Molecular mechanism of trehalose 6-phosphate inhibition of the plant metabolic sensor kinase SnRK1

SUCROSE-NON-FERMENTING1-RELATED PROTEIN KINASE1 (SnRK1), a central plant metabolic sensor kinase, phosphorylates its target proteins, triggering a global shift from anabolism to catabolism. Molecular modeling revealed that upon binding of KIN10 to GEMINIVIRUS REP-INTERACTING KINASE1 (GRIK1), KIN10's activation T-loop reorients into GRIK1's active site, enabling its phosphorylation and activation. Trehalose 6-phosphate (T6P) is a proxy for cellular sugar status and a potent inhibitor of SnRK1. T6P binds to KIN10, a SnRK1 catalytic subunit, weakening its affinity for GRIK1. Here, we investigate the molecular details of T6P inhibition of KIN10. Molecular dynamics simulations and in vitro phosphorylation assays identified and validated the T6P binding site on KIN10. Under high-sugar conditions, T6P binds to KIN10, blocking the reorientation of its activation loop and preventing its phosphorylation and activation by GRIK1. Under these conditions, SnRK1 maintains only basal activity levels, minimizing phosphorylation of its target proteins, thereby facilitating a general shift from catabolism to anabolism.

Ultrathin Magnesium-Based Coating as an Efficient Oxygen Barrier for Superconducting Circuit Materials

Scaling up superconducting quantum circuits based on transmon qubits necessitates substantial enhancements in qubit coherence time. Over recent years, tantalum (Ta) has emerged as a promising candidate for transmon qubits, surpassing conventional counterparts in terms of coherence time. However, amorphous surface Ta oxide layer may introduce dielectric loss, ultimately placing a limit on the coherence time. In this study, a novel approach for suppressing the formation of tantalum oxide using an ultrathin magnesium (Mg) capping layer is presented. Synchrotron-based X-ray photoelectron spectroscopy studies demonstrate that oxide is confined to an extremely thin region directly beneath the Mg/Ta interface. Additionally, it is demonstrated that the superconducting properties of thin Ta films are improved following the Mg capping, exhibiting sharper and higher-temperature transitions to superconductive and magnetically ordered states. Moreover, an atomic-scale mechanistic understanding of the role of the capping layer in protecting Ta from oxidation is established based on computational modeling. This work provides valuable insights into the formation mechanism and functionality of surface tantalum oxide, as well as a new materials design principle with the potential to reduce dielectric loss in superconducting quantum materials. Ultimately, the findings pave the way for the realization of large-scale, high-performance quantum computing systems.

Physical science research needed to evaluate the viability and risks of marine cloud brightening

Marine cloud brightening (MCB) is the deliberate injection of aerosol particles into shallow marine clouds to increase their reflection of solar radiation and reduce the amount of energy absorbed by the climate system. From the physical science perspective, the consensus of a broad international group of scientists is that the viability of MCB will ultimately depend on whether observations and models can robustly assess the scale-up of local-to-global brightening in today's climate and identify strategies that will ensure an equitable geographical distribution of the benefits and risks associated with projected regional changes in temperature and precipitation. To address the physical science knowledge gaps required to assess the societal implications of MCB, we propose a substantial and targeted program of research-field and laboratory experiments, monitoring, and numerical modeling across a range of scales.

Cell-type-specific transcriptomics uncovers spatial regulatory networks in bioenergy sorghum stems

Bioenergy sorghum is a low-input, drought-resilient, deep-rooting annual crop that has high biomass yield potential enabling the sustainable production of biofuels, biopower, and bioproducts. Bioenergy sorghum's 4-5 m stems account for ~80% of the harvested biomass. Stems accumulate high levels of sucrose that could be used to synthesize bioethanol and useful biopolymers if information about cell-type gene expression and regulation in stems was available to enable engineering. To obtain this information, laser capture microdissection was used to isolate and collect transcriptome profiles from five major cell types that are present in stems of the sweet sorghum Wray. Transcriptome analysis identified genes with cell-type-specific and cell-preferred expression patterns that reflect the distinct metabolic, transport, and regulatory functions of each cell type. Analysis of cell-type-specific gene regulatory networks (GRNs) revealed that unique transcription factor families contribute to distinct regulatory landscapes, where regulation is organized through various modes and identifiable network motifs. Cell-specific transcriptome data was combined with known secondary cell wall (SCW) networks to identify the GRNs that differentially activate SCW formation in vascular sclerenchyma and epidermal cells. The spatial transcriptomic dataset provides a valuable source of information about the function of different sorghum cell types and GRNs that will enable the engineering of bioenergy sorghum stems, and an interactive web application developed during this project will allow easy access and exploration of the data (https://mc-lab.shinyapps.io/lcm-dataset/).

