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Flow and motility type determine bacterial transport in porous media. Peritrichous bacteria outperform piliated and monotrichous types in maintaining motility (cross-streamline movement) under high-flow conditions.
Ion channels are multicomponent complexes (termed here as“electrosomes”) that conduct the bioelectrical signals required for life. It has been appreciated for decades that assembly is critical for proper channel function, but knowledge of the factors that undergird this important process has been lacking. Although there are now exemplar structures of representatives of most major ion channel classes, there has been no direct structural information to inform how these complicated, multipart complexes are put together or whether they interact with chaperone proteins that aid in their assembly. Recent structural characterization of a complex of the endoplasmic membrane protein complex (EMC) chaperone and a voltage-gated calcium channel (CaV) assembly intermediate comprising the pore-forming CaVα1 and cytoplasmic CaVβ subunits offers the first structural view into the assembly of a member of the largest ion channel class, the voltagegated ion channel (VGIC) superfamily. The structure shows how the EMC remodels the CaVα1/CaVβ complex through a set of rigid body movements for handoff to the extracellular CaVα2δ subunit to complete channel assembly in a process that involves intersubunit coordination of a divalent cation and ordering of CaVα1 elements. These findings set a new framework for deciphering the structural underpinnings of ion channel biogenesis that has implications for understanding channel function, how drugs and disease mutations act, and for investigating how other membrane proteins may engage the ubiquitous EMC chaperone.
Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data, with volumes soon expected to reach exabytes. Consequently, there is a growing need for computation, including structured data processing from raw data to consumer-ready derived data, extensive Monte Carlo simulation campaigns, and a wide range of end-user analysis. To manage these computational and storage demands, centralized workflow and data management systems are implemented. However, decisions regarding data placement and payload allocation are often made disjointly and via heuristic means. A significant obstacle in adopting more effective heuristic or AI-driven solutions is the absence of a quick and reliable introspective dynamic model to evaluate and refine alternative approaches. In this study, we aim to develop such an interactive system using real-world data. By examining job execution records from the PanDA workflow management system, we have pinpointed key performance indicators such as queuing time, error rate, and the extent of remote data access. The dataset includes five months of activity. Additionally, we are creating a generative AI model to simulate time series of payloads, which incorporate visible features like category, event count, and submitting group, as well as hidden features like the total computational load—derived from existing PanDA records and computing site capabilities. These hidden features, which are not visible to job allocators, whether heuristic or AI-driven, influence factors such as queuing times and data movement.
Network macroscopic fundamental diagrams (MFDs) have recently been shown to exist in real-world urban traffic networks. When present, MFDs can be used to model traffic dynamics within an urban network by dividing the network into a set of spatially compact homogeneous regions and tracking the average level of congestion in each region. Existing analytical methods to estimate MFD mostly focus on the behavior of a single type of vehicle and do not capture the patterns of mixed traffic (e.g., cars and buses). The existence of buses matters since a bus will block the movements of other vehicles when it dwells at the bus stop. This paper proposes an analytical method to estimate the impact of bus dwelling on a network’s MFD based on the network’s geometric features, traffic control strategies, and bus operation parameters, and validates the performance of the proposed method using simulations based on microscopic traffic models. Comparisons of the analytical and simulation results show that the proposed analytical method can generally provide a good estimate of the lower bound and upper bound of the network’s MFD.
Here, we present the details of a novel ultra-short pulsed laser machining workstation that has been employed for high-throughput laser machining of small-scale mechanical property specimens. This system employs a six degrees of freedom hexapod positioning stage capable of macroscopic movements at high positional accuracy. We developed a methodology that uses quantitative image analysis to measure key parameters required to minimize the hexapod positioning and rotational error. Application of this system to laser machining of small-scale 316L stainless steel tensile specimens and ultra-high molecular weight polyethylene compressive specimens using eucentric tilt and rotation about the specimen axis will be shown, where serial laser milling at a specimen tilt angle of 10° was used to effectively eliminate any taper in the sample cross section that is typically found in laser machining.
