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Activation of the innate immune system via pattern recognition receptors (PRRs) is key to generate lasting adaptive immunity. PRRs detect unique chemical patterns associated with invading microorganisms, but whether and how the physical properties of PRR ligands influence the development of the immune response remains unknown. Through the study of fungal mannans, we show that the physical form of PRR ligands dictates the immune response. Soluble mannans are immunosilent in the periphery but elicit a potent pro-inflammatory response in the draining lymph node (dLN). By modulating the physical form of mannans, we developed a formulation that targets both the periphery and the dLN. When combined with viral glycoprotein antigens, this mannan formulation broadens epitope recognition, elicits potent antigen-specific neutralizing antibodies, and confers protection against viral infections of the lung. Thus, the physical properties of microbial ligands determine the outcome of the immune response and can be harnessed for vaccine development.

A core challenge in global change biology is to predict how species will respond to future environmental change and to manage these responses. To make such predictions and management actions robust to novel futures, we need to accurately characterize how organisms experience their environments and the biological mechanisms by which they respond. All organisms are thermodynamically connected to their environments through the exchange of heat and water at fine spatial and temporal scales and this exchange can be captured with biophysical models. Although mechanistic models based on biophysical ecology have a long history of development and application, their use in global change biology remains limited despite their enormous promise and increasingly accessible software. We contend that greater understanding and training in the theory and methods of biophysical ecology is vital to expand their application. Our review shows how biophysical models can be implemented to understand and predict climate change impacts on species' behavior, phenology, survival, distribution, and abundance. It also illustrates the types of outputs that can be generated, and the data inputs required for different implementations. Examples range from simple calculations of body temperature at a particular site and time, to more complex analyses of species' distribution limits based on projected energy and water balances, accounting for behavior and phenology. We outline challenges that currently limit the widespread application of biophysical models relating to data availability, training, and the lack of common software ecosystems. We also discuss progress and future developments that could allow these models to be applied to many species across large spatial extents and timeframes. Finally, we highlight how biophysical models are uniquely suited to solve global change biology problems that involve predicting and interpreting responses to environmental variability and extremes, multiple or shif

The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article contains an overview of the software and some of its most heavily-used functionalities, including algorithmic details, standards, and examples. Key thrusts are highlighted: the calculation of structural, electronic, thermodynamic, and thermomechanical properties in addition to the modeling of complex materials, such as high-entropy ceramics and bulk metallic glasses. The aflow++ software prioritizes interoperability, minimizing the number of independent parameters and tolerances. It ensures consistency of results across property sets — facilitating machine learning studies. The software also features various validation schemes, offering real-time quality assurance for data generated in a high-throughput fashion. Altogether, these considerations contribute to the development of large and reliable materials databases that can ultimately deliver future materials systems.

Within 2 years after the start of the coronavirus disease 2019 (COVID-19) pandemic, novel severe acute respiratory syndrome coronavirus 2 vaccines were developed, rigorously evaluated in large phase 3 trials, and administered to more than 5 billion individuals globally. However, adverse events of special interest (AESIs) have been described post-implementation, including myocarditis after receipt of messenger RNA (mRNA) vaccines and thrombosis with thrombocytopenia syndrome after receipt of adenoviral vector vaccines. AESIs are rare (<1 to 10/100 000 vaccinees) and less frequent than COVID-19 complications, though they have associated morbidity and mortality. The diversity of COVID-19 vaccine platforms (eg, mRNA, viral vector, protein) and rates of AESIs both between and within platforms (eg, higher rate of myocarditis after mRNA-1273 vs BNT162b2 vaccines) present an important opportunity to advance vaccine safety science. The International Network of Special Immunization Services has been formed with experts in vaccine safety, systems biology, and other relevant disciplines to study cases of AESIs and matched controls to uncover the pathogenesis of rare AESIs and inform vaccine development.

In cold environments ectotherms can be dormant underground for long periods. In 1941 Cowles proposed an ecological trade-off involving the depth at which ectotherms overwintered: on warm days, only shallow reptiles could detect warming soils and become active; but on cold days, they risked freezing. Cowles discovered that most reptiles at a desert site overwintered at shallow depths. To extend his study, we compiled hourly soil temperatures (5 depths, 90 sites, continental USA) and physiological data, and simulated consequences of overwintering at fixed depths. In warm localities shallow ectotherms have lowest energy costs and largest reserves in spring, but in cold localities, they risk freezing. Ectotherms shifting hourly to the coldest depth potentially reduce energy expenses, but paradoxically sometimes have higher expenses than those at fixed depths. Biophysical simulations for a desert site predict that shallow ectotherms have increased opportunities for mid-winter activity but need to move deep to digest captured food. Our simulations generate testable predictions to eco-physiological questions but rely on physiological responses to acute cold rather than to natural cooling profiles. Furthermore, natural-history data to test most predictions do not exist. Thus, our simulation approach uncovers knowledge gaps and suggests research agendas for studying ectotherms overwintering underground.

An essential aspect of human communication is the ability to access and retrieve information from ones' 'mental lexicon'. This lexical access activates phonological and semantic components of concepts, yet the question whether and how these two components relate to each other remains widely debated. We harness tools from network science to construct a large-scale linguistic multilayer network comprising of phonological and semantic layers. We find that the links in the two layers are highly similar to each other and that adding information from one layer to the other increases efficiency by decreasing the network overall distances, but specifically affecting shorter distances. Finally, we show how a multilayer architecture demonstrates the highest efficiency, and how this efficiency relates to weak semantic relations between cue words in the network. Thus, investigating the interaction between the layers and the unique benefit of a linguistic multilayer architecture allows us to quantify theoretical cognitive models of lexical access.

Analyses of drug pharmacokinetics (PKs) and pharmacodynamics (PDs) performed in animals are often not predictive of drug PKs and PDs in humans, and in vitro PK and PD modelling does not provide quantitative PK parameters. Here, we show that physiological PK modelling of first-pass drug absorption, metabolism and excretion in humans—using computationally scaled data from multiple fluidically linked two-channel organ chips—predicts PK parameters for orally administered nicotine (using gut, liver and kidney chips) and for intravenously injected cisplatin (using coupled bone marrow, liver and kidney chips). The chips are linked through sequential robotic liquid transfers of a common blood substitute by their endothelium-lined channels (as reported by Novak et al. in an associated Article) and share an arteriovenous fluid-mixing reservoir. We also show that predictions of cisplatin PDs match previously reported patient data. The quantitative in-vitro-to-in-vivo translation of PK and PD parameters and the prediction of drug absorption, distribution, metabolism, excretion and toxicity through fluidically coupled organ chips may improve the design of drug-administration regimens for phase-I clinical trials., QC 20200322