Showing papers in "Proceedings of the National Academy of Sciences of the United States of America in 2018"

Global increase and geographic convergence in antibiotic consumption between 2000 and 2015.

Abstract: Tracking antibiotic consumption patterns over time and across countries could inform policies to optimize antibiotic prescribing and minimize antibiotic resistance, such as setting and enforcing per capita consumption targets or aiding investments in alternatives to antibiotics. In this study, we analyzed the trends and drivers of antibiotic consumption from 2000 to 2015 in 76 countries and projected total global antibiotic consumption through 2030. Between 2000 and 2015, antibiotic consumption, expressed in defined daily doses (DDD), increased 65% (21.1–34.8 billion DDDs), and the antibiotic consumption rate increased 39% (11.3–15.7 DDDs per 1,000 inhabitants per day). The increase was driven by low- and middle-income countries (LMICs), where rising consumption was correlated with gross domestic product per capita (GDPPC) growth (P = 0.004). In high-income countries (HICs), although overall consumption increased modestly, DDDs per 1,000 inhabitants per day fell 4%, and there was no correlation with GDPPC. Of particular concern was the rapid increase in the use of last-resort compounds, both in HICs and LMICs, such as glycylcyclines, oxazolidinones, carbapenems, and polymyxins. Projections of global antibiotic consumption in 2030, assuming no policy changes, were up to 200% higher than the 42 billion DDDs estimated in 2015. Although antibiotic consumption rates in most LMICs remain lower than in HICs despite higher bacterial disease burden, consumption in LMICs is rapidly converging to rates similar to HICs. Reducing global consumption is critical for reducing the threat of antibiotic resistance, but reduction efforts must balance access limitations in LMICs and take account of local and global resistance patterns.

The biomass distribution on Earth

Abstract: A census of the biomass on Earth is key for understanding the structure and dynamics of the biosphere. However, a global, quantitative view of how the biomass of different taxa compare with one another is still lacking. Here, we assemble the overall biomass composition of the biosphere, establishing a census of the ≈550 gigatons of carbon (Gt C) of biomass distributed among all of the kingdoms of life. We find that the kingdoms of life concentrate at different locations on the planet; plants (≈450 Gt C, the dominant kingdom) are primarily terrestrial, whereas animals (≈2 Gt C) are mainly marine, and bacteria (≈70 Gt C) and archaea (≈7 Gt C) are predominantly located in deep subsurface environments. We show that terrestrial biomass is about two orders of magnitude higher than marine biomass and estimate a total of ≈6 Gt C of marine biota, doubling the previous estimated quantity. Our analysis reveals that the global marine biomass pyramid contains more consumers than producers, thus increasing the scope of previous observations on inverse food pyramids. Finally, we highlight that the mass of humans is an order of magnitude higher than that of all wild mammals combined and report the historical impact of humanity on the global biomass of prominent taxa, including mammals, fish, and plants.

Trajectories of the Earth System in the Anthropocene.

Abstract: We explore the risk that self-reinforcing feedbacks could push the Earth System toward a planetary threshold that, if crossed, could prevent stabilization of the climate at intermediate temperature rises and cause continued warming on a "Hothouse Earth" pathway even as human emissions are reduced. Crossing the threshold would lead to a much higher global average temperature than any interglacial in the past 1.2 million years and to sea levels significantly higher than at any time in the Holocene. We examine the evidence that such a threshold might exist and where it might be. If the threshold is crossed, the resulting trajectory would likely cause serious disruptions to ecosystems, society, and economies. Collective human action is required to steer the Earth System away from a potential threshold and stabilize it in a habitable interglacial-like state. Such action entails stewardship of the entire Earth System-biosphere, climate, and societies-and could include decarbonization of the global economy, enhancement of biosphere carbon sinks, behavioral changes, technological innovations, new governance arrangements, and transformed social values.

Solving high-dimensional partial differential equations using deep learning

Abstract: Developing algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notoriously difficult problem known as the “curse of dimensionality.” This paper introduces a deep learning-based approach that can handle general high-dimensional parabolic PDEs. To this end, the PDEs are reformulated using backward stochastic differential equations and the gradient of the unknown solution is approximated by neural networks, very much in the spirit of deep reinforcement learning with the gradient acting as the policy function. Numerical results on examples including the nonlinear Black–Scholes equation, the Hamilton–Jacobi–Bellman equation, and the Allen–Cahn equation suggest that the proposed algorithm is quite effective in high dimensions, in terms of both accuracy and cost. This opens up possibilities in economics, finance, operational research, and physics, by considering all participating agents, assets, resources, or particles together at the same time, instead of making ad hoc assumptions on their interrelationships.