Controlling Product Distribution of Polyethylene Hydrogenolysis Using Bimetallic RuM3 (M = Fe, Co, Ni) Catalysts

Plastic hydrogenolysis is an attractive approach for producing value-added chemicals due to its mild reaction conditions, but controlling product distribution is challenging due to the formation of undesired CH4. This work reports several bimetallic RuM3/CeO2 (M = Fe, Co, Ni) catalysts that shift the product of low-density polyethylene hydrogenolysis toward longer-chain hydrocarbons. These catalysts were characterized by using X-ray absorption fine structure spectroscopy, electron microscopy imaging, and H2 temperature-programmed reduction. The combined catalytic evaluation and characterization results revealed that the product distribution was regulated by the formation of bimetallic alloys. A model compound, n-hexadecane, was selected to further understand the differences in hydrogenolysis over the Ru-based catalysts. Although a longer reaction time shifted the product toward smaller molecules, the bimetallic (RuCo3/CeO2) catalyst limited the further conversion of C2-C5 into CH4. This work highlights the role of bimetallic alloys in tailoring the interaction with hydrocarbons, thereby controlling the product distribution of polymer hydrogenolysis.

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

Silicon-Lattice-Matched Boron-Doped Gallium Phosphide: A Scalable Acousto-Optic Platform

The compact size, scalability, and strongly confined fields in integrated photonic devices enable new functionalities in photonic networking and information processing, both classical and quantum. Gallium phosphide (GaP) is a promising material for active integrated photonics due to its high refractive index, wide bandgap, strong nonlinear properties, and large acousto-optic figure of merit. This study demonstrates that silicon-lattice-matched boron-doped GaP (BGaP), grown at the 12-inch wafer scale, provides similar functionalities as GaP. BGaP optical resonators exhibit intrinsic quality factors exceeding 25,000 and 200,000 at visible and telecom wavelengths, respectively. It further demonstrates the electromechanical generation of low-loss acoustic waves and an integrated acousto-optic (AO) modulator. High-resolution spatial and compositional mapping, combined with ab initio calculations, indicate two candidates for the excess optical loss in the visible band: the silicon-GaP interface and boron dimers. These results demonstrate the promise of the BGaP material platform for the development of scalable AO technologies at telecom and provide potential pathways toward higher performance at shorter wavelengths.

Evaluating Stage Motion for Automated Electron Microscopy

Precise control is an essential and elusive quality of emerging self-driving transmission electron microscopes (TEMs). It is widely understood these instruments must be capable of performing rapid, high-volume, and arbitrary movements for practical self-driving operation. However, stage movements are difficult to automate at scale, owing to mechanical instability, hysteresis, and thermal drift. Such difficulties pose major barriers to artificial intelligence-directed microscope designs that require repeatable, precise movements. To guide design of emerging instruments, it is necessary to understand the behavior of existing mechanisms to identify rate limiting steps for full autonomy. Here, we describe a general framework to evaluate stage motion in any TEM. We define metrics to evaluate stage degrees of freedom, propose solutions to improve performance, and comment on fundamental limits to automated experimentation using present hardware.

Decarbonization of Agriculture: The Greenhouse Gas Impacts and Economics of Existing and Emerging Climate-Smart Practices

The worldwide emphasis on reducing greenhouse gas (GHG) emissions has increased focus on the potential to mitigate emissions through climate-smart agricultural practices, including regenerative, digital, and controlled environment farming systems. The effectiveness of these solutions largely depends on their ability to address environmental concerns, generate economic returns, and meet supply chain needs. In this Review, we summarize the state of knowledge on the GHG impacts and profitability of these three existing and emerging farming systems. Although we find potential for CO2 mitigation in all three approaches (depending on site-specific and climatic factors), we point to the greater level of research covering the efficacy of regenerative and digital agriculture in tackling non-CO2 emissions (i.e., N2O and CH4), which account for the majority of agriculture's GHG footprint. Despite this greater research coverage, we still find significant methodological and data limitations in accounting for the major GHG fluxes of these practices, especially the lifetime CH4 footprint of more nascent climate-smart regenerative agriculture practices. Across the approaches explored, uncertainties remain about the overall efficacy and persistence of mitigation-particularly with respect to the offsetting of soil carbon sequestration gains by N2O emissions and the lifecycle emissions of controlled environment agriculture systems compared to traditional systems. We find that the economic feasibility of these practices is also system-specific, although regenerative agriculture is generally the most accessible climate-smart approach. Robust incentives (including carbon credit considerations), investments, and policy changes would make these practices more financially accessible to farmers.

Designed Metal-Containing Peptoid Membranes as Enzyme Mimetics for Catalytic Organophosphate Degradation

The detoxification of lethal organophosphate (OP) residues in the environment is crucial to prevent human exposure and protect modern society. Despite serving as excellent catalysts for OP degradation, natural enzymes require costly preparation and readily deactivate upon exposure to environmental conditions. Herein, we designed and prepared a series of phosphotriesterase mimics based on stable, self-assembled peptoid membranes to overcome these limitations of the enzymes and effectively catalyze the hydrolysis of dimethyl p-nitrophenyl phosphate (DMNP)─a nerve agent simulant. By covalently attaching metal-binding ligands to peptoid N-termini, we attained enzyme mimetics in the form of surface-functionalized crystalline nanomembranes. These nanomembranes display a precisely controlled arrangement of coordinated metal ions, which resemble the active sites found in phosphotriesterases to promote DMNP hydrolysis. Moreover, using these highly programmable peptoid nanomembranes allows for tuning the local chemical environment of the coordinated metal ion to achieve enhanced hydrolysis activity. Among the crystalline membranes that are active for DMNP degradation, those assembled from peptoids containing bis-quinoline ligands with an adjacent phenyl side chain showed the highest hydrolytic activity with a 219-fold rate acceleration over the background, demonstrating the important role of the hydrophobic environment in proximity to the active sites. Furthermore, these membranes exhibited remarkable stability and were able to retain their catalytic activity after heating to 60 °C and after multiple uses. This work provides insights into the principal features to construct a new class of biomimetic materials with high catalytic efficiency, cost-effectiveness, and reusability applied in nerve agent detoxification.