Here, we present a Lagrangian-based implementation of Ehrenfest dynamics with nuclear–electronic orbital (NEO) theory and real-time time-dependent density functional theory for extended periodic systems. In addition to a quantum dynamical treatment of electrons and selected protons, this approach allows for the classical movement of all other nuclei to be taken into account in simulations of condensed matter systems. Furthermore, we introduce a Lagrangian formulation for the traveling proton basis approach and propose new schemes to enhance its application for extended periodic systems. Validation and proof-of-principle applications are performed on electronically excited proton transfer in the o-hydroxybenzaldehyde molecule with explicit solvating water molecules. These simulations demonstrate the importance of solvation dynamics and a quantum treatment of transferring protons. This work broadens the applicability of the NEO Ehrenfest dynamics approach for studying complex heterogeneous systems in the condensed phase.
Advances in wurtzite nitride ferroelectrics of Al 1-x M x N ( M = Sc or B) have led to novel capabilities, which must be integrated into existing fabrication processes. In the case of electronics operating > 200°C, a movement toward SiC-based platforms enables better performance over conventional Si, primarily due to the higher bandgap and lower intrinsic carrier concentration of SiC. Hence, the challenge is to develop a deposition process to integrate Al 1-x M x M x N ferroelectrics with elevated temperature-compatible materials for high temperature electronics such as the non-volatile memory component. We demonstrate epitaxial Al M N/Mo/SiC heterostructures, which provides both the crystalline and surface features that promote high-quality ferroelectric nitride film growth. Omega scans of the Mo (110) reflection exhibit a full width at half max of < 0.02° (40 arc sec) and the (0002) peak of the subsequently grown nitride film had a value of 1.1° for 160 nm thick Al 0.7 Sc 0.3 N and 1.3° for 400 nm thick Al 0.94 B 0.06 N. The crystallographic relationships found between the Al M N, Mo, and SiC layers indicate an advancement in sputter deposition of epitaxial films. Ferroelectric switching is also shown at 400 °C in both samples via polarization-electric field hysteresis and pulsed measurements, which exhibited P r values >100 μC cm -2 and E c between 3 and 4 M V cm -1 , despite a large presence of oxygen in both Al M N films ranging between 4 and 5 at.%, revealed by compositional analysis. This study demonstrates the process for synthesizing high-crystal quality ferroelectric nitride films, which can be used in extremely high temperature applications.
Quantum well (QW) structures are widely used in lasers, semiconductor optical amplifiers, and modulators, enabling their monolithic integration on the same substrate. As optoelectronic systems evolve to meet the growing bandwidth demands in the terahertz regime, a deep understanding of ultrafast carrier dynamics in QW structures becomes essential. We introduce a comprehensive model to analyze the ultrafast dynamics of interband photo-excited carriers in QW p–i–n structures and to calculate their frequency response. This model characterizes the entire photocarrier transport process, including carrier escape from QWs and movement across heterojunction interfaces. Additionally, we outline theoretical methods for calculating carrier escape times from both QWs and heterojunction interfaces. Using a GaAs/AlGaAs QW p–i–n structure as a case study, we discuss the effects of carrier escape times from QWs and heterojunction interfaces, as well as carrier transit time through the intrinsic region, on the frequency response of QW p–i–n structures.
The ecoevolutionary drivers of species niche expansion or contraction are critical for biodiversity but challenging to infer. Niche expansion may be promoted by local adaptation or constrained by physiological performance trade-offs. For birds, evolutionary shifts in migratory behavior permit the broadening of the climatic niche by expansion into varied, seasonal environments. Broader niches can be short-lived if diversifying selection and geography promote speciation and niche subdivision across climatic gradients. To illuminate niche breadth dynamics, we can ask how “outlier” species defy constraints. Of the 363 hummingbird species, the giant hummingbird (Patagona gigas) has the broadest climatic niche by a large margin. To test the roles of migratory behavior, performance trade-offs, and genetic structure in maintaining its exceptional niche breadth, we studied its movements, respiratory traits, and population genomics. Satellite and light-level geolocator tracks revealed an >8,300-km loop migration over the Central Andean Plateau. This migration included a 3-wk, ~4,100-m ascent punctuated by upward bursts and pauses, resembling the acclimatization routines of human mountain climbers, and accompanied by surging blood-hemoglobin concentrations. Extreme migration was accompanied by deep genomic divergence from high-elevation resident populations, with decisive postzygotic barriers to gene flow. The two forms occur side-by-side but differ almost imperceptibly in size, plumage, and respiratory traits. The high-elevation resident taxon is the world’s largest hummingbird, a previously undiscovered species that we describe and name here. The giant hummingbirds demonstrate evolutionary limits on niche breadth: when the ancestral niche expanded due to evolution (or loss) of an extreme migratory behavior, speciation followed.
