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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

WE-D-213-04: Preparing for Parts 2 & 3 of the ABR Nuclear Medicine Physics Exam

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR professional certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of all aspects of clinical medical physics. All three parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation and skill sets necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those aspects that are unique to the nuclear exam. Medical physicists who have recently completed each of part of the ABR exam will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to Prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to Prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-D-213-00: Preparing for the ABR Diagnostic and Nuclear Medicine Physics Exams

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR professional certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of all aspects of clinical medical physics. All three parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation and skill sets necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those aspects that are unique to the nuclear exam. Medical physicists who have recently completed each of part of the ABR exam will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to Prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to Prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-D-213-01: Preparing for Part 1 of the ABR Diagnostic Physics Exam

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR professional certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of all aspects of clinical medical physics. All three parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation and skill sets necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those aspects that are unique to the nuclear exam. Medical physicists who have recently completed each of part of the ABR exam will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to Prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to Prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-D-213-02: Preparing for Part 2 of the ABR Diagnostic Physics Exam

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR professional certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of all aspects of clinical medical physics. All three parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation and skill sets necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those aspects that are unique to the nuclear exam. Medical physicists who have recently completed each of part of the ABR exam will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to Prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to Prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-D-213-03: Preparing for Part 3 of the ABR Diagnostic Physics Exam

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR professional certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of all aspects of clinical medical physics. All three parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation and skill sets necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those aspects that are unique to the nuclear exam. Medical physicists who have recently completed each of part of the ABR exam will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to Prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to Prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

End-to-End Differentiable Modeling and Management of the Environment

Focal Area: (2) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization. We emphasize the importance of leveraging optimization techniques from AI/machine learning (ML) to solve challenging problems in Earth system modeling. Science Challenge: Automatic differentiation has had a transformative effect on ML by allowing the calculation of gradients of arbitrary functions in an incredibly large class of models. We can potentially realize similar improvements in parameter estimation and control for Earth system models (ESMs) by reimplementing them in computational frameworks from ML. Practitioners working with large (>10 7 parameters) models in ML and AI can obtain good predictive performance in a range of spatiotemporal tasks by making use of optimization via stochastic gradient descent and incorporating prior knowledge at multiple levels. We propose writing ESMs in open-source computational frameworks such as Torch, Tensorflow, and JAX to greatly expand the scope of environmental forecasting and management challenges, which can be addressed by leveraging automatic differentiation and gradient descent-like algorithms. We do not call for a wholesale replacement of physical models with data-driven surrogates, but rather advocate for interleaving physical and empirical equations in a manner that is most faithful to the extent of our scientific knowledge and observational data. Central to this topic is the merging of differentiable physical simulations with differentiable optimization layers, which are now both beginning to come to the forefront.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Summary: SEE4GEO

The seismoelectric effects technique (SEE) is a new and innovative approach for geothermal subsurface imaging and monitoring at reservoir scale. The objective of this project is to assess SEE in terms of data acquisition, cost and quality, and to determine its capability in comparison with classical imaging and monitoring techniques, particularly decoupled seismic and electromagnetic methods. This will be achieved by (1) development of a fast, true 3D numerical package, handling SEE imaging and subsurface properties characterization, including resistivity and permeability, (2) laboratory experiments performed in a controlled environment to define optimal deployment design, data quality, and inform field deployment, and (3) field surveys to ultimately test and draw lessons for practical use of SEE technology. There is a relatively extensive body of work in the literature on SEE, and members of this consortium have been involved in theoretical and numerical development of SEE modeling as well as laboratory experiments. Nevertheless, to our knowledge few, if any, documented efforts have been specifically targeting the use of SEE for geothermal subsurface imaging and monitoring. The strength and originality of our proposal rely on an integrated approach leveraging numerical, laboratory and field experiments, to properly document the practical use of SEE. Through this process, SEE in-hand technology for the geothermal industry will be able to progress from a TRL 1 to TRL 3.