Tracking Initial Fe(II)-Driven Ferrihydrite Transformations: A Mössbauer Spectroscopy and Isotope Investigation

Transformation of nanocrystalline ferrihydrite to more stable microcrystalline Fe(III) oxides is rapidly accelerated under reducing conditions with aqueous Fe(II) present. While the major steps of Fe(II)-catalyzed ferrihydrite transformation are known, processes in the initial phase that lead to nucleation and the growth of product minerals remain unclear. To track ferrihydrite-Fe(II) interactions during this initial phase, we used Fe isotopes, Mössbauer spectroscopy, and extractions to monitor the structural, magnetic, and isotope composition changes of ferrihydrite within ∼30 min of Fe(II) exposure. We observed rapid isotope mixing between aqueous Fe(II) and ferrihydrite during this initial lag phase. Our findings from Mössbauer spectroscopy indicate that a more magnetically ordered Fe(III) phase initially forms that is distinct from ferrihydrite and bulk crystalline transformation products. The signature of this phase is consistent with the early stage emergence of lepidocrocite-like lamellae observed in previous transmission electron microscopy studies. Its signature is furthermore removed by xylenol extraction of Fe(III), the same approach used to identify a chemically labile form of Fe(III) resulting from Fe(II) contact that is correlated to the ultimate emergence of crystalline product phases detectable by X-ray diffraction. Our work indicates that the mineralogical changes in the initial lag phase of Fh transformation initiated by Fe(II)-Fh electron transfer are critical to understanding ferrihydrite behavior in soils and sediments, particularly with regard to metal uptake and release.

Elucidating regulatory processes of intense physical activity by multi-omics analysis

BACKGROUND

Physiological and biochemical processes across tissues of the body are regulated in response to the high demands of intense physical activity in several occupations, such as firefighting, law enforcement, military, and sports. A better understanding of such processes can ultimately help improve human performance and prevent illnesses in the work environment.

METHODS

To study regulatory processes in intense physical activity simulating real-life conditions, we performed a multi-omics analysis of three biofluids (blood plasma, urine, and saliva) collected from 11 wildland firefighters before and after a 45 min, intense exercise regimen. Omics profiles post- versus pre-exercise were compared by Student's t-test followed by pathway analysis and comparison between the different omics modalities.

RESULTS

Our multi-omics analysis identified and quantified 3835 proteins, 730 lipids and 182 metabolites combining the 3 different types of samples. The blood plasma analysis revealed signatures of tissue damage and acute repair response accompanied by enhanced carbon metabolism to meet energy demands. The urine analysis showed a strong, concomitant regulation of 6 out of 8 identified proteins from the renin-angiotensin system supporting increased excretion of catabolites, reabsorption of nutrients and maintenance of fluid balance. In saliva, we observed a decrease in 3 pro-inflammatory cytokines and an increase in 8 antimicrobial peptides. A systematic literature review identified 6 papers that support an altered susceptibility to respiratory infection.

CONCLUSION

This study shows simultaneous regulatory signatures in biofluids indicative of homeostatic maintenance during intense physical activity with possible effects on increased infection susceptibility, suggesting that caution against respiratory diseases could benefit workers on highly physical demanding jobs.

Understanding the Role of Zinc Hydroxide Sulfate and its Analogues in Mildly Acidic Aqueous Zinc Batteries: A Review

Mildly acidic aqueous zinc batteries (AZBs) have attracted tremendous attention for grid storage applications with the expectation to tackle the issues of Li-ion batteries on high cost and poor safety. However, the performance, particularly energy density and cycle stability of AZBs are still unsatisfactory when compared with LIBs. To help the development of AZBs, a lot of effort have been made to understand the battery reaction mechanisms and precedent microscopic and spectroscopic analyses have shown flake-like large particles of zinc hydroxide sulfate (ZHS) and its analogues formed on the surfaces of cathodes and anodes in sulfate and other electrolyte systems during cycling. However, because of the complexity of the thermodynamics and kinetics of aqueous reactions to understand different battery conditions, controversies still exist. This article will review the roles of ZHS discussed in recent representative references aiming to shine light on the fundamental mechanisms of AZBs and pave ways to further improve the battery performance.

Dose estimates and their uncertainties for use in epidemiological studies of radiation-exposed populations in the Russian Southern Urals

