Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Alternative Identifiers”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25

Where Have All of the Electrons Gone? Understanding the Role of Alternative Electron Flow and O 2 Reduction in Balancing Cellular Redox (Final Technical Report)

An informed understanding of the fundamentals of light-driven electron transfer reactions in photoautotrophic organisms is necessary to enable improved photosynthetic efficiencies for the production of renewable fuels and novel biomaterials. To obtain high photosynthetic yields, light energy must be efficiently coupled to the fixation of CO 2 . Sub-optimal environmental conditions and metabolic routing (caused by blocks in biosynthetic routes) can severely impact the conversion of light energy to biomass and lead to reactive oxygen production, which in turn can cause cellular damage and productivity losses. Hence, plants, algae, and photosynthetic bacteria have evolved a network of alternative outlets to sustain the flow of photosynthetically derived-electrons. Our research was focused on the nature and integration of these outlets. The data obtained in this project will inform efforts to rationally engineer crop plants and algae to improve photosynthetic yields by enhancing electron transfer reactions to CO 2 reduction and targeted biomass accumulation. A major project research thrust was focused on identifying and quantifying the flow of photosynthetic reductant through electron transfer circuits that result in the light-dependent reduction of O 2 . New energy management strategies were identified that are used when normal carbon assimilation pathways are compromised due to nutrient deprivation, and/or by a reduction in starch synthesis/carbon storage, all conditions resulting in highly reduced intracellular redox pools. The green alga Chlamydomonas reinhardtii, and likely many other algae, have multiple O 2 -reducing pathways that play critical roles in maintaining cellular metabolic balance and scavenging electrons that could potentially cause cellular damage, which in some instances leads to reduced photosynthetic yields but increased fitness. We explored the activities of three essential outlets associated with Chlamydomonas reinhardtii photosynthetic electron transport: (1) reduction of O 2 to H 2 O through Flavodiiron proteins (FLVs) and (2) Plastid Terminal Oxidases (PTOX), and (3) the synthesis of starch. Real-time measurements of O 2 exchange demonstrated that FLVs immediately engage during dark to light transitions, allowing electron transport when the CBBC is not fully activated. Under these conditions, we quantified, for the first time, maximal FLV activity and its overall capacity to direct photosynthetic electrons towards O 2 reduction. However, when starch synthesis is compromised, a greater proportion of electrons is directed toward O 2 reduction through the FLVs, while PTOX, which is sensitive to the PQ pool redox state, is activated. This suggests, that starch synthesis has an important role in priming/regulating CBBC and electron transport. We also identified a biological ‘switch’ in the green alga Chlamydomonas reinhardtii that reversibly restricts photosynthetic electron transport (PET) at the cytochrome b 6 f complex when reductant and ATP generated by PET are in excess of the capacity of carbon metabolism to utilize these products; we specifically show a restriction at this switch when sta6 mutant cells, which cannot synthesize starch, are limited for nitrogen (growth inhibition) and subjected to a dark to light transition. This restriction causes diminished electron flow to PSI, which prevents PSI photodamage, and the plastid alternative oxidase (PTOX) becomes fully activated, serving as an electron valve that dissipates excitation energy absorbed by PSII, thereby lessening PSII photoinhibition. Furthermore, illumination of the cells following the dark acclimation gradually diminishes the restriction at the switch. Future engineering of these switches may allow more effective electron transfer to lipid (biofuel) pathways and diminish the number of electrons “wasted” in the reduction of O 2 to water. Lastly, we explored metabolic routing of electrons during algal fermentation and discovered multiple novel pathways that are activated when the preferred anoxic routes are blocked. These provide valuable products (e.g. lactate, glycerol) that can be used in broad portfolio of biotechnological applications.