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter

Abstract: Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.

The preregistration revolution.

Abstract: Progress in science relies in part on generating hypotheses with existing observations and testing hypotheses with new observations. This distinction between postdiction and prediction is appreciated conceptually but is not respected in practice. Mistaking generation of postdictions with testing of predictions reduces the credibility of research findings. However, ordinary biases in human reasoning, such as hindsight bias, make it hard to avoid this mistake. An effective solution is to define the research questions and analysis plan before observing the research outcomes—a process called preregistration. Preregistration distinguishes analyses and outcomes that result from predictions from those that result from postdictions. A variety of practical strategies are available to make the best possible use of preregistration in circumstances that fall short of the ideal application, such as when the data are preexisting. Services are now available for preregistration across all disciplines, facilitating a rapid increase in the practice. Widespread adoption of preregistration will increase distinctiveness between hypothesis generation and hypothesis testing and will improve the credibility of research findings.

Exposure to opposing views on social media can increase political polarization

Abstract: There is mounting concern that social media sites contribute to political polarization by creating “echo chambers” that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for 1 month that exposed them to messages from those with opposing political ideologies (e.g., elected officials, opinion leaders, media organizations, and nonprofit groups). Respondents were resurveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative posttreatment. Democrats exhibited slight increases in liberal attitudes after following a conservative Twitter bot, although these effects are not statistically significant. Notwithstanding important limitations of our study, these findings have significant implications for the interdisciplinary literature on political polarization and the emerging field of computational social science.

Normal aging induces A1-like astrocyte reactivity

Abstract: The decline of cognitive function occurs with aging, but the mechanisms responsible are unknown. Astrocytes instruct the formation, maturation, and elimination of synapses, and impairment of these functions has been implicated in many diseases. These findings raise the question of whether astrocyte dysfunction could contribute to cognitive decline in aging. We used the Bac-Trap method to perform RNA sequencing of astrocytes from different brain regions across the lifespan of the mouse. We found that astrocytes have region-specific transcriptional identities that change with age in a region-dependent manner. We validated our findings using fluorescence in situ hybridization and quantitative PCR. Detailed analysis of the differentially expressed genes in aging revealed that aged astrocytes take on a reactive phenotype of neuroinflammatory A1-like reactive astrocytes. Hippocampal and striatal astrocytes up-regulated a greater number of reactive astrocyte genes compared with cortical astrocytes. Moreover, aged brains formed many more A1 reactive astrocytes in response to the neuroinflammation inducer lipopolysaccharide. We found that the aging-induced up-regulation of reactive astrocyte genes was significantly reduced in mice lacking the microglial-secreted cytokines (IL-1α, TNF, and C1q) known to induce A1 reactive astrocyte formation, indicating that microglia promote astrocyte activation in aging. Since A1 reactive astrocytes lose the ability to carry out their normal functions, produce complement components, and release a toxic factor which kills neurons and oligodendrocytes, the aging-induced up-regulation of reactive genes by astrocytes could contribute to the cognitive decline in vulnerable brain regions in normal aging and contribute to the greater vulnerability of the aged brain to injury.

Word embeddings quantify 100 years of gender and ethnic stereotypes.

Abstract: Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts-e.g., the women's movement in the 1960s and Asian immigration into the United States-and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science.

Developing a molecular dynamics force field for both folded and disordered protein states.

Abstract: Molecular dynamics (MD) simulation is a valuable tool for characterizing the structural dynamics of folded proteins and should be similarly applicable to disordered proteins and proteins with both folded and disordered regions. It has been unclear, however, whether any physical model (force field) used in MD simulations accurately describes both folded and disordered proteins. Here, we select a benchmark set of 21 systems, including folded and disordered proteins, simulate these systems with six state-of-the-art force fields, and compare the results to over 9,000 available experimental data points. We find that none of the tested force fields simultaneously provided accurate descriptions of folded proteins, of the dimensions of disordered proteins, and of the secondary structure propensities of disordered proteins. Guided by simulation results on a subset of our benchmark, however, we modified parameters of one force field, achieving excellent agreement with experiment for disordered proteins, while maintaining state-of-the-art accuracy for folded proteins. The resulting force field, a99SB-disp, should thus greatly expand the range of biological systems amenable to MD simulation. A similar approach could be taken to improve other force fields.

