Upstream surface roughness and terrain are strong drivers of contrast in tornado potential between North and South America.

Central North America is the global hotspot for tornadoes, fueled by elevated terrain of the Rockies to the west and a source of warm, moist air from equatorward oceans. This conventional wisdom argues that central South America, with the Andes to the west and Amazon basin to the north, should have a "tornado alley" at least as active as central North America. Central South America has frequent severe thunderstorms yet relatively few tornadoes. Here, we show that conventional wisdom is missing an important ingredient specific to tornadoes: a smooth, flat ocean-like upstream surface. Using global climate model experiments, we show that central South American tornado potential substantially increases if its equatorward land surface is smoothed and flattened to be ocean-like. Similarly, we show that central North American tornado potential substantially decreases if its equatorward ocean surface is roughened to values comparable to forested land. A rough upstream surface suppresses the formation of tornadic environments principally by weakening the poleward low-level winds, characterized by a weakened low-level jet east of the mountain range. Results are shown to be robust for any midlatitude landmass using idealized experiments with a simplified continent and mountain range. Our findings indicate that large-scale upstream surface roughness is likely a first-order driver of the strong contrast in tornado potential between North and South America.

A modular and synthetic biosynthesis platform for de novo production of diverse halogenated tryptophan-derived molecules.

Halogen-containing molecules are ubiquitous in modern society and present unique chemical possibilities. As a whole, de novo fermentation and synthetic pathway construction for these molecules remain relatively underexplored and could unlock molecules with exciting new applications in industries ranging from textiles to agrochemicals to pharmaceuticals. Here, we report a mix-and-match co-culture platform to de novo generate a large array of halogenated tryptophan derivatives in Escherichia coli from glucose. First, we engineer E. coli to produce between 300 and 700 mg/L of six different halogenated tryptophan precursors. Second, we harness the native promiscuity of multiple downstream enzymes to access unexplored regions of metabolism. Finally, through modular co-culture fermentations, we demonstrate a plug-and-play bioproduction platform, culminating in the generation of 26 distinct halogenated molecules produced de novo including precursors to prodrugs 4-chloro- and 4-bromo-kynurenine and new-to-nature halogenated beta carbolines.

Fluorescence-Based Screens for Engineering Enzymes Linked to Halogenated Tryptophan.

Directed evolution is often limited by the throughput of accurate screening methods. Here we demonstrate the feasibility of utilizing a singular transcription factor (TF)-system that can be refactored in two ways (both as an activator and repressor). Specifically, we showcase the use of previously evolved 5-halo- or 6-halo-tryptophan-specific TF biosensors suitable for the detection of a halogenated tryptophan molecule in vivo. We subsequently validate the biosensor's utility for two halogenase-specific halo-tryptophan accumulation screens. First, we isolated 5-tryptophan-halogenase, XsHal, from a mixed pool of halogenases with 100% efficiency. Thereafter, we generated a targeted library of the catalytic residue of 6-tryptophan halogenase, Th-Hal, and isolated functioning halogenases with 100% efficiency. Lastly, we refactor the TF circuit to respond to the depletion of halogenated tryptophan and prototype a high-throughput biosensor-directed evolution scheme to screen for downstream enzyme variants capable of promiscuously converting halogenated tryptophan. Altogether, this work takes a significant step toward the rapid and higher throughput screening of halogenases and halo-tryptophan converting enzymes to further reinforce efforts to enable high-level bioproduction of halogenated chemicals.

Continental United States climate projections based on thermodynamic modification of historical weather.

Regional climate models can be used to examine how past weather events might unfold under different climate conditions by simulating analogue versions of those events with modified thermodynamic conditions (i.e., warming signals). Here, we apply this approach by dynamically downscaling a 40-year sequence of past weather from 1980-2019 driven by atmospheric re-analysis, and then repeating this 40-year sequence a total of 8 times using a range of time-evolving thermodynamic warming signals that follow 4 80-year future warming trajectories from 2020-2099. Warming signals follow two emission scenarios (SSP585 and SSP245) and are derived from two groups of global climate models based on whether they exhibit relatively high or low climate sensitivity. The resulting dataset, which contains 25 hourly and over 200 3-hourly variables at 12 km spatial resolution, can be used to examine a plausible range of future climate conditions in direct reference to previously observed weather and enables a systematic exploration of the ways in which thermodynamic change influences the characteristics of historical extreme events.

Near-term tropical cyclone risk and coupled Earth system model biases.

