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Abstract Key functions of antibodies, such as viral neutralisation, depend on high-affinity binding. However, viral neutralisation poorly correlates with antigen affinity for reasons that have been unclear. Here, we use a new mechanistic model of bivalent binding to study  >45 patient-isolated IgG1 antibodies interacting with SARS-CoV-2 RBD surfaces. The model provides the standard monovalent affinity/kinetics and new bivalent parameters, including the molecular reach: the maximum antigen separation enabling bivalent binding. We find large variations in these parameters across antibodies, including reach variations (22–46 nm) that exceed the physical antibody size (~15 nm). By using antigens of different physical sizes, we show that these large molecular reaches are the result of both the antibody and antigen sizes. Although viral neutralisation correlates poorly with affinity, a striking correlation is observed with molecular reach. Indeed, the molecular reach explains differences in neutralisation for antibodies binding with the same affinity to the same RBD-epitope. Thus, antibodies within an isotype class binding the same antigen can display differences in molecular reach, substantially modulating their binding and functional properties. 

The role of Artificial Intelligence (AI) in research and education continues to rapidly grow, resulting in increased collaboration between researchers in AI and Research Computing and Data (RCD) professionals to meet the research and teaching demands. RCD professionals bridge the gap between research and technology by guiding and collaborating with researchers and educators through the process of selecting the hardware, software, and services best suited for executing their AI projects. This includes ensuring compliance with funding and regulatory requirements across the entire lifecycle of the project. In this paper, we present an overview of the AI project lifecycle and how RCD professionals can facilitate its execution. 

We develop a convergent reaction-drift-diffusion master equation (CRDDME) to facilitate the study of reaction processes in which spatial transport is influenced by drift due to one-body potential fields within general domain geometries. The generalized CRDDME is obtained through two steps. We first derive an unstructured grid jump process approximation for reversible diffusions, enabling the simulation of drift-diffusion processes where the drift arises due to a conservative field that biases particle motion. Leveraging the Edge-Averaged Finite Element method, our approach preserves detailed balance of drift-diffusion fluxes at equilibrium, and preserves an equilibrium Gibbs-Boltzmann distribution for particles undergoing drift-diffusion on the unstructured mesh. We next formulate a spatially-continuous volume reactivity particle-based reaction-drift-diffusion model for reversible reactions of the form A + B <–> C. A finite volume discretization is used to generate jump process approximations to reaction terms in this model. The discretization is developed to ensure the combined reaction-drift-diffusion jump process approximation is consistent with detailed balance of reaction fluxes holding at equilibrium, along with supporting a discrete version of the continuous equilibrium state. The new CRDDME model represents a continuous-time discrete-space jump process approximation to the underlying volume reactivity model. We demonstrate the convergence and accuracy of the new CRDDME through a number of numerical examples, and illustrate its use on an idealized model for membrane protein receptor dynamics in T cell signaling. 

Abstract Investigations of the metabolic capabilities of anaerobic protists advances our understanding of the evolution of eukaryotic life on Earth and for uncovering analogous extraterrestrial complex microbial life. Certain species of foraminiferan protists live in environments analogous to early Earth conditions when eukaryotes evolved, including sulfidic, anoxic and hypoxic sediment porewaters. Foraminifera are known to form symbioses as well as to harbor organelles from other eukaryotes (chloroplasts), possibly bolstering the host’s independence from oxygen. The full extent of foraminiferal physiological capabilities is not fully understood. To date, evidence for foraminiferal anaerobiosis was gleaned from specimens first subjected to stresses associated with removal from in situ conditions. Here, we report comprehensive gene expression analysis of benthic foraminiferal populations preserved in situ on the euxinic (anoxic and sulfidic) bathyal seafloor, thus avoiding environmental alterations associated with sample recovery, including pressure reduction, sunlight exposure, warming, and oxygenation. Metatranscriptomics, metagenome-assembled genomes, and measurements of substrate uptake were used to study the kleptoplastidic foraminifer Nonionella stella inhabiting sulfur-oxidizing bacterial mats of the Santa Barbara Basin, off California. We show N. stella energy generation under dark euxinia is unusual because it orchestrates complex metabolic pathways for ATP production and carbon fixation through the Calvin cycle. These pathways include extended glycolysis, anaerobic fermentation, sulfide oxidation, and the presence of a membrane-bound inorganic pyrophosphatase, an enzyme that hydrolyzes inorganic pyrophosphate to actively pump protons across the mitochondrial membrane. 

Recent spectral graph sparsificationresearch aims to construct ultra-sparse subgraphs for preserving the original graph spectral (structural) properties, such as the first few Laplacian eigenvalues and eigenvectors, which has led to the development of a variety of nearly linear time numerical and graph algorithms. However, there is very limited progress in the spectral sparsification of directed graphs. In this work, we prove the existence of nearly linear-sized spectral sparsifiers for directed graphs under certain conditions. Furthermore, we introduce a practically efficient spectral algorithm (diGRASS) for sparsifying real-world, large-scale directed graphs leveraging spectral matrix perturbation analysis. The proposed method has been evaluated using a variety of directed graphs obtained from real-world applications, showing promising results for solving directed graph Laplacians, spectral partitioning of directed graphs, and approximately computing (personalized) PageRank vectors.