Climate-change-driven accelerated sea-level rise detected in the altimeter era.

Abstract: Using a 25-y time series of precision satellite altimeter data from TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3, we estimate the climate-change-driven acceleration of global mean sea level over the last 25 y to be 0.084 ± 0.025 mm/y2 Coupled with the average climate-change-driven rate of sea level rise over these same 25 y of 2.9 mm/y, simple extrapolation of the quadratic implies global mean sea level could rise 65 ± 12 cm by 2100 compared with 2005, roughly in agreement with the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5) model projections.

Novel concept of the smart NIR-light-controlled drug release of black phosphorus nanostructure for cancer therapy.

Abstract: A biodegradable drug delivery system (DDS) is one the most promising therapeutic strategies for cancer therapy. Here, we propose a unique concept of light activation of black phosphorus (BP) at hydrogel nanostructures for cancer therapy. A photosensitizer converts light into heat that softens and melts drug-loaded hydrogel-based nanostructures. Drug release rates can be accurately controlled by light intensity, exposure duration, BP concentration, and hydrogel composition. Owing to sufficiently deep penetration of near-infrared (NIR) light through tissues, our BP-based system shows high therapeutic efficacy for treatment of s.c. cancers. Importantly, our drug delivery system is completely harmless and degradable in vivo. Together, our work proposes a unique concept for precision cancer therapy by external light excitation to release cancer drugs. If these findings are successfully translated into the clinic, millions of patients with cancer will benefit from our work.

Oxygen radicals, nitric oxide, and peroxynitrite: Redox pathways in molecular medicine

Abstract: Oxygen-derived free radicals and related oxidants are ubiquitous and short-lived intermediates formed in aerobic organisms throughout life. These reactive species participate in redox reactions leading to oxidative modifications in biomolecules, among which proteins and lipids are preferential targets. Despite a broad array of enzymatic and nonenzymatic antioxidant systems in mammalian cells and microbes, excess oxidant formation causes accumulation of new products that may compromise cell function and structure leading to cell degeneration and death. Oxidative events are associated with pathological conditions and the process of normal aging. Notably, physiological levels of oxidants also modulate cellular functions via homeostatic redox-sensitive cell signaling cascades. On the other hand, nitric oxide ( • NO), a free radical and weak oxidant, represents a master physiological regulator via reversible interactions with heme proteins. The bioavailability and actions of • NO are modulated by its fast reaction with superoxide radical ( O 2 • − ), which yields an unusual and reactive peroxide, peroxynitrite, representing the merging of the oxygen radicals and • NO pathways. In this Inaugural Article, I summarize early and remarkable developments in free radical biochemistry and the later evolution of the field toward molecular medicine; this transition includes our contributions disclosing the relationship of • NO with redox intermediates and metabolism. The biochemical characterization, identification, and quantitation of peroxynitrite and its role in disease processes have concentrated much of our attention. Being a mediator of protein oxidation and nitration, lipid peroxidation, mitochondrial dysfunction, and cell death, peroxynitrite represents both a pathophysiologically relevant endogenous cytotoxin and a cytotoxic effector against invading pathogens.

Assembly and ecological function of the root microbiome across angiosperm plant species.

Abstract: Across plants and animals, host-associated microbial communities play fundamental roles in host nutrition, development, and immunity. The factors that shape host–microbiome interactions are poorly understood, yet essential for understanding the evolution and ecology of these symbioses. Plant roots assemble two distinct microbial compartments from surrounding soil: the rhizosphere (microbes surrounding roots) and the endosphere (microbes within roots). Root-associated microbes were key for the evolution of land plants and underlie fundamental ecosystem processes. However, it is largely unknown how plant evolution has shaped root microbial communities, and in turn, how these microbes affect plant ecology, such as the ability to mitigate biotic and abiotic stressors. Here we show that variation among 30 angiosperm species, which have diverged for up to 140 million years, affects root bacterial diversity and composition. Greater similarity in root microbiomes between hosts leads to negative effects on plant performance through soil feedback, with specific microbial taxa in the endosphere and rhizosphere potentially affecting competitive interactions among plant species. Drought also shifts the composition of root microbiomes, most notably by increasing the relative abundance of the Actinobacteria. However, this drought response varies across host plant species, and host-specific changes in the relative abundance of endosphere Streptomyces are associated with host drought tolerance. Our results emphasize the causes of variation in root microbiomes and their ecological importance for plant performance in response to biotic and abiotic stressors.