Hemostasis relies on a reaction network of serine proteases and their cofactors to form a blood clot. Coagulation factor IXa (protease) plays an essential role in hemostasis as evident from the bleeding disease associated with its absence. RNA aptamers specifically targeting individual coagulation factors have potential as anticoagulants and as probes of the relationship between structure and function. Here, we report X-ray structures of human factor IXa without a ligand bound to the active site either in the apo-form or in complex with an inhibitory aptamer specific for factor IXa. The aptamer binds to an exosite in the catalytic domain and allosterically distorts the active site. Our studies reveal a conformational ensemble of IXa states, wherein large movements of Trp 215 near the active site drive functional transitions between the closed (aptamer-bound), latent (apo), and open (substrate-bound) states. The latent state of the apo-enzyme may bear on the uniquely poor catalytic activity of IXa compared to other coagulation proteases. The exosite, to which the aptamer binds, has been implicated in binding VIIIa and heparin, both of which regulate IXa function. Our findings reveal the importance of exosite-driven allosteric modulation of IXa function and new strategies to rebalance hemostasis for therapeutic gain.
Dynamic changes in intracellular ultrastructure can be critical for the ability of organisms to acclimate to environmental conditions. Microalgae, which are responsible for ~50% of global photosynthesis, compartmentalize their Ribulose 1,5 Bisphosphate Carboxylase/Oxygenase (Rubisco) into a specialized structure known as the pyrenoid when the cells experience limiting CO 2 conditions; this compartmentalization is a component of the CO 2 Concentrating Mechanism (CCM), which facilitates photosynthetic CO 2 fixation as environmental levels of inorganic carbon (Ci) decline. Changes in the spatial distribution of mitochondria in green algae have also been observed under CO 2 limitation, although a role for this reorganization in CCM function remains unclear. We used the green microalga Chlamydomonas reinhardtii to monitor changes in mitochondrial position and ultrastructure as cells transition between high CO 2 and Low/Very Low CO 2 (LC/VLC). Upon transferring cells to VLC, the mitochondria move from a central to a peripheral cell location and orient in parallel tubular arrays that extend along the cell’s apico-basal axis. We show that these ultrastructural changes correlate with CCM induction and are regulated by the CCM master regulator CIA5. The apico-basal orientation of the mitochondrial membranes, but not the movement of the mitochondrion to the cell periphery, is dependent on microtubules and the MIRO1 protein, with the latter involved in membrane–microtubule interactions. Furthermore, blocking mitochondrial respiration in VLC-acclimated cells reduces the affinity of the cells for Ci. Overall, our results suggest that mitochondrial repositioning functions in integrating cellular architecture and energetics with CCM activities and invite further exploration of how intracellular architecture can impact fitness under dynamic environmental conditions.
Molecular Dynamics (MD) simulations using classical force-fields are commonly employed in numerous scientific investigations. However, many natural processes involve bond breaking and quantum forces. This complexity is compounded by the presence of multiple competing length and timescales. For example, accurately modeling the thermodynamics and dynamics of a chemical reaction requires accounting for the concerted movements of numerous solvent molecules and ions with their own fast or slow timescales. While widely used static Density Functional Theory (DFT) calculations at 0 temperature can be beneficial for such investigations, they do not account for dynamics, and lack precision in describing the molecular environments. They particularly fail at correct, rigorous treatments of finite-temperature fluctuations, and thus generalization to experimentally relevant conditions. In PNAS Benayad et al develop a scalable, generalizable approach for designing Neural Network Potentials (NNPs) that can handle chemical reactivity in solvated systems with quantum accuracy at classical costs. Specifically, they study phosphoester bond formation and rupture, which is fundamentally relevant to the Phosphorus-Oxygen bond formation central to life, and especially for the RNA world hypothesis. The framework developed here has the potential to generalize to different chemical reactions of energy and biological relevance.