Most current climate models predict that the equatorial Pacific will evolve under greenhouse gas-induced warming to a more El Niño-like state over the next several decades, with a reduced zonal sea surface temperature gradient and weakened atmospheric Walker circulation. Yet, observations over the last 50 y show the opposite trend, toward a more La Niña-like state. Recent research provides evidence that the discrepancy cannot be dismissed as due to internal variability but rather that the models are incorrectly simulating the equatorial Pacific response to greenhouse gas warming. This implies that projections of regional tropical cyclone activity may be incorrect as well, perhaps even in the direction of change, in ways that can be understood by analogy to historical El Niño and La Niña events: North Pacific tropical cyclone projections will be too active, North Atlantic ones not active enough, for example. Other perils, including severe convective storms and droughts, will also be projected erroneously. While it can be argued that these errors are transient, such that the models' responses to greenhouse gases may be correct in equilibrium, the transient response is relevant for climate adaptation in the next several decades. Given the urgency of understanding regional patterns of climate risk in the near term, it would be desirable to develop projections that represent a broader range of possible future tropical Pacific warming scenarios-including some in which recent historical trends continue-even if such projections cannot currently be produced using existing coupled earth system models.

A tripartite microbial co-culture system for de novo biosynthesis of diverse plant phenylpropanoids.

Plant-derived phenylpropanoids, in particular phenylpropenes, have diverse industrial applications ranging from flavors and fragrances to polymers and pharmaceuticals. Heterologous biosynthesis of these products has the potential to address low, seasonally dependent yields hindering ease of widespread manufacturing. However, previous efforts have been hindered by the inherent pathway promiscuity and the microbial toxicity of key pathway intermediates. Here, in this study, we establish the propensity of a tripartite microbial co-culture to overcome these limitations and demonstrate to our knowledge the first reported de novo phenylpropene production from simple sugar starting materials. After initially designing the system to accumulate eugenol, the platform modularity and downstream enzyme promiscuity was leveraged to quickly create avenues for hydroxychavicol and chavicol production. The consortia was found to be compatible with Engineered Living Material production platforms that allow for reusable, cold-chain-independent distributed manufacturing. This work lays the foundation for further deployment of modular microbial approaches to produce plant secondary metabolites.

Enhancing long-term storage and stability of engineered living materials through desiccant storage and trehalose treatment.

Engineered living materials (ELMs) have broad applications for enabling on-demand bioproduction of compounds ranging from small molecules to large proteins. However, most formulations and reports lack the capacity for storage beyond a few months. In this study, we develop an optimized procedure to maximize stress resilience of yeast-laden ELMs through the use of desiccant storage and 10% trehalose incubation before lyophilization. This approach led to over 1-year room temperature storage stability across a range of strain genotypes. In particular, we highlight the superiority of exogenously added trehalose over endogenous, engineered production in yielding robust preservation resilience that is independent of cell state. This simple, effective protocol enables sufficient accumulation of intracellular trehalose over a short period of contact time across a range of strain backgrounds without requiring the overexpression of a trehalose importer. A variety of microscopic analysis including µ-CT and confocal microscopy indicate that cells form spherical colonies within F127-BUM ELMs that have variable viability upon storage. The robustness of the overall procedure developed here highlights the potential for widespread deployment to enable on-demand, cold-chain independent bioproduction.

Global tropical cyclone precipitation scaling with sea surface temperature.

Understanding the relationship between tropical cyclone (TC) precipitation and sea surface temperature (SST) is essential for both TC hazard forecasting and projecting how these hazards will change in the future due to climate change. This work untangles how global TC precipitation is impacted by present-day SST variability (known as apparent scaling) and by long-term changes in SST caused by climate change (known as climate scaling). A variety of datasets are used including precipitation and SST observations, realistic climate model simulations, and idealized climate model simulations. The apparent scaling rates depend on precipitation metric; examples shown here have ranges of 6.1 to 9.5% per K versus 5.9 to 9.8% per K for two different metrics. The climate scaling is estimated at about 5% per K, which is slightly less than the atmospheric moisture scaling based on thermodynamic principles of about 7% per K (i.e., the Clausius-Clapeyron scaling). The apparent scaling is greater than the climate scaling, which implies that the relationship between TC precipitation and present-day SST variability should not be used to project the long-term response of TC precipitation to climate change.

Attribution of 2020 hurricane season extreme rainfall to human-induced climate change.

The 2020 North Atlantic hurricane season was one of the most active on record, causing heavy rains, strong storm surges, and high winds. Human activities continue to increase the amount of greenhouse gases in the atmosphere, resulting in an increase of more than 1 °C in the global average surface temperature in 2020 compared to 1850. This increase in temperature led to increases in sea surface temperature in the North Atlantic basin of 0.4-0.9 °C during the 2020 hurricane season. Here we show that human-induced climate change increased the extreme 3-hourly storm rainfall rates and extreme 3-day accumulated rainfall amounts during the full 2020 hurricane season for observed storms that are at least tropical storm strength (>18 m/s) by 10 and 5%, respectively. When focusing on hurricane strength storms (>33 m/s), extreme 3-hourly rainfall rates and extreme 3-day accumulated rainfall amounts increase by 11 and 8%, respectively.