Many residents of the Russian Southern Urals were exposed to radioactive environmental pollution created by the operations of the Mayak Production Association in the mid- 20th century. There were two major releases: the discharge of about 1x1017 Bq of liquid waste into the Techa River between 1949 and 1959; and the atmospheric release of 7.4 * 1016 Bq as a result an explosion in the radioactive waste-storage facility in 1957. The releases into the Techa River resulted in the exposure of more than 30,000 people who lived in riverside villages between 1950 and 1961. The 1957 accident contaminated a larger area with the highest exposure levels in an area that is called the East Urals Radioactive Trace (EURT). Current epidemiologic studies of the exposed populations are based on dose estimates obtained using a Monte-Carlo dosimetry system (TRDS-2016MC) that provides multiple realizations of the annual doses for each cohort member. These dose realizations provide a central estimate of the individual dose and information on the uncertainty of these dose estimates. In addition, the correlation of individual annual doses over realizations provides important information on shared uncertainties that can be used to assess the impact of shared dose uncertainties on risk estimate uncertainty.This paper considers dose uncertainties in the TRDS-2016MC. Individual doses from external and internal radiation sources were reconstructed for 48,036 people based on environmental contamination patterns, residential histories, individual 90Sr body-burden measurements and dietary intakes. Dietary intake of 90Sr resulted in doses accumulated in active bone marrow (or simply, marrow) that were an order of magnitude greater than those in soft tissues. About 84% of the marrow dose and 50% of the stomach dose was associated with internal exposures. The lognormal distribution is well-fitted to the individual dose realizations, which, therefore, could be expressed and easily operated in terms of geometric mean (GM) and geometric standard deviation (GSD). Cohort average GM for marrow and stomach cumulative doses are 0.21 and 0.03 Gy, respectively. Cohort average dose uncertainties in terms of GSD are as follows: for marrow it is 2.93 (90%CI: 2.02-4.34); for stomach and the other non-calcified tissues it is 2.32 (90% CI: 1.78-2.9).

Novel Focused Ion Beam Liftouts for Spatial Characterization of Spherical Biominerals With Transmission Electron Microscopy

Focused ion beam (FIB) is frequently used to prepare electron- and X-ray-beam-transparent thin sections of samples, called lamellae. Typically, lamellae are prepared from only a subregion of a sample. In this paper, we present a novel approach for FIB lamella preparation of microscopic samples, wherein the entire cross-section of the whole sample can be investigated. The approach was demonstrated using spherical, porous, and often hollow microprecipitates of biologically precipitated calcium carbonate. The microprecipitate morphology made these biogenic samples more fragile and challenging than materials commonly investigated using FIB lamellae. Our method enables the appropriate orientation of the lamellae required for further electron/X-ray analyses after attachment to the transmission electron microscopy (TEM) grid post and facilitates more secure adhesion onto the grid post. We present evidence of autofluorescence in bacterially precipitated vaterite using this lamella preparation method coupled with TEM selected area diffraction. This innovative approach allows studying biomineralization at the micro to nano scales, which can provide novel insights into bacterial responses to microenvironmental conditions.

Understanding palladium-tellurium cluster formation on WTe2: From a kinetically hindered distribution to thermodynamically controlled monodispersity

A fundamental understanding of the transition metal dichalcogenide (TMDC)-metal interface is critical for their utilization in a broad range of applications. We investigate how the deposition of palladium (Pd), as a model metal, on WTe₂(001), leads to the assembly of Pd into clusters and nanoparticles. Using X-ray photoemission spectroscopy, scanning tunneling microscopy imaging, and ab initio simulations, we find that Pd nucleation is driven by the interaction with and the availability of mobile excess tellurium (Te) leading to the formation of Pd-Te clusters at room temperature. Surprisingly, the nucleation of Pd-Te clusters is not affected by intrinsic surface defects, even at elevated temperatures. Upon annealing, the Pd-Te nanoclusters adopt an identical nanostructure and are stable up to ∼523 K. Density functional theory calculations provide a foundation for our understanding of the mobility of Pd and Te atoms, preferential nucleation of Pd-Te clusters, and the origin of their annealing-induced monodispersity. These results highlight the role the excess chalcogenide atoms may play in the metal deposition process. More broadly, the discoveries of synthetic pathways yielding thermally robust monodispersed nanostructures on TMDCs are critical to the manufacturing of novel quantum and microelectronics devices and catalytically active nano-alloy centers.

Co-Assembly of Carbon Nanotube Porins into Biomimetic Peptoid Membranes

Robust and cost-effective membrane-based separations are essential to solving many global crises, such as the lack of clean water. Even though the current polymer-based membranes are widely used for separations, their performance and precision can be enhanced by using a biomimetic membrane architecture that consists of highly permeable and selective channels embedded in a universal membrane matrix. Researchers have shown that artificial water and ion channels, such as carbon nanotube porins (CNTPs), embedded in lipid membranes can deliver strong separation performance. However, their applications are limited by the relative fragility and low stability of the lipid matrix. In this work, we demonstrate that CNTPs can co-assemble into two dimension (2D) peptoid membrane nanosheets, opening up a way to produce highly programmable synthetic membranes with superior crystallinity and robustness. A combination of molecular dynamics (MD) simulations, Raman spectroscopy, X-ray diffraction (XRD), and atomic force microscopy (AFM) measurements to verify the co-assembly of CNTP and peptoids are used and show that it does not disrupt peptoid monomer packing within the membrane. These results provide a new option for designing affordable artificial membranes and highly robust nanoporous solids.

Opportunities for human factors in machine learning

Introduction

The field of machine learning and its subfield of deep learning have grown rapidly in recent years. With the speed of advancement, it is nearly impossible for data scientists to maintain expert knowledge of cutting-edge techniques. This study applies human factors methods to the field of machine learning to address these difficulties.

Methods

Using semi-structured interviews with data scientists at a National Laboratory, we sought to understand the process used when working with machine learning models, the challenges encountered, and the ways that human factors might contribute to addressing those challenges.