The U.S. Department of Energy (DOE) is leading the National effort to manage the back end of the nuclear fuel cycle, encompassing the safe transportation, storage/staging, and/or eventual disposal of used nuclear fuel (UNF) and high-level radioactive waste. The Next Generation System Analysis Model (NGSAM) is DOE’s discrete-event, agent-based simulation tool designed to model the full life cycle of UNF from reactor discharge to final disposal. NGSAM supports the DOE Office of Spent Fuel and High-Level Waste Disposition by enabling a detailed, scenario-based analysis of logistics, infrastructure, and shipping strategies. NGSAM replaces legacy models with a modern, flexible platform built on Repast Simphony and enhanced by the Process Analysis Tool. NGSAM simulates the movement and interaction of individual fuel assemblies with system components such as canisters, casks, railcars, and facilities. The model integrates with the Java Transportation Operations Model to plan and execute transportation scenarios, supporting both constrained and unconstrained resource allocation. Key features include customizable allocation and acceptance algorithms, detailed facility-level operations, and a Quick Edit tool for rapid scenario adjustments. NGSAM supports multimodal transportation modeling (e.g. rail, road, barge) and provides comprehensive cost, schedule, and infrastructure data. NGSAM utilizes data from sources such as DOE’s STANDARDS UNF database and DOE’s Stakeholder Tool for Assessing Radioactive Transportation, while also allowing user-defined inputs for scenario customization. NGSAM enables stakeholders to evaluate complex UNF management strategies, assess system performance under varying assumptions, and inform decision making for future infrastructure investments. Its modular architecture and integration with other Integrated Waste Management System tools make it a critical asset for planning the safe and efficient disposition of the Nation’s growing UNF inventory.
In order to ease the computational burden associated with designing irradiation experiments in the Advanced Test Reactor (ATR), scaling factors are often used to estimate design parameters at different lobe powers. Here, this paper examines the validity of long-standing assumptions about the contribution of lobe power to total experiment heating in the ATR. For each of the ATR’s 77 different experiment positions, the fractional contribution of each of the ATR’s five lobes to the total heating in that position is calculated and compared to traditional assumptions. The updated fractional contributions are then used to scale heating rates in a sample problem, and the results are compared to traditional scaling methods as well as explicit MC21 heating calculations. It is concluded that for experiment locations in close proximity to the ATR driver fuel (i.e. flux traps and the A, H, and B positions), heating rates scaled with the updated fractional contributions generally agree better with explicit MC21 calculations than do heating rates scaled using the traditionally assumed contributions. For the I positions, which are located on the very periphery of the ATR core, both scaling methods led to poor results when compared against explicit calculations due to the effect that movement of the outer shim control cylinders has on the experiment heating in those positions.
Estimating the equilibrium state for pebble bed reactors (PBRs) presents complex challenges as it requires simultaneous consideration of changes in the pebbles’ movement as well as their fuel compositions. Whereas traditional approaches use multigroup diffusion codes for neutronics calculations of PBRs’ equilibrium state, the double-heterogeneity of PBRs complicates neutron cross-section generation. Continuous-energy Monte Carlo (MC) methods are better suited for detailed PBR analysis because of their natural handling of double-heterogeneity, but they demand substantially more computational resources. Here, this study introduces a novel method for efficiently estimating the equilibrium state in small and micro PBRs with reduced computational cost. The method is anticipated to accelerate the processes of core design and performing parametric studies for utilizing advanced fuel and structural materials. The HTR-10 reactor design was used for validating the method’s predictions and evaluating its computational efficiency. When compared to reference calculation values from the literature, criticality (k-effective) was predicted to be approximately within the margin of error of the MC transport calculation, average core power density (in megawatts per cubic meter) was predicted within 2.5% relative error, and maximum thermal flux (10 13 n/cm 2 .s −1 ) was predicted within 1.8% relative error. The calculated inventory of fission products and fuel composition in the equilibrium core were within 15% and 16.6%, respectively, when compared to reported values from the literature. The difference is attributed to variance in the considered values of the core temperature, which was found to significantly affect the depletion analyses.
