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The Neurospora crassa genome has a gene cluster for the synthesis of galactosaminogalactan (GAG). The gene cluster includes the following: (1) UDP-glucose-4-epimerase to convert UDP-glucose and UDP-N-acetylglucosamine to UDP-galactose and UDP-N-acetylgalactosamine (NCU05133), (2) GAG synthase for the synthesis of an acetylated GAG (NCU05132), (3) GAG deacetylase (/NCW-1/NCU05137), (4) GH135-1, a GAG hydrolase with specificity for N-acetylgalactosamine-containing GAG (NCU05135), and (5) GH114-1, a galactosaminidase with specificity for galactosamine-containing GAG (NCU05136). The deacetylase was previously shown to be a major cell wall glycoprotein and given the name of NCW-1 (non-GPI anchored cell wall protein-1). Characterization of the polysaccharides found in the growth medium from the wild type and the GAG synthase mutant demonstrates that there is a major reduction in the levels of polysaccharides containing galactosamine and N-acetylgalactosamine in the mutant growth medium, providing evidence that the synthase is responsible for the production of a GAG. The analysis also indicates that there are other galactose-containing polysaccharides produced by the fungus. Phenotypic characterization of wild-type and mutant isolates showed that deacetylated GAG from the wild type can function as an adhesin to a glass surface and provides the fungal mat with tensile strength, demonstrating that the deacetylated GAG functions as an intercellular adhesive. The acetylated GAG produced by the deacetylase mutant was found to function as an adhesive for chitin, alumina, celite (diatomaceous earth), activated charcoal, and wheat leaf particulates. 

A burst buffer is a common method to bridge the performance gap between the I/O needs of modern supercomputing applications and the performance of the shared file system on large-scale supercomputers. However, existing I/O sharing methods require resource isolation, offline profiling, or repeated execution that significantly limit the utilization and applicability of these systems. Here we present ThemisIO, a policy-driven I/O sharing framework for a remote-shared burst buffer: a dedicated group of I/O nodes, each with a local storage device. ThemisIO preserves high utilization by implementing opportunity fairness so that it can reallocate unused I/O resources to other applications. ThemisIO accurately and efficiently allocates I/O cycles among applications, purely based on real-time I/O behavior without requiring user-supplied information or offline-profiled application characteristics. ThemisIO supports a variety of fair sharing policies, such as user-fair, size-fair, as well as composite policies, e.g., group-then-user-fair. All these features are enabled by its statistical token design. ThemisIO can alter the execution order of incoming I/O requests based on assigned tokens to precisely balance I/O cycles between applications via time slicing, thereby enforcing processing isolation. Experiments using I/O benchmarks show that ThemisIO sustains 13.5--13.7% higher I/O throughput and 19.5--40.4% lower performance variation than existing algorithms. For real applications, ThemisIO significantly reduces the slowdown by 59.1--99.8% caused by I/O interference. 

Abstract Cellulose, the main component of the plant cell wall, is synthesized by the multimeric cellulose synthase (CESA) complex (CSC). In plant cells, CSCs are assembled in the endoplasmic reticulum or Golgi and transported through the endomembrane system to the plasma membrane (PM). However, how CESA catalytic activity or conserved motifs around the catalytic core influence vesicle trafficking or protein dynamics is not well understood. Here, we used yellow fluorescent protein (YFP)-tagged AtCESA6 and created 18 mutants in key motifs of the catalytic domain to analyze how they affected seedling growth, cellulose biosynthesis, complex formation, and CSC dynamics and trafficking in Arabidopsis thaliana. Seedling growth and cellulose content were reduced by nearly all mutations. Moreover, mutations in most conserved motifs slowed CSC movement in the PM as well as delivery of CSCs to the PM. Interestingly, mutations in the DDG and QXXRW motifs affected YFP-CESA6 abundance in the Golgi. These mutations also perturbed post-Golgi trafficking of CSCs. The 18 mutations were divided into 2 groups based on their phenotypes; we propose that Group I mutations cause CSC trafficking defects, whereas Group II mutations, especially in the QXXRW motif, affect protein folding and/or CSC rosette formation. Collectively, our results demonstrate that the CESA6 catalytic domain is essential for cellulose biosynthesis as well as CSC formation, protein folding and dynamics, and vesicle trafficking. 

