurrent off-target epitopes across immunizations and support the engineered VLPs as a promising platform for peptide immunization. HighlightsNovel HIV-1 fusion peptide immunogens using glycan-engineered alphavirus-like particlesImproved FP-directed response by minimizing recurrent carrier-specific epitopes across immunizationsImproved neutralizing response by sequential immunization with diverse FP variantsFP-directed antibodies neutralizing multi-clade wildtype viruses in nearly all animals 

Pseudomonas aeruginosa (PA) is an opportunistic pathogen frequently isolated from cutaneous chronic wounds. How PA, in the presence of oxidative stress (OS), colonizes chronic wounds and forms a biofilm is still unknown. The purpose of this study is to investigate the changes in gene expression seen when PA is challenged with the high levels of OS present in chronic wounds. We used a biofilm-forming PA strain isolated from the chronic wounds of our murine model (RPA) and performed a qPCR to obtain gene expression patterns as RPA developed a biofilm in vitro in the presence of high levels of OS, and then compared the findings in vivo, in our mouse model of chronic wounds. We found that the planktonic bacteria under OS conditions overexpressed quorum sensing genes that are important for the bacteria to communicate with each other, antioxidant stress genes important to reduce OS in the microenvironment for survival, biofilm formation genes and virulence genes. Additionally, we performed RNAseq in vivo and identified the activation of novel genes/pathways of the Type VI Secretion System (T6SS) involved in RPA pathogenicity. In conclusion, RPA appears to survive the high OS microenvironment in chronic wounds and colonizes these wounds by turning on virulence, biofilm-forming and survival genes. These findings reveal pathways that may be promising targets for new therapies aimed at disrupting PA-containing biofilms immediately after debridement to facilitate the treatment of chronic human wounds. 

Abstract Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic superfluid Bose-Einstein condensate (BEC). While these properties can be precisely probed experimentally, accurately describing them poses significant theoretical challenges due to strong pairing correlations and the non-perturbative nature of particle interactions. In this work, we introduce a Pfaffian-Jastrow neural-network quantum state featuring a message-passing architecture to efficiently capture pairing and backflow correlations. We benchmark our approach on existing Slater-Jastrow frameworks and state-of-the-art diffusion Monte Carlo methods, demonstrating a performance advantage and the scalability of our scheme. We show that transfer learning stabilizes the training process in the presence of strong, short-ranged interactions, and allows for an effective exploration of the BCS-BEC crossover region. Our findings highlight the potential of neural-network quantum states as a promising strategy for investigating ultra-cold Fermi gases. 

In this study, we explore the similarities and differences between variational Monte Carlo techniques that employ conventional and artificial neural network representations of the ground-state wave function for fermionic systems. Our primary focus is on shallow neural network architectures, specifically the restricted Boltzmann machine, and we examine unsupervised learning algorithms that are appropriate for modeling complex many-body correlations. We assess the advantages and drawbacks of conventional and neural network wave functions by applying them to a range of circular quantum dot systems. Our findings, which include results for systems containing up to 90 electrons, emphasize the efficient implementation of these methods on both homogeneous and heterogeneous high-performance computing facilities. 

Abstract Myotonic dystrophy type 2 (DM2) is a genetic disease caused by expanded CCTG DNA repeats in the first intron of CNBP. The number of CCTG repeats in DM2 patients ranges from 75 to 11,000, yet little is known about the molecular mechanisms responsible for repeat expansions or contractions. We developed an experimental system in Saccharomyces cerevisiae that enables the selection of large-scale contractions of (CCTG)100 within the intron of a reporter gene and subsequent genetic analysis. Contractions exceeded 80 repeat units, causing the final repetitive tract to be well below the threshold for disease. We found that Rad51 and Rad52 are involved in these massive contractions, indicating a mechanism that uses homologous recombination. Srs2 helicase was shown previously to stabilize CTG, CAG, and CGG repeats. Loss of Srs2 did not significantly affect CCTG contraction rates in unperturbed conditions. In contrast, loss of the RecQ helicase Sgs1 resulted in a 6-fold decrease in contraction rate with specific evidence that helicase activity is required for large-scale contractions. Using a genetic assay to evaluate chromosome arm loss, we determined that CCTG and reverse complementary CAGG repeats elevate the rate of chromosomal fragility compared to a short-track control. Overall, our results demonstrate that the genetic control of CCTG repeat contractions is notably distinct among disease-causing microsatellite repeat sequences.