Predicting cancer outcomes from histology and genomics using convolutional networks

Abstract: Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma. We use statistical sampling techniques to address challenges in learning survival from histology images, including tumor heterogeneity and the need for large training cohorts. We also provide insights into the prediction mechanisms of SCNNs, using heat map visualization to show that SCNNs recognize important structures, like microvascular proliferation, that are related to prognosis and that are used by pathologists in grading. These results highlight the emerging role of deep learning in precision medicine and suggest an expanding utility for computational analysis of histology in the future practice of pathology.

Draft genome sequence of Camellia sinensis var. sinensis provides insights into the evolution of the tea genome and tea quality.

Abstract: Tea, one of the world’s most important beverage crops, provides numerous secondary metabolites that account for its rich taste and health benefits. Here we present a high-quality sequence of the genome of tea, Camellia sinensis var. sinensis (CSS), using both Illumina and PacBio sequencing technologies. At least 64% of the 3.1-Gb genome assembly consists of repetitive sequences, and the rest yields 33,932 high-confidence predictions of encoded proteins. Divergence between two major lineages, CSS and Camellia sinensis var. assamica (CSA), is calculated to ∼0.38 to 1.54 million years ago (Mya). Analysis of genic collinearity reveals that the tea genome is the product of two rounds of whole-genome duplications (WGDs) that occurred ∼30 to 40 and ∼90 to 100 Mya. We provide evidence that these WGD events, and subsequent paralogous duplications, had major impacts on the copy numbers of secondary metabolite genes, particularly genes critical to producing three key quality compounds: catechins, theanine, and caffeine. Analyses of transcriptome and phytochemistry data show that amplification and transcriptional divergence of genes encoding a large acyltransferase family and leucoanthocyanidin reductases are associated with the characteristic young leaf accumulation of monomeric galloylated catechins in tea, while functional divergence of a single member of the glutamine synthetase gene family yielded theanine synthetase. This genome sequence will facilitate understanding of tea genome evolution and tea metabolite pathways, and will promote germplasm utilization for breeding improved tea varieties.

The timescale of early land plant evolution

Abstract: Establishing the timescale of early land plant evolution is essential for testing hypotheses on the coevolution of land plants and Earth's System. The sparseness of early land plant megafossils and stratigraphic controls on their distribution make the fossil record an unreliable guide, leaving only the molecular clock. However, the application of molecular clock methodology is challenged by the current impasse in attempts to resolve the evolutionary relationships among the living bryophytes and tracheophytes. Here, we establish a timescale for early land plant evolution that integrates over topological uncertainty by exploring the impact of competing hypotheses on bryophyte-tracheophyte relationships, among other variables, on divergence time estimation. We codify 37 fossil calibrations for Viridiplantae following best practice. We apply these calibrations in a Bayesian relaxed molecular clock analysis of a phylogenomic dataset encompassing the diversity of Embryophyta and their relatives within Viridiplantae. Topology and dataset sizes have little impact on age estimates, with greater differences among alternative clock models and calibration strategies. For all analyses, a Cambrian origin of Embryophyta is recovered with highest probability. The estimated ages for crown tracheophytes range from Late Ordovician to late Silurian. This timescale implies an early establishment of terrestrial ecosystems by land plants that is in close accord with recent estimates for the origin of terrestrial animal lineages. Biogeochemical models that are constrained by the fossil record of early land plants, or attempt to explain their impact, must consider the implications of a much earlier, middle Cambrian-Early Ordovician, origin.

Status threat, not economic hardship, explains the 2016 presidential vote.