59 BASIC BIOLOGICAL SCIENCES↗

An experimental program with high duty-cycle polarized and unpolarized positron beams at Jefferson Lab

Positron beams, both polarized and unpolarized, are identified as important ingredients for the experimental programs at the next generation of lepton accelerators. In the context of the hadronic physics program at Jefferson Lab (JLab), positron beams are complementary, even essential, tools for a precise understanding of the electromagnetic structure of nucleons and nuclei, in both the elastic and deep-inelastic regimes. For instance, elastic scattering of polarized and unpolarized electrons and positrons from the nucleon enables a model independent determination of its electromagnetic form factors. Also, the deeply-virtual scattering of polarized and unpolarized electrons and positrons allows unambiguous separation of the different contributions to the cross section of the lepto-production of photons and of lepton-pairs, enabling an accurate determination of the nucleons and nuclei generalized parton distributions, and providing an access to the gravitational form factors of the energy-momentum tensor. Furthermore, positron beams offer the possibility of alternative tests of the Standard Model of particle physics through the search of a dark photon, the precise measurement of electroweak couplings, and the investigation of charged lepton flavor violation. This document discusses the perspectives of an experimental program with high duty-cycle positron beams at JLab.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Sustainable bioleaching of lithium-ion batteries for critical metal recovery: Process optimization through design of experiments and thermodynamic modeling

Recycling spent lithium-ion batteries (LIBs) could alleviate supply risks for critical metals and be less harmful to the environment compared to new production of metals from mining. Developing a cost-effective LIB bioleaching process could be a promising alternative to traditional energy-intensive recycling technologies. Here, this study aimed to optimize bioleaching conditions for maximum economic competitiveness through design of experiments using iterative response surface methodology (RSM), assisted by thermodynamic modeling. The optimal condition was identified as 2.5% pulp density in 75 mM gluconic acid biolixiviant at 55°C for 30 h which could recover 57%–84% of nickel, 71%–86% of cobalt, and 100% of lithium and manganese, yielding a 17%–26% net profit margin. The recommended pulp density and acid concentrations, together with the observed metal solubilization, were supported by thermodynamic modeling predictions. Our study demonstrated that combining RSM with thermodynamic simulations could be a powerful tool for optimizing bioleaching conditions.

60 APPLIED LIFE SCIENCES↗

Systematic Comparison of High-throughput Single-Cell and Single-Nucleus Transcriptomes during Cardiomyocyte Differentiation

A comprehensive reference map of all cell types in the human body is necessary for improving our understanding of fundamental biological processes and in diagnosing and treating disease. High-throughput single-cell RNA sequencing techniques have emerged as powerful tools to identify and characterize cell types in complex and heterogeneous tissues. However, extracting intact cells from tissues and organs is often technically challenging or impossible, for example in heart or brain tissue. Single-nucleus RNA sequencing provides an alternative way to obtain transcriptome profiles of such tissues. To systematically assess the differences between high-throughput single-cell and single-nuclei RNA-seq approaches, we compared Drop-seq and DroNc-seq, two microfluidic-based 3' RNA capture technologies that profile total cellular and nuclear RNA, respectively, during a time course experiment of human induced pluripotent stem cells (iPSCs) differentiating into cardiomyocytes. Clustering of timeseries transcriptomes from Drop-seq and DroNc-seq revealed six distinct cell types, five of which were found in both techniques. Furthermore, single-cell trajectories reconstructed from both techniques reproduced expected differentiation dynamics. We then applied DroNc-seq to postmortem heart tissue to test its performance on heterogeneous human tissue samples. Our data confirm that DroNcseq yields similar results to Drop-seq on matched samples and can be successfully used to generate reference maps for the human cell atlas.