Machine learning-enabled non-destructive paper chromogenic array detection of multiplexed viable pathogens on food.

Fast and simultaneous identification of multiple viable pathogens on food is critical to public health. Here we report a pathogen identification system using a paper chromogenic array (PCA) enabled by machine learning. The PCA consists of a paper substrate impregnated with 23 chromogenic dyes and dye combinations, which undergo colour changes on exposure to volatile organic compounds emitted by pathogens of interest. These colour changes are digitized and used to train a multi-layer neural network (NN), endowing it with high-accuracy (91-95%) strain-specific pathogen identification and quantification capabilities. The trained PCA-NN system can distinguish between viable Escherichia coli, E. coli O157:H7 and other viable pathogens, and can simultaneously identify both E. coli O157:H7 and Listeria monocytogenes on fresh-cut romaine lettuce, which represents a realistic and complex environment. This approach has the potential to advance non-destructive pathogen detection and identification on food, without enrichment, culturing, incubation or other sample preparation steps.

Clouds and Convective Self-Aggregation in a Multimodel Ensemble of Radiative-Convective Equilibrium Simulations.

The Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) is an intercomparison of multiple types of numerical models configured in radiative-convective equilibrium (RCE). RCE is an idealization of the tropical atmosphere that has long been used to study basic questions in climate science. Here, we employ RCE to investigate the role that clouds and convective activity play in determining cloud feedbacks, climate sensitivity, the state of convective aggregation, and the equilibrium climate. RCEMIP is unique among intercomparisons in its inclusion of a wide range of model types, including atmospheric general circulation models (GCMs), single column models (SCMs), cloud-resolving models (CRMs), large eddy simulations (LES), and global cloud-resolving models (GCRMs). The first results are presented from the RCEMIP ensemble of more than 30 models. While there are large differences across the RCEMIP ensemble in the representation of mean profiles of temperature, humidity, and cloudiness, in a majority of models anvil clouds rise, warm, and decrease in area coverage in response to an increase in sea surface temperature (SST). Nearly all models exhibit self-aggregation in large domains and agree that self-aggregation acts to dry and warm the troposphere, reduce high cloudiness, and increase cooling to space. The degree of self-aggregation exhibits no clear tendency with warming. There is a wide range of climate sensitivities, but models with parameterized convection tend to have lower climate sensitivities than models with explicit convection. In models with parameterized convection, aggregated simulations have lower climate sensitivities than unaggregated simulations.

Improving ionic liquid tolerance in Saccharomyces cerevisiae through heterologous expression and directed evolution of an ILT1 homolog from Yarrowia lipolytica.

Ionic liquids show promise for deconstruction of lignocellulosic biomass prior to fermentation. Yet, imidazolium ionic liquids (IILs) can be toxic to microbes even at concentrations present after recovery. Here, we show that dominant overexpression of an Ilt1p homolog (encoded by YlILT1/YALI0C04884) from the IIL-tolerant yeast Yarrowia lipolytica confers an improvement in 1-ethyl-3-methylimidazolium acetate tolerance in Saccharomyces cerevisiae compared to the endogenous Ilt1p (ScILT1/YDR090C). We subsequently enhance tolerance in S. cerevisiae through directed evolution of YlILT1 using growth-based selection, leading to identification of mutants that grow in up to 3.5% v/v ionic liquid. Lastly, we demonstrate that strains expressing YlILT1 variants demonstrate improved growth rate and ethanol production in the presence of residual IIL. This shows that dominant overexpression of a heterologous protein (wild type or evolved) from an IIL-tolerant yeast can increase tolerance in S. cerevisiae at concentrations relevant to bioethanol production from IIL-treated biomass.

Big Data Research in Pediatric Neurosurgery: Content, Statistical Output, and Bibliometric Analysis.

