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DOE OSTI2023

Integrating Artificial Intelligence into Science Gateways

Science gateways are altering the manner in which people interact with high performance computing (HPC) by providing a web browser based interface to advanced computing platforms. In particular, science gateways lower the barrier to using HPC by simplifying the process of submitting workloads to such systems and by offloading the efforts required to use HPC to the maintainers of the system. While science gateways decrease the time-to-science that comes with using such advanced systems, progress can still be made in improving the user's experience. In this paper we explore two strategies for integrating artificial intelligence tools commonly found in non-HPC service workflows: voice activated assistants and chatbots. Since August 2021, the HPC group at Idaho National Laboratory answers an average of 581 support tickets per month of which a large percentage could be addressed via these two strategies. This work defines the key capabilities that an HPC voice activated assistant and chatbot would need to address for a userbase consisting of largely non-expert users as well as a design for integration into the Open OnDemand science gateway.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Automatic DDoS Attack Detection on SDNs: Preprint

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks pose a serious threat to computing networks - especially to critical systems within the U.S. electrical grid. As attack mechanisms have increased in complexity and variety, more sophisticated detection mechanisms have become necessary to ensure network security. This paper explores the use of artificial intelligence to automate the process of detection and mitigation of DoS and DDoS attacks within the framework of Software-Defined Networking (SDN), to a high degree. Machine learning algorithms are trained to recognize DoS and DDoS attacks and are deployed in real-time to mitigate malicious network traffic. The results show a well-tuned gradient-boosted decision tree detecting DoS and DDoS attacks, as well as initial successful mitigation of attacks within an SDN framework.

cyber detection↗

DOE OSTI2024

Redesigning large-scale multimodal transit networks with shared autonomous mobility services

Here, this study addresses a large-scale multimodal transit network design problem, with Shared Autonomous Mobility Services (SAMS) as both transit feeders and an origin-to-destination mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and generates transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, satisfying transit origin-destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each zone are characterized with continuous approximation. The decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. The model is illustrated through a set of experiments with the Chicago network comprised of 50 zones and seven modes, under three scenarios. The computational results present savings in traveler journey time and operator cost demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.

Autonomous vehicles↗

DOE OSTI2021

The flexibility gap: Socioeconomic and geographical factors driving residential flexibility

Residential consumers are moving to the center of electricity systems and their flexibility is seen as a key resource to integrate renewable energy sources and support the grid. However, residential flexibility capacities are not homogeneous, as they depend on household appliances, comfort patterns, occupancy, and climate conditions. In this work, we calculate the technical flexibility capacities of 45 consumer types in mainland Spain, organised according to income and regional criteria. We show that flexibility gaps exist at both regional and socioeconomic (income) levels with flexibility differences of up to 10 times more capacity between the household groups from the lowest to the highest capacities. These geographical and socioeconomic gaps in flexibility can lead to distortions in national markets and have the potential to exclude citizens from the provision of flexibility services. Our results show in quantitative terms that a consumer-centered approach without considering correcting measures nor these gaps in drafting energy policies may lead to increasing inequality levels in the residential sector. Under an economic competitive paradigm, households with lower income levels or located in regions with lower flexibility potential may be excluded from the provision of flexibility to the detriment of households with larger potential, raising justice concerns in a flexibility-based energy transition.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Thank You to Our Peer Reviewers in 2025

Peer reviewing is the foundation of modern scholarship, with external specialists being asked to fairly check and evaluate submitted work. This difficult and often time‐consuming activity is performed voluntarily, with the understanding that one's own scholarship shall benefit down the line from a careful analysis of its assumption, results, accuracy, and yes, language, as we are now evaluating someone else's work. At Space Weather, we pride ourselves on a fair but quick review process yielding high‐quality articles with a time from submission to first decision of less than 2 months. This would not be possible without the hard work of all our reviewers. Once a year, we take the occasion to name these reviewers to thank them for their service to the journal and the community.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗

DOE OSTI2020

Monthly Sewer Monitoring Report for LLNL Livermore Site, April 2020

Lawrence Livermore National Laboratory (LLNL) collects effluent samples from its sewer outfall at the B196 Sewer Monitoring Station (SMS). Effluent flow-proportional composite samples are collected at the SMS on a daily (midnight-to-midnight), weekly (Thursday through Wednesday), and “monthly” (composited from daily) basis; effluent grab samples are also collected each month at that same location. Certified contract laboratories analyzes these compliance samples, and results (Tables 1–3) are used to establish LLNL compliance with the 2019–2020 Wastewater Discharge Permit (Permit 1250) granted by the City of Livermore Water Resources Division (WRD). Supplemental analyses for biochemical oxygen demand (BOD) and total suspended solids (TSS) are performed onsite and reported (Table 4) for the calculation and assessment of sewer service charges. Quarterly, effluent is sampled for metals (24-hour composite) and cyanide (grab sample) concentrations (mg/L). The results are presented in Table 5. In addition to the sampling noted above, the SMS continuously monitors LLNL sewage effluent in real-time for flow rate, pH, metals, and radioactivity. Standard measurement methods are used to monitor flow and pH to determine permit compliance. Unique nonstandard analytical methods monitor for the presence of metals and radioactivity using x-ray fluorescence (XRF) and gamma spectroscopy, respectively. If an anomalous condition is detected by the monitoring system, an alarm activates LLNL’s Sewer Diversion Facility (SDF) and Livermore Water Reclamation Plant (LWRP) operators are notified that a potential release has occurred.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

LWR Aging Management for Life Beyond 80

While several decades of materials degradation research enabled the current US fleet of Light Water Reactors (LWR) to plan to produce electrical power through extended operations up to 80 years of plant life, it now seems appropriate to evaluate what issues, methods, and timelines need to be considered to meet the expected electric power demands for life beyond eighty (LBE) years. To address these questions, it will be necessary to review the electrical capacity and the projected LWR fleet capacity including new builds and advanced reactors. This effort should begin with a materials degradation assessment focused on known and possible unknown issues using a reduced Expanded Materials Degradation Assessment [1], which was employed to identify knowledge gaps for a second license renewal (60-80 yrs.). The focus of the LBE assessment should include: Establishing a timeline to initiate assessments of possible materials aging issues and to ensure sufficient time to evaluate materials degradation research gaps; assessing components and materials to be evaluated, e.g., metals (reactor pressure vessels and alloys within and outside the pressure boundary), concrete, cables, as well as mitigation methods, advanced monitoring, and validation with ex-service materials; initiating a LBE research plan focused on developing an expanded mechanistic understanding of materials degradation and refined models through Codes and Standards evaluation for use by the nuclear industry; and maintaining and strengthening the nuclear materials human knowledge base to address new and as yet unknown degradation modes.

Chen, Xiang↗

DOE OSTI2023

Retractable Sensors for In-Core Service in Material Test Reactors

Material Test Reactors (MTRs) such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) are used to irradiate nuclear fuels and materials to evaluate their performance after high levels of exposure to a reactor in-core environment. The most critical tests are equipped with instrumentation leads, which allow real-time data collection. However, because of the very harsh environment inside high-power MTR experiments, there are very few sensors that can survive and maintain their calibrated readings for the time periods required to obtain the high neutron doses needed for new fuels and materials qualification. As a result, sometimes sponsoring programs are forced to accept low reliability of sensors, collecting useful data for only part of the experiment duration. The work described herein is based on the observation that MTRs normally run at constant power and the corresponding conditions within reactor experiments typically evolve relatively slowly. Therefore, even one or two measurements per day would provide a complete and representative data set. With this in mind, INL has embarked on a program to develop a mechanism capable of pushing a very small-diameter sensor (typically a thermocouple or optical fiber) into the location to be measured, leave the sensor for roughly 60 seconds to allow it to reach equilibrium and transmit the signal, then pull it up and away from the high neutron flux and high-temperature region. Small-diameter capillary tubes, up to 8 m long, are used to guide the sensors to the appropriate locations. These capillary tubes serve as essentially very deep, thin-walled thermowells. The distance a thermocouple or optical fiber would need to traverse is on the order of 40 - 80 cm. By adopting this infrequent cycling strategy, the thermocouple or optical fiber would spend only a few hours in the high-neutron flux/high-temperature environment over the duration of even the longest irradiation experiment. To date, INL has developed two styles of drive mechanisms. The first is based on counter-rotating wheels which drive the sensors in a manner similar to a small MIG welder. This has the advantage of being able to accommodate a very long insertion length. The second is based on a ball screw drive and has the advantages of positive attachment and being able to move more than one sensor at a time. Both drive mechanisms have been fabricated and tested in a laboratory setting. Both systems can handle hard mineral insulated cable (such as thermocouples) or optical fibers encased in small diameter tube. The sizes tested to date are 1 - 1.6 mm diameter. Work in this area is ongoing with an eye toward demonstration in the Massachusetts Institute of Technology's MITR reactor, followed by deployment in an ATR irradiation experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Retractable Sensors for In-Core Service in Material Test Reactors