59 BASIC BIOLOGICAL SCIENCES↗

Drug-induced kidney injury: challenges and opportunities

Abstract Drug-induced kidney injury (DIKI) is a frequently reported adverse event, associated with acute kidney injury, chronic kidney disease, and end-stage renal failure. Prospective cohort studies on acute injuries suggest a frequency of around 14%–26% in adult populations and a significant concern in pediatrics with a frequency of 16% being attributed to a drug. In drug discovery and development, renal injury accounts for 8 and 9% of preclinical and clinical failures, respectively, impacting multiple therapeutic areas. Currently, the standard biomarkers for identifying DIKI are serum creatinine and blood urea nitrogen. However, both markers lack the sensitivity and specificity to detect nephrotoxicity prior to a significant loss of renal function. Consequently, there is a pressing need for the development of alternative methods to reliably predict drug-induced kidney injury (DIKI) in early drug discovery. In this article, we discuss various aspects of DIKI and how it is assessed in preclinical models and in the clinical setting, including the challenges posed by translating animal data to humans. We then examine the urinary biomarkers accepted by both the US Food and Drug Administration (FDA) and the European Medicines Agency for monitoring DIKI in preclinical studies and on a case-by-case basis in clinical trials. We also review new approach methodologies (NAMs) and how they may assist in developing novel biomarkers for DIKI that can be used earlier in drug discovery and development.

Connor, Skylar (ORCID:0000000233479180)↗

A data-driven mobility–energy typology framework for New York State

Exploring multidimensional aspects of differences in technology adoption, travel, and vehicle ownership across settlement types can help inform energy-efficient and affordable mobility system goals. Concurrently, mapping key enablers, barriers, and risks for successfully meeting ambitious goals and targets (e.g. by geography, age, income, education, population density) offer important explanatory power as to context-specific challenges and opportunities. This paper explores how a highly geographically resolved understanding of social, economic, techno-infrastructural, environmental, and governance (SET EG) factors shape variations in technology adoption and associated mobility and energy outcomes in diverse communities of New York State, in terms of electric vehicle adoption rates, alternative commute mode choices, vehicles per household, and vehicle fuel economy. Results indicate the range of two to three times higher adoption rates for electric vehicles by more highly educated, wealthier, “Core Urban” populations relative to the other identified typologies, given the labels “Rural”, “Suburban”, and “Urban” populations. Additionally, commute mode choice is closely linked with population and employment density—more than 89% of Core Urbanites use transit or active modes, compared with just 26% and 18% of Suburban and Rural residents, respectively. Household vehicle ownership varies from approximately 1.9 vehicles per household in Rural areas to only 0.6 in Core Urban settings. Findings on differences among the four settlement types, which go beyond simply rural to urban contexts, suggest an important need to explore how best to manage and anticipate very different types of services that may be supportive in achieving energy-efficient and affordable mobility systems statewide.

54 ENVIRONMENTAL SCIENCES↗

Interpreting and Accelerating Transformers for Jet Tagging

Attention-based transformers are ubiquitous in machine learning applications from natural language processing to computer vision. In high energy physics, one central application is to classify collimated particle showers in colliders based on the particle of origin, known as jet tagging. In this work, we study the interpretatbility and prospects for acceleration of Particle Transformer (ParT), a state-of-the-art model, leverages particle-level attention to improve jet-tagging performance. We analyzing ParT's attention maps and particle-pair correlations in the eta-phi plane, revealing intriguing features, such as a binary attention pattern that identifies critical substructure in jets. These insights enhance our understanding of the model's internal workings and learning process and hint at ways to improve its efficiency. Along these lines, we also explore low-rank attention, attention alternatives, and dynamic quantization to accelerate transformers for jet tagging. With quantization, we achieve a 50% reduction in model size and a 10% increase in inference speed without compromising accuracy. These combined efforts enhance both the performance and the interpretability of transformers in high-energy physics, opening avenues for more efficient and physics-driven model designs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Leaching of Rare Earth Elements from Central Appalachian Coal Seam Underclays