BACKGROUND/AIMS: We sought to describe pediatric "big data" publications since 2000, their statistical output, and clinical implications. METHODS: We searched 4 major North American neurosurgical journals for articles utilizing non-neurosurgery-specific databases for clinical pediatric neurosurgery research. Articles were analyzed for descriptive and statistical information. We analyzed effect sizes (ESs), confidence intervals (CIs), and p values for clinical relevance. A bibliometric analysis was performed using several key citation metrics. RESULTS: We identified 74 articles, which constituted 1.7% of all pediatric articles (n = 4,436) published, with an exponential increase after 2013 (53/74, 72%). The Healthcare Cost and Utilization Project (HCUP) databases were most frequently utilized (n = 33); hydrocephalus (n = 19) was the most common study topic. The statistical output (n = 49 studies with 464 ESs, 456 CIs, and 389 p values) demonstrated that the majority of the ESs (253/464, 55%) were categorized as small; half or more of the CI spread (CIS) values and p values were high (274/456, 60%) and very strong (195/389, 50%), respectively. Associations with a combination of medium-to-large ESs (i.e., magnitude of difference), medium-to-high CISs (i.e., precision), and strong-to-very strong p values comprised only 20% (75/381) of the reported ESs. The total number of citations for the 74 articles was 1,115 (range per article, 0-129), with the median number of citations per article being 8.5. Four studies had > 50 citations, and 2 of them had > 100 citations. The calculated h-index was 16, h-core citations were 718, the e-index was 21.5, and the Google i10-index was 34. CONCLUSIONS: There has been a dramatic increase in the use of "big data" in the pediatric neurosurgical literature. Reported associations that may, as a group, be of greatest interest to practitioners represented only 20% of the total output from these publications. Citations were weighted towards a few highly cited publications.

Alternatives to carcinogenic preservatives in Chinese Sausage - Sorbic acid-loaded chitosan/tripolyphosphate nanoparticles.

Preservatives in processed meat raise significant concerns associated with bowel cancer and diabetes, and implicate various public health issues. This introduces the need for safer preservatives to uphold public health standards. However, developing safer alternatives to these preservatives poses a significant challenge to food industry. A potential solution to this issue is the use of loaded nanoparticles as preservative agents. This study investigated antimicrobial and antioxidant effects of sorbic acid-loaded chitosan/tripolyphosphate nanoparticles (SAN) in Chinese Sausage. SAN were prepared through ionic gelation, followed by natural air-drying for 20 days. After preparation, the antimicrobial and antioxidant activities of various treatment groups were analyzed intermittently during storage at room temperature. SAN-treated samples had significantly lower levels of surviving bacteria, molds, and yeasts than the blank control (p < 0.05) over the entire 72 days of storage. Additionally the SAN-treated samples also had lower levels of surviving bacteria than the chitosan/tripolyphosphate samples after 31-56 days of storage (p < 0.05). The thiobarbituric acid value and pH of the SAN-treated samples were also significantly lower than the blank control (p < 0.05). These results indicated that SAN could be a good intervention strategy to retard lipid oxidation and inhibit microbial growth in Chinese Sausage.

Expanding beyond canonical metabolism: Interfacing alternative elements, synthetic biology, and metabolic engineering.

Metabolic engineering offers an exquisite capacity to produce new molecules in a renewable manner. However, most industrial applications have focused on only a small subset of elements from the periodic table, centered around carbon biochemistry. This review aims to illustrate the expanse of chemical elements that can currently (and potentially) be integrated into useful products using cellular systems. Specifically, we describe recent advances in expanding the cellular scope to include the halogens, selenium and the metalloids, and a variety of metal incorporations. These examples range from small molecules, heteroatom-linked uncommon elements, and natural products to biomining and nanotechnology applications. Collectively, this review covers the promise of an expanded range of elemental incorporations and the future impacts it may have on biotechnology.

T7 Polymerase Expression of Guide RNAs in vivo Allows Exportable CRISPR-Cas9 Editing in Multiple Yeast Hosts.

Efficient guide RNA expression often limits CRISPR-Cas9 implementation in new hosts. To address this limitation in fungal systems, we demonstrate the utility of a T7 polymerase system to effectively express sgRNAs. Initially, we developed a methodology in Saccharomyces cerevisiae using a modified version of the T7 P266L mutant polymerase with an SV40 nuclear localization signal to allow guide RNA expression immediately downstream of a T7 promoter. To improve targeting efficiency, guide RNA design was found to be tolerant to three mismatches or up to three additional bases appended to the 5' end. The addition of three guanines to a T7-based guide RNA improved guide RNA expression 80-fold and achieved transcriptional output similar to the strong Pol III snr52 promoter. Resulting gene editing and dCas9-guided gene regulation with a T7-based guide RNA was on par with the commonly used snr52 system in S. cerevisiae. Finally, 96% and 60% genome editing efficiencies were achieved in Kluyveromyces lactis and Yarrowia lipolytica respectively with minimal optimization of this system. Thus, T7-based expression of sgRNAs offers an orthogonal method for implementing CRISPR systems in fungal systems.