Abstract: This study evaluates evidence pertaining to popular narratives explaining the American public’s support for Donald J. Trump in the 2016 presidential election. First, using unique representative probability samples of the American public, tracking the same individuals from 2012 to 2016, I examine the “left behind” thesis (that is, the theory that those who lost jobs or experienced stagnant wages due to the loss of manufacturing jobs punished the incumbent party for their economic misfortunes). Second, I consider the possibility that status threat felt by the dwindling proportion of traditionally high-status Americans (i.e., whites, Christians, and men) as well as by those who perceive America’s global dominance as threatened combined to increase support for the candidate who emphasized reestablishing status hierarchies of the past. Results do not support an interpretation of the election based on pocketbook economic concerns. Instead, the shorter relative distance of people’s own views from the Republican candidate on trade and China corresponded to greater mass support for Trump in 2016 relative to Mitt Romney in 2012. Candidate preferences in 2016 reflected increasing anxiety among high-status groups rather than complaints about past treatment among low-status groups. Both growing domestic racial diversity and globalization contributed to a sense that white Americans are under siege by these engines of change.

Earth BioGenome Project: Sequencing life for the future of life.

Abstract: Increasing our understanding of Earth’s biodiversity and responsibly stewarding its resources are among the most crucial scientific and social challenges of the new millennium. These challenges require fundamental new knowledge of the organization, evolution, functions, and interactions among millions of the planet’s organisms. Herein, we present a perspective on the Earth BioGenome Project (EBP), a moonshot for biology that aims to sequence, catalog, and characterize the genomes of all of Earth’s eukaryotic biodiversity over a period of 10 years. The outcomes of the EBP will inform a broad range of major issues facing humanity, such as the impact of climate change on biodiversity, the conservation of endangered species and ecosystems, and the preservation and enhancement of ecosystem services. We describe hurdles that the project faces, including data-sharing policies that ensure a permanent, freely available resource for future scientific discovery while respecting access and benefit sharing guidelines of the Nagoya Protocol. We also describe scientific and organizational challenges in executing such an ambitious project, and the structure proposed to achieve the project’s goals. The far-reaching potential benefits of creating an open digital repository of genomic information for life on Earth can be realized only by a coordinated international effort.

Rapid growth of the US wildland-urban interface raises wildfire risk

Abstract: The wildland-urban interface (WUI) is the area where houses and wildland vegetation meet or intermingle, and where wildfire problems are most pronounced. Here we report that the WUI in the United States grew rapidly from 1990 to 2010 in terms of both number of new houses (from 30.8 to 43.4 million; 41% growth) and land area (from 581,000 to 770,000 km2; 33% growth), making it the fastest-growing land use type in the conterminous United States. The vast majority of new WUI areas were the result of new housing (97%), not related to an increase in wildland vegetation. Within the perimeter of recent wildfires (1990–2015), there were 286,000 houses in 2010, compared with 177,000 in 1990. Furthermore, WUI growth often results in more wildfire ignitions, putting more lives and houses at risk. Wildfire problems will not abate if recent housing growth trends continue.

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning.

Abstract: Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into “big data” sciences. Motion-sensor “camera traps” enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2 million-image Snapshot Serengeti dataset. Our deep neural networks automatically identify animals with >93.8% accuracy, and we expect that number to improve rapidly in years to come. More importantly, if our system classifies only images it is confident about, our system can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that of crowdsourced teams of human volunteers, saving >8.4 y (i.e., >17,000 h at 40 h/wk) of human labeling effort on this 3.2 million-image dataset. Those efficiency gains highlight the importance of using deep neural networks to automate data extraction from camera-trap images, reducing a roadblock for this widely used technology. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild.

Characterization and engineering of a plastic-degrading aromatic polyesterase

Abstract: Poly(ethylene terephthalate) (PET) is one of the most abundantly produced synthetic polymers and is accumulating in the environment at a staggering rate as discarded packaging and textiles. The properties that make PET so useful also endow it with an alarming resistance to biodegradation, likely lasting centuries in the environment. Our collective reliance on PET and other plastics means that this buildup will continue unless solutions are found. Recently, a newly discovered bacterium, Ideonella sakaiensis 201-F6, was shown to exhibit the rare ability to grow on PET as a major carbon and energy source. Central to its PET biodegradation capability is a secreted PETase (PET-digesting enzyme). Here, we present a 0.92 A resolution X-ray crystal structure of PETase, which reveals features common to both cutinases and lipases. PETase retains the ancestral α/β-hydrolase fold but exhibits a more open active-site cleft than homologous cutinases. By narrowing the binding cleft via mutation of two active-site residues to conserved amino acids in cutinases, we surprisingly observe improved PET degradation, suggesting that PETase is not fully optimized for crystalline PET degradation, despite presumably evolving in a PET-rich environment. Additionally, we show that PETase degrades another semiaromatic polyester, polyethylene-2,5-furandicarboxylate (PEF), which is an emerging, bioderived PET replacement with improved barrier properties. In contrast, PETase does not degrade aliphatic polyesters, suggesting that it is generally an aromatic polyesterase. These findings suggest that additional protein engineering to increase PETase performance is realistic and highlight the need for further developments of structure/activity relationships for biodegradation of synthetic polyesters.