Results

Results of the interviews were analyzed to create a generalization of the process of working with machine learning models. Issues encountered during each process step are described.

Discussion

Recommendations and areas for collaboration between data scientists and human factors experts are provided, with the goal of creating better tools, knowledge, and guidance for machine learning scientists.

Unraveling Unique Surface Chemistry of Transition Metal Nitrides in Controlling Selective C-O Bond Scission Pathways of Glycerol

Controlled C-O bond scission is an important step for upgrading glycerol, a major byproduct from the continuously increasing biodiesel production. Transition metal nitride catalysts have been identified as promising hydrodeoxygenation (HDO) catalysts, but fundamental understanding regarding the active sites of the catalysts and reaction mechanism remains unclear. This work demonstrates a fundamental surface science study of Mo₂N and Cu/Mo₂N for the selective HDO reaction of glycerol, using a combination of model surface experiments and first-principles calculations. Temperature-programmed desorption (TPD) experiments showed that clean Mo₂N cleaved two or three C-O bonds of glycerol to produce allyl alcohol, propanal, and propylene. The addition of Cu to Mo₂N changed the reaction pathway to one C-O bond scission to produce acetol. High-resolution electron energy loss spectroscopy (HREELS) results identified the surface intermediates, showing a facile C-H bond activation on Mo₂N. Density functional theory (DFT) calculations revealed that the surface N on Mo₂N interacted with the H atoms in glycerol and blocked some Mo sites to enable selective C-O bond scission. This work shows that Mo₂N and Cu/Mo₂N are active and selective for the controlled C-O bond scission of glycerol and in turn provides insights into the rational catalyst design for selective oxygen removal of relevant biomass-derived oxygenates.

Rapid growth of anthropogenic organic nanoparticles greatly alters cloud life cycle in the Amazon rainforest

Aerosol-cloud interactions remain uncertain in assessing climate change. While anthropogenic activities produce copious aerosol nanoparticles smaller than 10 nanometers, they are too small to act as efficient cloud condensation nuclei (CCN). The mechanisms responsible for particle growth to CCN-relevant sizes are poorly understood. Here, we present aircraft observations of rapid growth of anthropogenic nanoparticles downwind of an isolated metropolis in the Amazon rainforest. Model analysis reveals that the sustained particle growth to CCN sizes is predominantly caused by particle-phase diffusion-limited partitioning of semivolatile oxidation products of biogenic hydrocarbons. Cloud-resolving numerical simulations show that the enhanced CCN concentrations in the urban plume substantially alter the formation of shallow convective clouds, suppress precipitation, and enhance the transition to deep convective clouds. The proposed nanoparticle growth mechanism, expressly enabled by the abundantly formed semivolatile organics, suggests an appreciable impact of anthropogenic aerosols on cloud life cycle in previously unpolluted forests of the world.

Deciphering the microbial and molecular responses of geographically diverse Setaria accessions grown in a nutrient-poor soil

The microbial and molecular characterization of the ectorhizosphere is an important step towards developing a more complete understanding of how the cultivation of biofuel crops can be undertaken in nutrient poor environments. The ectorhizosphere of Setaria is of particular interest because the plant component of this plant-microbe system is an important agricultural grain crop and a model for biofuel grasses. Importantly, Setaria lends itself to high throughput molecular studies. As such, we have identified important intra- and interspecific microbial and molecular differences in the ectorhizospheres of three geographically distant Setaria italica accessions and their wild ancestor S. viridis. All were grown in a nutrient-poor soil with and without nutrient addition. To assess the contrasting impact of nutrient deficiency observed for two S. italica accessions, we quantitatively evaluated differences in soil organic matter, microbial community, and metabolite profiles. Together, these measurements suggest that rhizosphere priming differs with Setaria accession, which comes from alterations in microbial community abundances, specifically Actinobacteria and Proteobacteria populations. When globally comparing the metabolomic response of Setaria to nutrient addition, plants produced distinctly different metabolic profiles in the leaves and roots. With nutrient addition, increases of nitrogen containing metabolites were significantly higher in plant leaves and roots along with significant increases in tyrosine derived alkaloids, serotonin, and synephrine. Glycerol was also found to be significantly increased in the leaves as well as the ectorhizosphere. These differences provide insight into how C₄ grasses adapt to changing nutrient availability in soils or with contrasting fertilization schemas. Gained knowledge could then be utilized in plant enhancement and bioengineering efforts to produce plants with superior traits when grown in nutrient poor soils.

In-Home Mobility Frequency and Stability in Older Adults Living Alone With or Without MCI: Introduction of New Metrics