Abstract Stratospheric aerosol geoengineering has been proposed as a potential solution to reduce climate change and its impacts. Here, we explore the responses of the Hadley circulation (HC) intensity and the intertropical convergence zone (ITCZ) using the strategic stratospheric aerosol geoengineering, in which sulfur dioxide was injected into the stratosphere at four different locations to maintain the global-mean surface temperature and the interhemispheric and equator-to-pole temperature gradients at present-day values (baseline). Simulations show that, relative to the baseline, strategic stratospheric aerosol geoengineering generally maintains northern winter December–January–February (DJF) HC intensity under RCP8.5, while it overcompensates for the greenhouse gas (GHG)-forced southern winter June–July–August (JJA) HC intensity increase, producing a 3.5 ± 0.4% weakening. The residual change of southern HC intensity in JJA is mainly associated with stratospheric heating and tropospheric temperature response due to enhanced stratospheric aerosol concentrations. Geoengineering overcompensates for the GHG-driven northward ITCZ shifts, producing 0.7° ± 0.1° and 0.2° ± 0.1° latitude southward migrations in JJA and DJF, respectively relative to the baseline. These migrations are affected by tropical interhemispheric temperature differences both at the surface and in the free troposphere. Further strategies for reducing the residual change of HC intensity and ITCZ shifts under stratospheric aerosol geoengineering could involve minimizing stratospheric heating and restoring and preserving the present-day tropical tropospheric interhemispheric temperature differences. 

Scientific Machine Learning (SciML) is a new multidisciplinary methodology that combines the data-driven machine learning models and the principle-based computational models to improve the simulations of scientific phenomenon and uncover new scientific rules from existing measurements. This article reveals the experience of using the SciML method to discover the nonlinear dynamics that may be hard to model or be unknown in the real-world scenario. The SciML method solves the traditional principle-based differential equations by integrating a neural network to accurately model the nonlinear dynamics while respecting the scientific constraints and principles. The paper discusses the latest SciML models and apply them to the oscillator simulations and experiment. Besides better capacity to simulate, and match with the observation, the results also demonstrate a successful discovery of the hidden physics in the pendulum dynamics using SciML. 