Deep learning to represent subgrid processes in climate models.

Abstract: The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system model development could play a key role in reducing climate prediction uncertainty in the coming decade.

Lack of group-to-individual generalizability is a threat to human subjects research

Abstract: Only for ergodic processes will inferences based on group-level data generalize to individual experience or behavior. Because human social and psychological processes typically have an individually variable and time-varying nature, they are unlikely to be ergodic. In this paper, six studies with a repeated-measure design were used for symmetric comparisons of interindividual and intraindividual variation. Our results delineate the potential scope and impact of nonergodic data in human subjects research. Analyses across six samples (with 87-94 participants and an equal number of assessments per participant) showed some degree of agreement in central tendency estimates (mean) between groups and individuals across constructs and data collection paradigms. However, the variance around the expected value was two to four times larger within individuals than within groups. This suggests that literatures in social and medical sciences may overestimate the accuracy of aggregated statistical estimates. This observation could have serious consequences for how we understand the consistency between group and individual correlations, and the generalizability of conclusions between domains. Researchers should explicitly test for equivalence of processes at the individual and group level across the social and medical sciences.

Robust prediction of individual creative ability from brain functional connectivity

Abstract: People’s ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis—connectome-based predictive modeling—to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences (r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems—intrinsic functional networks that tend to work in opposition—suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks.

Macrophages impede CD8 T cells from reaching tumor cells and limit the efficacy of anti–PD-1 treatment

Abstract: In a large proportion of cancer patients, CD8 T cells are excluded from the vicinity of cancer cells. The inability of CD8 T cells to reach tumor cells is considered an important mechanism of resistance to cancer immunotherapy. We show that, in human lung squamous-cell carcinomas, exclusion of CD8 T cells from tumor islets is correlated with a poor clinical outcome and with a low lymphocyte motility, as assessed by dynamic imaging on fresh tumor slices. In the tumor stroma, macrophages mediate lymphocyte trapping by forming long-lasting interactions with CD8 T cells. Using a mouse tumor model with well-defined stromal and tumor cell areas, macrophages were depleted with PLX3397, an inhibitor of colony-stimulating factor-1 receptor (CSF-1R). Our results reveal that a CSF-1R blockade enhances CD8 T cell migration and infiltration into tumor islets. Although this treatment alone has minor effects on tumor growth, its combination with anti–PD-1 therapy further increases the accumulation of CD8 T cells in close contact with malignant cells and delays tumor progression. These data suggest that the reduction of macrophage-mediated T cell exclusion increases tumor surveillance by CD8 T cells and renders tumors more responsive to anti–PD-1 treatment.

Chromatin organization by an interplay of loop extrusion and compartmental segregation

Abstract: Mammalian chromatin is spatially organized at many scales showing two prominent features in interphase: (i) alternating regions (1-10 Mb) of active and inactive chromatin that spatially segregate into different compartments, and (ii) domains (<1 Mb), that is, regions that preferentially interact internally [topologically associating domains (TADs)] and are central to gene regulation. There is growing evidence that TADs are formed by active extrusion of chromatin loops by cohesin, whereas compartmentalization is established according to local chromatin states. Here, we use polymer simulations to examine how loop extrusion and compartmental segregation work collectively and potentially interfere in shaping global chromosome organization. A model with differential attraction between euchromatin and heterochromatin leads to phase separation and reproduces compartmentalization as observed in Hi-C. Loop extrusion, essential for TAD formation, in turn, interferes with compartmentalization. Our integrated model faithfully reproduces Hi-C data from puzzling experimental observations where altering loop extrusion also led to changes in compartmentalization. Specifically, depletion of chromatin-associated cohesin reduced TADs and revealed finer compartments, while increased processivity of cohesin strengthened large TADs and reduced compartmentalization; and depletion of the TAD boundary protein CTCF weakened TADs while leaving compartments unaffected. We reveal that these experimental perturbations are special cases of a general polymer phenomenon of active mixing by loop extrusion. Our results suggest that chromatin organization on the megabase scale emerges from competition of nonequilibrium active loop extrusion and epigenetically defined compartment structure.