Background: Older adults spend a considerable amount of time inside their residences; however, most research investigates out-of-home mobility and its health correlates. We measured indoor mobility using room-to-room transitions, tested their psychometric properties, and correlated indoor mobility with cognitive and functional status. Materials and Methods: Community-dwelling older adults living alone (n = 139; age = 78.1 ± 8.6 years) from the Oregon Center for Aging & Technology (ORCATECH) and Minority Aging Research Study (MARS) were included in the study. Two indoor mobility features were developed using non-parametric parameters (frequency; stability): Indoor mobility frequency (room-to-room transitions/day) was detected using passive infrared (PIR) motion sensors fixed on the walls in four geographic locations (bathroom; bedroom; kitchen; living room) and using door contact sensors attached to the egress door in the entrance. Indoor mobility stability was estimated by variances of number of room-to-room transitions over a week. Test-retest reliability (Intra-class coefficient, ICC) and the minimal clinically important difference (MCID) defined as the standard error of measurement (SEM) were generated. Generalized estimating equations models related mobility features with mild cognitive impairment (MCI) and functional status (gait speed). Results: An average of 206 days (±127) of sensor data were analyzed per individual. Indoor mobility frequency and stability showed good to excellent test-retest reliability (ICCs = 0.91[0.88-0.94]; 0.59[0.48-0.70]). The MCIDs of mobility frequency and mobility stability were 18 and 0.09, respectively. On average, a higher indoor mobility frequency was associated with faster gait speed (β = 0.53, p = 0.04), suggesting an increase of 5.3 room-to-room transitions per day was associated with an increase of 10 cm/s gait speed. A decrease in mobility stability was associated with MCI (β = -0.04, p = 0.03). Discussion: Mobility frequency and stability in the home are clinically meaningful and reliable features. Pervasive-sensing systems deployed in homes can objectively reveal cognitive and functional status in older adults who live alone.

Groundwater residence time estimates obscured by anthropogenic carbonate

Groundwater is an important source of drinking and irrigation water. Dating groundwater informs its vulnerability to contamination and aids in calibrating flow models. Here, we report measurements of multiple age tracers (¹⁴C, ³H, ³⁹Ar, and ⁸⁵Kr) and parameters relevant to dissolved inorganic carbon (DIC) from 17 wells in California's San Joaquin Valley (SJV), an agricultural region that is heavily reliant on groundwater. We find evidence for a major mid-20th century shift in groundwater DIC input from mostly closed- to mostly open-system carbonate dissolution, which we suggest is driven by input of anthropogenic carbonate soil amendments. Crucially, enhanced open-system dissolution, in which DIC equilibrates with soil CO₂, fundamentally affects the initial ¹⁴C activity of recently recharged groundwater. Conventional ¹⁴C dating of deeper SJV groundwater, assuming an open system, substantially overestimates residence time and thereby underestimates susceptibility to modern contamination. Because carbonate soil amendments are ubiquitous, other groundwater-reliant agricultural regions may be similarly affected.

Further insights into the Fe(ii) reduction of 2-line ferrihydrite: a semi in situ and in situ TEM study

The biotic or abiotic reduction of nano-crystalline 2-line ferrihydrite (2-line FH) into more thermodynamically stable phases such as lepidocrocite-LP, goethite-GT, magnetite-MG, and hematite-HT plays an important role in the geochemical cycling of elements and nutrients in aqueous systems. In our study, we employed the use of in situ liquid cell (LC) and semi in situ analysis in an environmental TEM to gain further insights at the micro/nano-scale into the reaction mechanisms by which Fe(ii)_(aq) catalyzes 2-line FH. We visually observed for the first time the following intermediate steps: (1) formation of round and wire-shaped precursor nano-particles arising only from Fe(ii)_(aq), (2) two distinct dissolution mechanisms for 2 line-FH (i.e. reduction of size and density as well as breakage through smaller nano-particles), (3) lack of complete dissolution of 2-line FH (i.e. "induction-period"), (4) an amorphous phase growth ("reactive-FH/labile Fe(iii) phase") on 2 line-FH, (5) deposition of amorphous nano-particles on the surface of 2 line-FH and (6) assemblage of elongated crystalline lamellae to form tabular LP crystals. Furthermore, we observed phenomena consistent with the movement of adsorbate ions from solution onto the surface of a Fe(iii)-oxy/hydroxide crystal. Thus our work here reveals that the catalytic transformation of 2-line FH by Fe(ii)_(aq) at the micro/nano scale doesn't simply occur via dissolution-reprecipitation or surface nucleation-solid state conversion mechanisms. Rather, as we demonstrate here, it is an intricate chemical process that goes through a series of intermediate steps not visible through conventional lab or synchrotron bulk techniques. However, such intermediate steps may affect the environmental fate, bioavailability, and transport of elements of such nano-particles in aqueous environments.

Mechanistic insight into biopolymer induced iron oxide mineralization through quantification of molecular bonding

Microbial production of iron (oxyhydr)oxides on polysaccharide rich biopolymers occurs on such a vast scale that it impacts the global iron cycle and has been responsible for major biogeochemical events. Yet the physiochemical controls these biopolymers exert on iron (oxyhydr)oxide formation are poorly understood. Here we used dynamic force spectroscopy to directly probe binding between complex, model and natural microbial polysaccharides and common iron (oxyhydr)oxides. Applying nucleation theory to our results demonstrates that if there is a strong attractive interaction between biopolymers and iron (oxyhydr)oxides, the biopolymers decrease the nucleation barriers, thus promoting mineral nucleation. These results are also supported by nucleation studies and density functional theory. Spectroscopic and thermogravimetric data provide insight into the subsequent growth dynamics and show that the degree and strength of water association with the polymers can explain the influence on iron (oxyhydr)oxide transformation rates. Combined, our results provide a mechanistic basis for understanding how polymer-mineral-water interactions alter iron (oxyhydr)oxides nucleation and growth dynamics and pave the way for an improved understanding of the consequences of polymer induced mineralization in natural systems.