58 GEOSCIENCES↗
DOE OSTI2022

Seismoelectric Effects for Geothermal Resources Assessment and Monitoring (SEE4GEO)

The seismoelectric effects technique (SEE) is a new and innovative approach for geothermal subsurface imaging and monitoring at reservoir scale. The objective of this project is to assess SEE in terms of data acquisition, cost and quality, and to determine its capability in comparison with classical imaging and monitoring techniques, particularly decoupled seismic and electromagnetic methods. This will be achieved by (1) development of a fast, true 3D numerical package, handling SEE imaging and subsurface properties characterization, including resistivity and permeability, (2) laboratory experiments performed in a controlled environment to define optimal deployment design, data quality, and inform field deployment, and (3) field surveys to ultimately test and draw lessons for practical use of SEE technology. There is a relatively extensive body of work in the literature on SEE, and members of this consortium have been involved in theoretical and numerical development of SEE modeling as well as laboratory experiments. Nevertheless, to our knowledge few, if any, documented efforts have been specifically targeting the use of SEE for geothermal subsurface imaging and monitoring. The strength and originality of our proposal rely on an integrated approach leveraging numerical, laboratory and field experiments, to properly document the practical use of SEE. Through this process, SEE in-hand technology for the geothermal industry will be able to progress from a TRL 1 to TRL 3.

58 GEOSCIENCES↗
DOE OSTI2016

A promising limited angular computed tomography reconstruction via segmentation based regional enhancement and total variation minimization

X-ray computed tomography (CT) is a powerful and common inspection technique used for the industrial non-destructive testing. However, large-sized and heavily absorbing objects cause the formation of artifacts because of either the lack of specimen penetration in specific directions or the acquisition of data from only a limited angular range of views. Although the sparse optimization-based methods, such as the total variation (TV) minimization method, can suppress artifacts to some extent, reconstructing the images such that they converge to accurate values remains difficult because of the deficiency in continuous angular data and inconsistency in the projections. To address this problem, we use the idea of regional enhancement of the true values and suppression of the illusory artifacts outside the region to develop an efficient iterative algorithm. This algorithm is based on the combination of regional enhancement of the true values and TV minimization for the limited angular reconstruction. In this algorithm, the segmentation approach is introduced to distinguish the regions of different image knowledge and generate the support mask of the image. A new regularization term, which contains the support knowledge to enhance the true values of the image, is incorporated into the objective function. Then, the proposed optimization model is solved by variable splitting and the alternating direction method efficiently. A compensation approach is also designed to extract useful information from the initial projections and thus reduce false segmentation result and correct the segmentation support and the segmented image. The results obtained from comparing both simulation studies and real CT data set reconstructions indicate that the proposed algorithm generates a more accurate image than do the other reconstruction methods. The experimental results show that this algorithm can produce high-quality reconstructed images for the limited angular reconstruction and suppress the illusory artifacts caused by the deficiency in valid data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1981

University/industry collaboration in remote sensing education

A graduate level course covering the development and structure of geographical information systems and the acquisition and processing of LANDSAT data for input to these systems is described. A portion of the course was devoted to hands-on classification of LANDSAT digital tapes utilizing both university and private industry processing systems. This industry/university collaboration was extremely successful and resulted in a high quality course. It gave the students an excellent experience in working in a real-world client/consultant relationship undertaken to accomplish a specific task. There were two key factors in the success of the collaboration. First, there was a very careful product definition and advance meetings between the University faculty and the company personnel to be involved. Second, the students were not taken into the industrial facility until late in the course, after they had a reasonable knowledge of the physical bases of remote sensing, the concept of spectral signatures, and the fundamentals of pattern analysis.

Ragan, R. M.↗
NASA NTRS2008

An ATP System for Deep-Space Optical Communication

An acquisition, tracking, and pointing (ATP) system is proposed for aiming an optical-communications downlink laser beam from deep space. In providing for a direction reference, the concept exploits the mature technology of star trackers to eliminate the need for a costly and potentially hazardous laser beacon. The system would include one optical and two inertial sensors, each contributing primarily to a different portion of the frequency spectrum of the pointing signal: a star tracker (<10 Hz), a gyroscope (<50 Hz), and a precise fluid-rotor inertial angular-displacement sensor (sometimes called, simply, "angle sensor") for the frequency range >50 Hz. The outputs of these sensors would be combined in an iterative averaging process to obtain high-bandwidth, high-accuracy pointing knowledge. The accuracy of pointing knowledge obtainable by use of the system was estimated on the basis of an 8-cm-diameter telescope and known parameters of commercially available star trackers and inertial sensors: The single-axis pointing-knowledge error was found to be characterized by a standard deviation of 150 nanoradians - below the maximum value (between 200 and 300 nanoradians) likely to be tolerable in deep-space optical communications.