Rare earth elements (REE) are necessary for advanced technological and energy applications. To support the emerging need, it is necessary to identify new domestic sources of REE and technologies to separate and recover saleable REE product in a safe and economical manner. Underclay rock associated with Central Appalachian coal seams and prevalent in coal utilization waste products is an alternative source of REE to hard rock ores that are mainly composed of highly refractory REE-bearing minerals. This study utilizes a suite of analytical techniques and benchtop leaching tests to characterize the properties and leachability of the coal seam underclays sampled. Laboratory bench-top and flow-through reactor leaching experiments were conducted on underclay rock powders to produce a pregnant leach solution (PLS) that has relatively low concentrations of gangue elements Al, Si, Fe, and Th and is amenable to further processing steps to recover and produce purified REE product. The leaching method described here uses a chelating agent, the citrate anion, to solubilize elements that are adsorbed, or weakly bonded to the surface of clay minerals or other mineral solid phases in the rock. The citrate PLS produced from leaching specific underclay powders contains relatively higher concentrations of REE and lower concentrations of gangue elements compared to PLS produced from sequential digestion using ammonium sulfate and mineral acids. Citrate solution leaching of underclay produces a PLS with lower concentrations of gangue elements and higher concentrations of REE than achieved with hydrochloric acid or sulfuric acid. The results provide a preliminary assessment of the types of REE-bearing minerals and potential leachability of coal seam underclays from the Central Appalachian basin.

58 GEOSCIENCES↗

Irradiation Facilities and Irradiation Methods for High Power Target

High Power Target systems are key elements in future neutrino and other rare particle production in accelerators. These systems transform an intense source of protons into secondary particles of interest to enable new scientific discoveries. As beam intensity and energies increase, target systems face significant challenges. Radiation damages and thermal shocks in target materials were identified as the leading cross-cutting challenges of high-power target facilities. Target material R&D to address these challenges are essential to enable and ensure reliable operation of future-generation accelerators. Irradiation facilities and alternative methods are critical to provide a full support of material R&D and better address these critical challenges.

43 PARTICLE ACCELERATORS↗

Will Nontoxic High‐Performance Perovskite Photovoltaics Ever Be Possible?

Lead-based halide perovskite solar cells represent a significant advancement in photovoltaic technology, achieving certified power conversion efficiencies of over 27%. However, the toxicity of lead poses a major barrier to widespread commercialization. The demand for environmentally safe alternatives has driven extensive research into Pb-free perovskites. Current efforts include replacing Pb with Sn or Ge; forming double perovskites in which Pb is substituted by a monovalent–trivalent cation pair; and developing chalcogenide perovskites where the B site (ABX 3 ) adopts tetravalent cations (rather than Pb) to balance the charge. This concept examines the recent progress in developing Pb-free alternatives, revealing fundamental performance bottlenecks, inherent material limitations, and persistent development challenges. Through a comparative assessment of material properties and device performance limitations, this work highlights the underlying dilemma between environmental safety and efficiency in perovskite photovoltaics. The analysis identifies fundamental material constraints that create substantial barriers to simultaneously achieving both objectives.

36 MATERIALS SCIENCE↗

Life Cycle Analysis of Renewable Natural Gas and Lactic Acid Production from Waste Feedstocks

Producing fuels and chemicals from waste is considered economically favorable, due to low feedstock cost, and environmentally favorable, due to avoided emissions from conventional waste management practices. In this study, we evaluate the life cycle greenhouse gas (GHG) emission reduction benefits of renewable natural gas (RNG) and lactic acid (LA) production from four types of wet waste feedstocks (wastewater sludge, food waste, swine manure, and fats, oil, and grease [FOG]) via anaerobic digestion (AD) and LA fermentation, respectively. RNG can be used as an alternative to fossil natural gas, while LA from waste feedstocks can displace conventional LA production pathways (mainly from corn via fermentation). Providing comprehensive life cycle GHG emissions of the combinations of waste feedstocks and products through different routes helps identify the GHG hotspots and show where emissions savings come from. The results show that the carbon intensities (CIs) of waste-derived RNG and LA are much lower than those of their counterparts. We estimated the life cycle GHG emissions for RNG to be between -146 and 27 g carbon dioxide equivalent (CO2e)/MJ, much lower than the CI of fossil fuels. Waste-derived LA pathways also show substantially lower CIs, ranging from -4.2 to -1.4 kgCO2e/kg LA, compared to the CIs of LA from corn and corn stover (1.2 and 0.3 kgCO2e/kg LA, respectively). We will also discuss that the low CIs of waste-derived products can come from low yields leading to high emission credits. Thus, life cycle analysis results presented per weight of treated waste can be used to support decisions about which waste feedstocks and products are to be used for sustainable waste valorization. In addition, we found that monetary emission reduction credits can play an important role in driving waste valorization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Coupling of Lipid Peroxidation and Criegee Intermediate Mediated Autoxidation in the Heterogeneous Oxidation of Linoleic Acid Aerosols