Remodeling of the Histoplasma Capsulatum Membrane Induced by Monoclonal Antibodies

Antibodies play a central role in host immunity by directly inactivating or recognizing an invading pathogen to enhance different immune responses to combat the invader. However, the cellular responses of pathogens to the presence of antibodies are not well-characterized. Here, we used different mass spectrometry techniques to study the cellular responses of the pathogenic fungus Histoplasma capsulatum to monoclonal antibodies (mAb) against HSP60, the surface protein involved in infection. A proteomic analysis of H. capsulatum yeast cells revealed that mAb binding regulates a variety of metabolic and signaling pathways, including fatty acid metabolism, sterol metabolism, MAPK signaling and ubiquitin-mediated proteolysis. The regulation of the fatty acid metabolism was accompanied by increases in the level of polyunsaturated fatty acids, which further augmented the degree of unsaturated lipids in H. capsulatum's membranes and energy storage lipids, such as triacylglycerols, phosphatidylcholines, phosphatidylethanolamines and phosphatidylinositols. MAb treatment also regulated sterol metabolism by increasing the levels of cholesterol and ergosterol in the cells. We also showed that global changes in the lipid profiles resulted in an increased susceptibility of H. capsulatum to the ergosterol-targeting drug amphotericin B. Overall, our data showed that mAb induction of global changes in the composition of H. capsulatum membranes can potentially impact antifungal treatment during histoplasmosis.

On the regeneration of fish scales: structure and mechanical behavior

Fish scales serve as a dermal armor that provides protection from physical injury. Owing to a number of outstanding properties, fish scales are inspiring new concepts for layered engineered materials and next-generation flexible armors. Although past efforts have primarily focused on the structure and mechanical behavior of ontogenetic scales, the structure-property relationships of regenerated scales have received limited attention. In the present study, common carp (Cyprinus carpio) acquired from the wild were held live in an aquatic laboratory at 10°C and 20°C. Ontogenetic scales were extracted from the fish for analysis, as well as regenerated scales after approximately 1 year of development and growth. Their microstructure was characterized using microscopy and Raman spectroscopy, and the mechanical properties were evaluated in uniaxial tension to failure under hydrated conditions. The strength, strain to fracture and toughness of the regenerated scales were significantly lower than those of ontogenetic scales from the same fish, regardless of the water temperature. Scales that regenerated at 20°C exhibited significantly higher strength, strain to fracture and toughness than those regenerated at 10°C. The regenerated scales exhibited a highly mineralized outer layer, but no distinct limiting layer or external elasmodine; they also possessed a significantly lower number of plies in the basal layer than the ontogenetic scales. The results suggest that a mineralized layer develops preferentially during scale regeneration with the topology needed for protection, prior to the development of other qualities.

Genetic and metabolic links between the murine microbiome and memory

BACKGROUND

Recent evidence has linked the gut microbiome to host behavior via the gut-brain axis [1-3]; however, the underlying mechanisms remain unexplored. Here, we determined the links between host genetics, the gut microbiome and memory using the genetically defined Collaborative Cross (CC) mouse cohort, complemented with microbiome and metabolomic analyses in conventional and germ-free (GF) mice.

RESULTS

A genome-wide association analysis (GWAS) identified 715 of 76,080 single-nucleotide polymorphisms (SNPs) that were significantly associated with short-term memory using the passive avoidance model. The identified SNPs were enriched in genes known to be involved in learning and memory functions. By 16S rRNA gene sequencing of the gut microbial community in the same CC cohort, we identified specific microorganisms that were significantly correlated with longer latencies in our retention test, including a positive correlation with Lactobacillus. Inoculation of GF mice with individual species of Lactobacillus (L. reuteri F275, L. plantarum BDGP2 or L. brevis BDGP6) resulted in significantly improved memory compared to uninoculated or E. coli DH10B inoculated controls. Untargeted metabolomics analysis revealed significantly higher levels of several metabolites, including lactate, in the stools of Lactobacillus-colonized mice, when compared to GF control mice. Moreover, we demonstrate that dietary lactate treatment alone boosted memory in conventional mice. Mechanistically, we show that both inoculation with Lactobacillus or lactate treatment significantly increased the levels of the neurotransmitter, gamma-aminobutyric acid (GABA), in the hippocampus of the mice.

CONCLUSION

Together, this study provides new evidence for a link between Lactobacillus and memory and our results open possible new avenues for treating memory impairment disorders using specific gut microbial inoculants and/or metabolites. Video Abstract.

High-pressure, high-temperature molecular doping of nanodiamond

The development of color centers in diamond as the basis for emerging quantum technologies has been limited by the need for ion implantation to create the appropriate defects. We present a versatile method to dope diamond without ion implantation by synthesis of a doped amorphous carbon precursor and transformation at high temperatures and high pressures. To explore this bottom-up method for color center generation, we rationally create silicon vacancy defects in nanodiamond and investigate them for optical pressure metrology. In addition, we show that this process can generate noble gas defects within diamond from the typically inactive argon pressure medium, which may explain the hysteresis effects observed in other high-pressure experiments and the presence of noble gases in some meteoritic nanodiamonds. Our results illustrate a general method to produce color centers in diamond and may enable the controlled generation of designer defects.