This review presents two examples of domain dynamics in crystalline solid state samples which were imaged recently with synchrotron X-rays by Bragg Coherent Diffraction methods. Domains are associated with sub-atomic phase shifts in the structure which are revealed by the strong phase contrast associated with Bragg diffraction. At low temperatures, La2-xSrxNiO4 has an antiferromagnetic spin-ordered phase with correlation lengths of 200 nm; its domains are imaged with magnetic Bragg Ptychography to visualize their slow fluctuations. Hydrothermally synthesized nanocrystalline BaTiO3 is found to be made up of ~50 nm domains using Bragg Coherent Diffraction Imaging; electric field induced movement of its internal domain walls is a likely explanation of the nano-material's anomalously large dielectric constant, which is used in the manufacture of multilayer ceramic capacitors
Eco-approach and departure (EAD) enable continuous vehicle motion in urban signalized corridors. Since such a motion can extend to the EAD vehicles’ followers, it makes EAD a promising technology to benefit the traffic flow where automated vehicles and conventional vehicles coexist. Most existing EAD studies envision an ideal setting that neglects real-world operational conditions such as lane changes, multi-movement intersection configuration, partially automated fleet, and/or limited traffic state awareness. This study aims to fill the gap by designing an EAD algorithm considering real-world traffic operation constraints. The proposed algorithm uses a model predictive controller to minimize vehicle speed reduction and variation based on the real-time traffic signal control plan and measured queues at the intersection. The required inputs are readily available at many modern intersections. We observed that the proposed controller’s performance might degrade because of lane-changing maneuvers and lead-left turn traffic signals. These observations motivated our development of a lane change management strategy and a signal control implementation strategy to facilitate the EAD implementation. The lane change management strategies separate the EAD operations and lane-changing maneuvers in time and space. The signal control implementation strategy applies lag-left turn signals to enable EAD operation for both the through and left-turn vehicles. Compared to the non-EAD case, our EAD approach produces 2.5% to 7.8% energy savings while keeping similar intersection mobility. Notably, this approach brings about 2.5% to 3.6% energy savings in a 2% CAV case. This result demonstrates the feasibility of deploying EAD at low connected automated vehicle penetration rates.
Commercial trucks are essential elements of the nation's supply chain system. Meanwhile, intensive truck movements contribute significantly to system externalities, such as energy use and air pollution. However, collecting detailed fleet composition and distribution of operational patterns remains a barrier to accurately accounting for these impacts. The recently released 2021 US Vehicle Inventory and Use Survey (US VIUS) fills a critical gap in understanding commercial truck fleet distributions, their operations, and business constraints at the national scale. This study aims to understand the latest US commercial vehicle fleet composition and operational characteristics using 2021 US VIUS data and calibrate the fleet inputs in regulatory emission models to assess the potential emission implications of the VIUS-derived fleet composition. The emission rates for commercial trucks and default fleet composition are collected from the U.S. EPA's MOtor Vehicle Emission Simulator (MOVES4). The 2021 US VIUS data is applied to improve fleet characteristics such as the long-haul fraction and the vehicle mileage accumulation rate. The study also investigates potential emission reduction benefits under various forecasted fleet electrification scenarios. The energy consumption and critical air pollutant rates by vehicle types are compared between MOVES4 and US VIUS fleets for both current and future scenarios to provide insights into the latest U.S. commercial vehicle fleet characteristics and their implications on energy and emissions. This study helps policymakers and practitioners advance the commercial fleet generation for emission models. It also deepens the understanding of the emission reduction potential of the commercial fleet under various fleet projections.