Caspase-8 induces cleavage of gasdermin D to elicit pyroptosis during Yersinia infection.

Abstract: Cell death and inflammation are intimately linked during Yersinia infection. Pathogenic Yersinia inhibits the MAP kinase TGFβ-activated kinase 1 (TAK1) via the effector YopJ, thereby silencing cytokine expression while activating caspase-8–mediated cell death. Here, using Yersinia pseudotuberculosis in corroboration with costimulation of lipopolysaccharide and (5Z)-7-Oxozeaenol, a small-molecule inhibitor of TAK1, we show that caspase-8 activation during TAK1 inhibition results in cleavage of both gasdermin D (GSDMD) and gasdermin E (GSDME) in murine macrophages, resulting in pyroptosis. Loss of GsdmD delays membrane rupture, reverting the cell-death morphology to apoptosis. We found that the Yersinia-driven IL-1 response arises from asynchrony of macrophage death during bulk infections in which two cellular populations are required to provide signal 1 and signal 2 for IL-1α/β release. Furthermore, we found that human macrophages are resistant to YopJ-mediated pyroptosis, with dampened IL-1β production. Our results uncover a form of caspase-8–mediated pyroptosis and suggest a hypothesis for the increased sensitivity of humans to Yersinia infection compared with the rodent reservoir.

MYB72-dependent coumarin exudation shapes root microbiome assembly to promote plant health

Abstract: Plant roots nurture a tremendous diversity of microbes via exudation of photosynthetically fixed carbon sources. In turn, probiotic members of the root microbiome promote plant growth and protect the host plant against pathogens and pests. In the Arabidopsis thaliana-Pseudomonas simiae WCS417 model system the root-specific transcription factor MYB72 and the MYB72-controlled β-glucosidase BGLU42 emerged as important regulators of beneficial rhizobacteria-induced systemic resistance (ISR) and iron-uptake responses. MYB72 regulates the biosynthesis of iron-mobilizing fluorescent phenolic compounds, after which BGLU42 activity is required for their excretion into the rhizosphere. Metabolite fingerprinting revealed the antimicrobial coumarin scopoletin as a dominant metabolite that is produced in the roots and excreted into the rhizosphere in a MYB72- and BGLU42-dependent manner. Shotgun-metagenome sequencing of root-associated microbiota of Col-0, myb72, and the scopoletin biosynthesis mutant f6'h1 showed that scopoletin selectively impacts the assembly of the microbial community in the rhizosphere. We show that scopoletin selectively inhibits the soil-borne fungal pathogens Fusarium oxysporum and Verticillium dahliae, while the growth-promoting and ISR-inducing rhizobacteria P. simiae WCS417 and Pseudomonas capeferrum WCS358 are highly tolerant of the antimicrobial effect of scopoletin. Collectively, our results demonstrate a role for coumarins in microbiome assembly and point to a scenario in which plants and probiotic rhizobacteria join forces to trigger MYB72/BGLU42-dependent scopolin production and scopoletin excretion, resulting in improved niche establishment for the microbial partner and growth and immunity benefits for the host plant.

β-Amyloid accumulation in the human brain after one night of sleep deprivation.

Abstract: The effects of acute sleep deprivation on β-amyloid (Aβ) clearance in the human brain have not been documented. Here we used PET and 18F-florbetaben to measure brain Aβ burden (ABB) in 20 healthy controls tested after a night of rested sleep (baseline) and after a night of sleep deprivation. We show that one night of sleep deprivation, relative to baseline, resulted in a significant increase in Aβ burden in the right hippocampus and thalamus. These increases were associated with mood worsening following sleep deprivation, but were not related to the genetic risk (APOE genotype) for Alzheimer's disease. Additionally, baseline ABB in a range of subcortical regions and the precuneus was inversely associated with reported night sleep hours. APOE genotyping was also linked to subcortical ABB, suggesting that different Alzheimer's disease risk factors might independently affect ABB in nearby brain regions. In summary, our findings show adverse effects of one-night sleep deprivation on brain ABB and expand on prior findings of higher Aβ accumulation with chronic less sleep.