Early exposure to UV radiation overshadowed by precipitation and litter quality as drivers of decomposition in the northern Chihuahuan Desert

Dryland ecosystems cover nearly 45% of the Earth’s land area and account for large proportions of terrestrial net primary production and carbon pools. However, predicting rates of plant litter decomposition in these vast ecosystems has proven challenging due to their distinctly dry and often hot climate regimes, and potentially unique physical drivers of decomposition. In this study, we elucidated the role of photopriming, i.e. exposure of standing dead leaf litter to solar radiation prior to litter drop that would chemically change litter and enhance biotic decay of fallen litter. We exposed litter substrates to three different UV radiation treatments simulating three-months of UV radiation exposure in southern New Mexico: no light, UVA+UVB+Visible, and UVA+Visible. There were three litter types: mesquite leaflets (Prosopis glandulosa, litter with high nitrogen (N) concentration), filter paper (pure cellulose), and basswood (Tilia spp, high lignin concentration). We deployed the photoprimed litter in the field within a large scale precipitation manipulation experiment: ∼50% precipitation reduction, ∼150% precipitation addition, and ambient control. Our results revealed the importance of litter substrate, particularly N content, for overall decomposition in drylands, as neither filter paper nor basswood exhibited measurable mass loss over the course of the year-long study, while high N-containing mesquite litter exhibited potential mass loss. We saw no effect of photopriming on subsequent microbial decay. We did observe a precipitation effect on mesquite where the rate of decay was more rapid in ambient and precipitation addition treatments than in the drought treatment. Overall, we found that precipitation and N played a critical role in litter mass loss. In contrast, photopriming had no detected effects on mass loss over the course of our year-long study. These results underpin the importance of biotic-driven decomposition, even in the presence of photopriming, for understanding litter decomposition and biogeochemical cycles in drylands.

Borehole muography of subsurface reservoirs

Imaging subsurface rock formations or geological objects like oil and gas reservoirs, mineral deposits, cavities or even magmatic plumbing systems under active volcanoes has been for many years a major quest of geoscientists. Since these subsurface objects cannot be observed directly, different indirect methods have been developed. These methods are all based on variations of certain physical properties of the subsurface materials that can be detected from the ground surface or from boreholes. To determine the density distribution, a new imaging technique using cosmic-ray muon detectors deployed in a borehole has been developed and a first prototype of a borehole muon detector successfully tested. In addition to providing a static image of the subsurface density in three dimensions (or three-dimensional tomography), borehole muography can also inform on the variations of density with time, which recently became of major importance with the injection of large volumes of fluids, mainly water and CO₂, in porous subsurface reservoirs (e.g. aquifer storage and recovery, wastewater disposal, enhanced oil recovery and carbon sequestration). This raises several concerns about the risk of leakage and the mechanical integrity of the reservoirs. Determining the field scale induced displacement of fluids by geophysical methods like muography is thus a priority.This article is part of the Theo Murphy meeting issue 'Cosmic-ray muography'.

Dynamic graphs, community detection, and Riemannian geometry

A community is a subset of a wider network where the members of that subset are more strongly connected to each other than they are to the rest of the network. In this paper, we consider the problem of identifying and tracking communities in graphs that change over time - dynamic community detection - and present a framework based on Riemannian geometry to aid in this task. Our framework currently supports several important operations such as interpolating between and averaging over graph snapshots. We compare these Riemannian methods with entry-wise linear interpolation and find that the Riemannian methods are generally better suited to dynamic community detection. Next steps with the Riemannian framework include producing a Riemannian least-squares regression method for working with noisy data and developing support methods, such as spectral sparsification, to improve the scalability of our current methods.

Supersaturated calcium carbonate solutions are classical

Mechanisms of CaCO₃ nucleation from solutions that depend on multistage pathways and the existence of species far more complex than simple ions or ion pairs have recently been proposed. Herein, we provide a tightly coupled theoretical and experimental study on the pathways that precede the initial stages of CaCO₃ nucleation. Starting from molecular simulations, we succeed in correctly predicting bulk thermodynamic quantities and experimental data, including equilibrium constants, titration curves, and detailed x-ray absorption spectra taken from the supersaturated CaCO₃ solutions. The picture that emerges is in complete agreement with classical views of cluster populations in which ions and ion pairs dominate, with the concomitant free energy landscapes following classical nucleation theory.

Reassortment Networks and the evolution of pandemic H1N1 swine-origin influenza

Prior research developed Reassortment Networks to reconstruct the evolution of segmented viruses under both reassortment and mutation. We report their application to the swine-origin pandemic H1N1 virus (S-OIV). A database of all influenza A viruses, for which complete genome sequences were available in Genbank by October 2009, was created and dynamic programming was used to compute distances between all corresponding segments. A reassortment network was created to obtain the minimum cost evolutionary paths from all viruses to the exemplar S-OIV A/California/04/2009. This analysis took 35 hours on the Cray Extreme Multithreading (XMT) supercomputer, which has special hardware to permit efficient parallelization. Six specific H1N1/H1N2 bottleneck viruses were identified that almost always lie on minimum cost paths to S-OIV. We conjecture that these viruses are crucial to S-OIV evolution and worthy of careful study from a molecular biology viewpoint. In phylogenetics, ancestors are typically medians that have no functional constraints. In our method, ancestors are not inferred, but rather chosen from previously observed viruses along a path of mutation and reassortment leading to the target virus. This specificity and functional constraint render our results actionable for further experiments in vitro and in vivo.