The health of the planet and its people are at risk. The deterioration of the global commons—ie, the natural systems that support life on Earth—is exacerbating energy, food, and water insecurity, and increasing the risk of disease, disaster, displacement, and conflict. In this Commission, we quantify safe and just Earth-system boundaries (ESBs) and assess minimum access to natural resources required for human dignity and to enable escape from poverty. Collectively, these describe a safe and just corridor that is essential to ensuring sustainable and resilient human and planetary health and thriving in the Anthropocene. We then discuss the need for translation of ESBs across scales to inform science-based targets for action by key actors (and the challenges in doing so), and conclude by identifying the system transformations necessary to bring about a safe and just future. Our concept of the safe and just corridor advances research on planetary boundaries and the justice and Earth-system aspects of the Sustainable Development Goals. We define safe as ensuring the biophysical stability of the Earth system, and our justice principles include minimising harm, meeting minimum access needs, and redistributing resources and responsibilities to enhance human health and wellbeing. The ceiling of the safe and just corridor is defined by the more stringent of the safe and just ESBs to minimise significant harm and ensure Earth-system stability. The base of the corridor is defined by the impacts of minimum global access to food, water, energy, and infrastructure for the global population, in the domains of the variables for which we defined the ESBs. Living within the corridor is necessary, because exceeding the ESBs and not meeting basic needs threatens human health and life on Earth. However, simply staying within the corridor does not guarantee justice because within the corridor resources can also be inequitably distributed, aggravating human health and causing environmental damage. Procedural and substantive justice are necessary to ensure that the space within the corridor is justly shared. We define eight safe and just ESBs for five domains—the biosphere (functional integrity and natural ecosystem area), climate, nutrient cycles (phosphorus and nitrogen), freshwater (surface and groundwater), and aerosols—to reduce the risk of degrading biophysical life-support systems and avoid tipping points. Seven of the ESBs have already been transgressed: functional integrity, natural ecosystem area, climate, phosphorus, nitrogen, surface water, and groundwater. The eighth ESB, air pollution, has been transgressed at the local level in many parts of the world. Although safe boundaries would ensure Earth-system stability and thus safeguard the overall biophysical conditions that have enabled humans to flourish, they do not necessarily safeguard everyone against harm or allow for minimum access to resources for all. We use the concept of Earth-system justice—which seeks to ensure wellbeing and reduce harm within and across generations, nations, and communities, and between humans and other species, through procedural and distributive justice—to assess safe boundaries. Earth-system justice recognises unequal responsibility for, and unequal exposure and vulnerability to, Earth-system changes, and also recognises unequal capacities to respond and unequal access to resources. We also assess the extent to which safe ESBs could minimise irreversible, existential, and other major harms to human health and wellbeing through a review of who is affected at each boundary. Not all safe ESBs are just, in that they do not minimise all significant harm (eg, that associated with the climate change, aerosol, or nitrogen ESBs). Billions of people globally do not have sufficient access to energy, clean water, food, and other resources. For climate change, for example, tens of millions of people are harmed at lower levels of warming than that defined in the safe ESB, and thus to avoid significant harm would require a more stringent ESB. In other domains, the safe ESBs align with the just ESBs, although some need to be modified, or complemented with local standards, to prevent significant harm (eg, the aerosols ESB). We examine the implications of achieving the social SDGs in 2018 through an impact modelling exercise, and quantify the minimum access to resources required for basic human dignity (level 1) as well as the minimum resources required to enable escape from poverty (level 2). We conclude that without social transformation and redistribution of natural resource use (eg, from top consumers of natural resources to those who currently do not have minimum access to these resources), meeting minimum-access levels for people living below the minimum level would increase pressures on the Earth system and the risks of further transgressions of the ESBs. We also estimate resource-access needs for human populations in 2050 and the associated Earth-system impacts these could have. We project that the safe and just climate ESB will be overshot by 2050, even if everybody in the world lives with only the minimum required access to resources (no more, no less), unless there are transformations of, for example, the energy and food systems. Thus, a safe and just corridor will only be possible with radical societal transformations and technological changes. Living within the safe and just corridor requires operationalisation of ESBs by key actors across all levels, which can be achieved via cross-scale translation (whereby resources and responsibilities for impact reductions are equitably shared among actors). We focus on cities and businesses because of the magnitude of their impacts on the Earth system, and their potential to take swift action and act as agents of change. We explore possible approaches for translating each ESB to cities and businesses via the sequential steps of transcription, allocation, and adjustment. We highlight how different elements of Earth-system justice can be reflected in the allocation and adjustment steps by choosing appropriate sharing approaches, informed by the governance context and broader enabling conditions. Finally we discuss system transformations that could move humanity into a safe and just corridor and reduce risks of instability, injustice, and harm to human health. These transformations aim to minimise harm and ensure access to essential resources, while addressing the drivers of Earth-system change and vulnerability and the institutional and social barriers to systemic transformations, and include reducing and reallocating consumption, changing economic systems, technology, and governance. 

Scientific Machine Learning (SciML) is a new multidisciplinary methodology that combines the data-driven machine learning models and the principle-based computational models to improve the simulations of scientific phenomenon and uncover new scientific rules from existing measurements. This article reveals the experience of using the SciML method to discover the nonlinear dynamics that may be hard to model or be unknown in the real-world scenario. The SciML method solves the traditional principle-based differential equations by integrating a neural network to accurately model the nonlinear dynamics while respecting the scientific constraints and principles. The paper discusses the latest SciML models and apply them to the oscillator simulations and experiment. Besides better capacity to simulate, and match with the observation, the results also demonstrate a successful discovery of the hidden physics in the pendulum dynamics using SciML.