Material Test Reactors (MTRs) such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) are used to irradiate nuclear fuels and materials to evaluate their performance after high levels of exposure to a reactor in-core environment. The most critical tests are equipped with instrumentation leads, which allow real-time data collection. However, because of the very harsh environment inside high-power MTR experiments, there are very few sensors that can survive and maintain their calibrated readings for the time periods required to obtain the high neutron doses needed for new fuels and materials qualification. As a result, sometimes sponsoring programs are forced to accept low reliability of sensors, collecting useful data for only part of the experiment duration. The work described herein is based on the observation that MTRs normally run at constant power and the corresponding conditions within reactor experiments typically evolve relatively slowly. Therefore, even one or two measurements per day would provide a complete and representative data set. With this in mind, INL has embarked on a program to develop a mechanism capable of pushing a very small-diameter sensor (typically a thermocouple or optical fiber) into the location to be measured, leave the sensor for roughly 60 seconds to allow it to reach equilibrium and transmit the signal, then pull it up and away from the high neutron flux and high-temperature region. Small-diameter capillary tubes, up to 8 m long, are used to guide the sensors to the appropriate locations. These capillary tubes serve as essentially very deep, thin-walled thermowells. The distance a thermocouple or optical fiber would need to traverse is on the order of 40 – 80 cm. By adopting this infrequent cycling strategy, the thermocouple or optical fiber would spend only a few hours in the high-neutron flux/high-temperature environment over the duration of even the longest irradiation experiment. To date, INL has developed two styles of drive mechanisms. The first is based on counter-rotating wheels which drive the sensors in a manner similar to a small MIG welder. This has the advantage of being able to accommodate a very long insertion length. The second is based on a ball screw drive and has the advantages of positive attachment and being able to move more than one sensor at a time. Both drive mechanisms have been fabricated and tested in a laboratory setting. Both systems can handle hard mineral insulated cable (such as thermocouples) or optical fibers encased in small diameter tube. The sizes tested to date are 1 – 1.6 mm diameter. Work in this area is ongoing with an eye toward demonstration in the Massachusetts Institute of Technology’s MITR reactor, followed by deployment in an ATR irradiation experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

A New Design Consideration for Bidirectional Resonant Converters

This paper introduces innovative design considerations for a bidirectional resonant converter, optimized for Electric Vehicle (EV) charging and vehicle-togrid (V2G) applications. By eliminating the need for complex voltage or current gain functions, the proposed design greatly simplifies the development process, making it more accessible for manufacturers and system integrators. This approach not only reduces engineering time and resource investment but also accelerates time-to-market, giving stakeholders a competitive edge in rapidly expanding EV infrastructure markets. In addition, the streamlined design ensures soft switching at maximum output power, leading to higher efficiency and improved durability, which translates to lower operating costs and a longer service life for EV charging equipment. These benefits provide a clear pathway to more cost-effective, scalable, and sustainable EV charging and V2G solutions, enhancing value for stakeholders by facilitating smoother integration into the grid and supporting energy resilience. To validate the benefits, a 60 kW bidirectional charger with a flexible input range of 700−900VDC and an output range of 400−1250VDC was developed, underscoring the design's adaptability and potential for large-scale implementation.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗

DOE OSTI2020

Development of Cable Aging Acceptance Criteria for Nuclear Facilities

The aging of nuclear power plant electrical cables has been the subject of substantial research and development (R&D) projects in the nuclear industry, national and international laboratories, universities, and vendor organizations for several years. More specifically, these R&D efforts were conducted to develop equipment and techniques for assessing age-related degradation of in-service cables in nuclear facilities including power plants, research reactors, waste facilities and fuel fabrication plants. In recent years, cable R&D efforts in the nuclear arena have also included work to develop new cables for the next generation of reactors. Over time, exposure to harsh environmental conditions such as elevated temperatures, radiation, and humidity in nuclear installations can result in age-related degradation and failure of cables. In the current fleet of nuclear reactors, there are thousands of miles of cabling installed in each plant and many of these cables are exposed to these harsh environmental conditions. For these cables, the jacket and insulation polymers harden and become brittle over time, making them more susceptible to crack formation and growth, moisture intrusion, and other mechanisms that can lead to cable failure. Moreover, the existing U.S. fleet of 98 nuclear reactors has an average operating age of 38 years. Many of these nuclear power plants have applied for and almost all have been granted regulatory approval for license renewals to operate for 60 years, 20 years beyond their original 40-year life. Further, subsequent license renewals (SLRs) are underway with a few commercial nuclear power sites already approved to operate up to 80 years. As these reactors pursue operating life extensions, utilities must find a way to address issues associated with age-related degradation of cables. Designers of small modular reactors are also in need of technologies to evaluate the performance of cables that will be installed in harsher environments (e.g. higher temperatures, radiation doses, dose rates, etc.) than those present in current-generation reactors. Today, a variety of cable condition monitoring (CM) techniques have been developed and successfully used in nuclear facilities. These techniques are used to identify age-related degradation and assess the condition of cables to determine if their performance characteristics have changed with age. However, objective criteria must be developed for these CM tests to help quantify cable condition and thereby develop repair and replacement schedules. With operating life extending to 80 years and more SLRs on the horizon, both nuclear facilities and regulators need an objective means to determine the aged condition of cables.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Development of Cable Aging Acceptance Criteria for Nuclear Facilities

36 MATERIALS SCIENCE↗

DOE OSTI2025

Noble Liquid Test Facility at Fermilab

The Noble Liquid Test Facility (NLTF) at Fermilab is a liquid argon detector R\&D facility open to the national and international HEP community. The facility consists of 4 permanent cryostats, ranging from 250L up to 3000L, open space for small open dewar testing, and an optical test stand facility capable of measuring the optical properties of materials and characterizing photon detectors. NLTF’s strongest advantage is its capability to provide ultra-pure LAr in a reliable manner. Its inline filters are capable of filtering all of the three biggest contaminants for standard LAr detectors, O$_2$ and H$_2$O down to $< 1$ppb and N$_2$ $< 1$ppm. This is critical for the users of the test stands as small levels of impurities can dramatically change the efficiency of LArTPCs for the collection of charge and light. The smallest cryostat is mainly used for material testing, a service provided to the international HEP community interested in understanding how the introduction of a specific material might affect the electron lifetime in LAr. The test stands can be equipped with a purity monitor, which allows measuring the electron lifetime in real time, as well as gas sampling and analyzing, and in the near future, local recirculation and filtering. The facility has hosted many successful R\&D projects, which have published their results in well-known journals and talks. A few examples of such projects are: NIR light production in LAr and GAr, the characterization of VUV metalenses, the testing of new filter media capable of filtering N$_2$ from LAr, high voltage studies and direct charge amplification in LAr, and various doping studies.