Autoxidation is an established mechanism for the degradation of organic molecules, which is relevant in the atmosphere, combustion processes, the environment, and the rancidification of lipids (commonly known as lipid peroxidation). Autoxidation proceeds via radical chain reactions involving hydroxyl (•OH), peroxy (RO2•), and alkoxy radicals, which are also prominent oxidants in the atmosphere. Recent reports have provided evidence for an alternative autoxidation mechanism driven instead by Criegee intermediates (CIs), which are produced from the reaction of β-hydroxy peroxy radicals (β-OH-RO2•). This work evaluates the contributions of these two mechanisms in the •OH initiated heterogeneous oxidation of linoleic acid (LA) aerosols. Reaction kinetics and product distributions are monitored using a vacuum ultraviolet photoionization aerosol time-of-flight mass spectrometer. To explain the observed kinetics, a kinetic model is developed that incorporates both the conventional peroxidation and alternative CI-mediated autoxidation mechanisms. We observe that the CI-mediated autoxidation pathways enhance the heterogeneous autoxidation rate, while the peroxidation reactions, although present, contributes less to the overall oxidation rate. α-Acyloxyalkyl hydroperoxides (AAHPs) are identified as key indicators for bimolecular reactions of CI with LA, highlighting the role of LA as a CI scavenger. Moreover, the measured functionalized LA products with hydroxyl or carbonyl group(s), serve as markers for the peroxidation reactions. In summary, this work presents a quantitative framework to understand the coupled reaction network of •OH, RO2•, β-OH-RO2• radicals, and CI in driving heterogeneous autoxidation, which is crucial for understanding degradation mechanisms of organic molecules in the environment and atmosphere.

Criegee intermediate↗

Direct Recycling of Lithium-Ion Battery Cathodes: A Multi-Stage Annealing Process to Recover the Pristine Structure and Performance

Direct recycling is an alternative low-cost process that retains the structure of the lithium-ion battery cathode rather than going through materials dissolution and repurification. While the process shows promise, various steps need to be better understood and studied to identify possible roadblocks, cost intensive steps, and needed variations in protocol that may be necessary based on the feedstock of recycled cathodes available. In this study, we have evaluated the process of thermal relithiation as it applies to end-of-life cathode materials. For recovered end-of-life cathodes, approximately 20% of the useable lithium has been lost due to solid electrolyte interface formation, parasitic reactions, and particle isolation; this lost lithium contributes to higher cell impedance and a gradual shift in electrode potentials. As a first step in the direct recycling process, this lithium needs to be restored not only to recover capacity but also to eliminate surface vacancies and hinder surface reconstruction that may increase impedance and impact performance. Here, we have optimized a coating method followed by a two-stage annealing process that restores a model delithiated NMC333 material to pristine performance. Comparisons to single-stage processes are made, and new characterization tools have been used to better understand the processes.

25 ENERGY STORAGE↗

Tunable magnons of an antiferromagnetic Mott insulator via interfacial metal-insulator transitions

Antiferromagnetic insulators present a promising alternative to ferromagnets due to their ultrafast spin dynamics essential for low-energy terahertz spintronic device applications. Magnons, i.e., quantized spin waves capable of transmitting information through excitations, serve as a key functional element in this paradigm. However, identifying external mechanisms to effectively tune magnon properties has remained a major challenge. Here we demonstrate that interfacial metal-insulator transitions offer an effective method for controlling the magnons of Sr 2 IrO 4 , a strongly spin-orbit coupled antiferromagnetic Mott insulator. Resonant inelastic x-ray scattering experiments reveal a significant softening of zone-boundary magnon energies in Sr 2 IrO 4 films epitaxially interfaced with metallic 4 d transition-metal oxides. Therefore, the magnon dispersion of Sr 2 IrO 4 can be tuned by metal-insulator transitions of the 4 d transition-metal oxides. We tentatively attribute this non-trivial behavior to a long-range phenomenon mediated by magnon-acoustic phonon interactions. Our experimental findings introduce a strategy for controlling magnons and underscore the need for further theoretical studies to better understand the underlying microscopic interactions between magnons and phonons.

magnetic properties and materials↗

Tensor decompositions for count data that leverage stochastic and deterministic optimization

There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decomposition (CPD). The challenge of fitting a low-rank, nonnegative CPD model to Poisson-distributed count data is of particular interest. Several popular algorithms use local search methods to approximate the maximum likelihood estimator (MLE) of the Poisson CPD model. Here, this work presents two new algorithms that extend state-of-the-art local methods for Poisson CPD. Hybrid GCP-CPAPR combines Generalized Canonical Decomposition (GCP) with stochastic optimization and CP Alternating Poisson Regression (CPAPR), a deterministic algorithm, to increase the probability of converging to the MLE over either method used alone. Restarted CPAPR with SVDrop uses a heuristic based on the singular values of the CPD model unfoldings to identify convergence toward optimizers that are not the MLE and restarts within the feasible domain of the optimization problem, thus reducing overall computational cost when using a multi-start strategy. We provide empirical evidence that indicates our approaches outperform existing methods with respect to converging to the Poisson CPD MLE.

CPAPR↗

Adversarial methods to reduce simulation bias in neutrino interaction event filtering at liquid argon time projection chambers

For current and future neutrino oscillation experiments using large liquid argon time projection chambers (LAr-TPCs), a key challenge is identifying neutrino interactions from the pervading cosmic-ray background. Rejection of such background is often possible using traditional cut-based selections, but this typically requires the prior use of computationally expensive reconstruction algorithms. This work demonstrates an alternative approach of using a 3D submanifold sparse convolutional network trained on low-level information from the scintillation light signal of interactions inside LAr-TPCs. This technique is applied to example simulations from ICARUS, the far detector of the short baseline neutrino program at Fermilab. The results of the network, show that cosmic background is reduced by up to 76.3% whilst neutrino interaction selection efficiency remains over 98.9%. We further present a way to mitigate potential biases from imperfect input simulations by applying domain adversarial neural networks (DANNs), for which modified simulated samples are introduced to imitate real data and a small portion of them are used for adversarial training. A series of mock-data studies are performed and demonstrate the effectiveness of using DANNs to mitigate biases, showing neutrino interaction selection efficiency performances significantly better than that achieved without the adversarial training.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Design Paradigm for Modular Multilevel Converter-Based Generator Rectifier Systems

Modular Multilevel Converters (MMC) are being widely considered for medium to high voltage DC applications. Designing such converters through multi-objective optimization is of interest because such an approach allows the trade-off between competing objectives (for example mass and loss) to be explicitly and quantitatively identified. In this work, an optimization based design paradigm for MMC based generator rectifier systems is proposed. Such development typically requires detailed component design and simulation models for the electric machine and converter which are computationally expensive. As an alternative, the proposed work utilizes an electric machine metamodel, inductor metamodel, and high-speed steady-state simulation model for the MMC to facilitate multi-objective optimization minimizing system metrics of interest while satisfying system constraints. A case study is undertaken to demonstrate the functionality of the proposed design paradigm.

42 ENGINEERING↗

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

59 BASIC BIOLOGICAL SCIENCES↗