Blaszczyk, Flor María [Fermilab]↗

DOE OSTI2021

Managing the techno-economic impacts of partial string failure in multistring energy storage systems

The role of energy storage systems (ESSs) is becoming increasingly important for today’s electric power systems. Unavailability of an ESS assigned to critical grid services may cause unwanted disruption of those services and hence, may have a significant techno-economic impact. Like any physical equipment, an ESS is vulnerable to various types of faults. Failure of one or more strings in a multistring ESS does not have to be the cause of shutting down the entire ESS. It can still operate with a partial number of strings and continue providing critical services to the grid, if there are no reliability or safety issues and is acceptable under applicable standards. However, it is important to make sure that the control strategies are adaptable to the changes in ESS capacity caused by failed strings. Also, depending on the previous operation and type of failure, the reallocation of duty cycle burden among available strings could be non-uniform. These complexities suggest that mitigation of the impact of partial failure in multistring ESSs is not trivial and needs careful consideration. This is the topic of this paper. The proposed work investigates the impact of partial failure of a large multistring ESS on the assigned service and develops strategies to adjust the ESS control duty cycles for reducing such impacts. In doing so, the paper proposes a novel two-stage framework that plans for the multistring failure using robust optimization theory and then adjusts in real-time using a rule-based algorithm, based on the real-time information on the power availability of the string. Illustrations provided in this work are based on frequency regulation use-case, which is a common application for many utility-scale ESSs. A 750 kilowatt (kW)/1500 kilowatt-hour (kWh) 3-string ESS is used for the demonstration in this work and the efficacy of the proposed method is demonstrated and compared against methods that do not incorporate string failure in their strategy. In particular, here we show that revenue loss of 93% can be incurred when partial string failure is not included in operation and planning. These losses in revenue are reduced by 60% with the proposed method.

25 ENERGY STORAGE↗

DOE OSTI2023

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Autonomous Cyber Defense Against Dynamic Multi-strategy Infrastructural DDoS Attacks

Dynamic Infrastructural Distributed Denial of Service (I-DDoS) attacks constantly change attack vectors to congest core backhaul links and disrupt critical network availability while evading end-system defenses. To effectively counter these highly dynamic attacks, defense mechanisms need to exhibit adaptive decision strategies for real-time mitigation. This paper presents a novel Autonomous DDoS Defense framework that employs model-based reinforcement agents. The framework continuously learns attack strategies, predicts attack actions, and dynamically determines the optimal composition of defense tactics such as filtering, limiting, and rerouting for flow diversion. Our contributions include extending the underlying formulation of the Markov Decision Process (MDP) to address simultaneous DDoS attack and defense behavior, and accounting for environmental uncertainties. We also propose a fine-grained action mitigation approach robust to classification inaccuracies in Intrusion Detection Systems (IDS). Additionally, our reinforcement learning model demonstrates resilience against evasion and deceptive attacks. Evaluation experiments using real-world and simulated DDoS traces demonstrate that our autonomous defense framework ensures the delivery of approximately 96 - 98% of benign traffic despite the diverse range of attack strategies.

Dutta, Ashutosh↗

DOE OSTI2023

Reverse engineering environmental metatranscriptomes clarifies best practices for eukaryotic assembly

Abstract Background Diverse communities of microbial eukaryotes in the global ocean provide a variety of essential ecosystem services, from primary production and carbon flow through trophic transfer to cooperation via symbioses. Increasingly, these communities are being understood through the lens of omics tools, which enable high-throughput processing of diverse communities. Metatranscriptomics offers an understanding of near real-time gene expression in microbial eukaryotic communities, providing a window into community metabolic activity. Results Here we present a workflow for eukaryotic metatranscriptome assembly, and validate the ability of the pipeline to recapitulate real and manufactured eukaryotic community-level expression data. We also include an open-source tool for simulating environmental metatranscriptomes for testing and validation purposes. We reanalyze previously published metatranscriptomic datasets using our metatranscriptome analysis approach. Conclusion We determined that a multi-assembler approach improves eukaryotic metatranscriptome assembly based on recapitulated taxonomic and functional annotations from an in-silico mock community. The systematic validation of metatranscriptome assembly and annotation methods provided here is a necessary step to assess the fidelity of our community composition measurements and functional content assignments from eukaryotic metatranscriptomes.

Krinos, Arianna I. (ORCID:0000000197678392)↗