Lee, Shinhak↗
DOE OSTI2019

Multi-component background learning automates signal detection for spectroscopic data

Abstract Automated experimentation has yielded data acquisition rates that supersede human processing capabilities. Artificial Intelligence offers new possibilities for automating data interpretation to generate large, high-quality datasets. Background subtraction is a long-standing challenge, particularly in settings where multiple sources of the background signal coexist, and automatic extraction of signals of interest from measured signals accelerates data interpretation. Herein, we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest. The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals. While the model can incorporate prior knowledge, it does not require knowledge of the signals since the shapes of the background signals, the noise levels, and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework. Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets, a transformative capability with many applications in the physical sciences and beyond.

Ament, Sebastian E.↗
DOE OSTI2015

Femtosecond stimulated Raman spectroscopy by six-wave mixing

Femtosecond Stimulated Raman Spectroscopy (FSRS) is motivated by the knowledge of the molecular geometry changes that accompany sub-picosecond chemical reactions. The detection of vibrational resonances throughout the entire fingerprint region of the spectrum with sub-100-fs delay precision is fairly straightforward to accomplish with the FSRS technique. Despite its utility, FSRS must contend with substantial technical challenges that stem from a large background of residual laser light and lower-order nonlinearities when all laser pulses are electronically resonant with the equilibrium system. In this work, a geometry based on five incident laser beams is used to eliminate much of this undesired background in experiments conducted on metmyoglobin. Compared to a three-beam FSRS geometry with all electronically resonant laser pulses, the five-beam approach described here offers major improvements in the data acquisition rate, sensitivity, and background suppression. The susceptibility of the five-beam geometry to experimental artifacts is investigated using control experiments and model calculations. Of particular concern are undesired cascades of third-order nonlinearities, which are known to challenge FSRS measurements carried out on electronically off-resonant systems. It is generally understood that “forbidden” steps in the desired nonlinear optical processes are the origin of the problems encountered under off-resonant conditions. In contrast, the present experiments are carried out under electronically resonant conditions, where such unfortunate selection rules do not apply. Nonetheless, control experiments based on spectroscopic line shapes, signal phases, and sample concentrations are conducted to rule out significant contributions from cascades of third-order processes. Theoretical calculations are further used to estimate the relative intensities of the direct and cascaded responses. Overall, the control experiments and model calculations presented in this work suggest promise for multidimensional resonance Raman investigations of heme proteins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Cyber-Attack Detection for Photovoltaic Farms Based on Power-Electronics-Enabled Harmonic State Space Modeling

Here in this paper, a physics-data-based detection method is proposed to detect a variety of cyber-attacks in Photovoltaic (PV) farms using the power electronics-enabled harmonic state space (HSS) models, which, to our knowledge, is original. At the device level, HSS-based detection is developed to monitor harmonic vectors of individual PV converter with minimum sensor measurements, thus improving accuracy and robustness compared to Kalman Filter-based detection. At the system level that involves multiple PV converters, a clustering approach is developed to investigate attack propagation and accurately locate attack sources within a PV farm. The proposed approach is one of the first attempts to address PV security through interaction between the device and system, maximizing the accuracy and robustness at different levels. To verify the feasibility, a comprehensive attacks model is built, including single attack, coordinated attacks, and replay attacks. Besides, the impacts of irradiance changes are taken into consideration in the test scenarios. With the real-time data acquisition and hardware-in-the-loop testbed, comprehensive test results are provided to verify the feasibility of the proposed detection methodology.

42 ENGINEERING↗
DOE OSTI2019

Wind direction estimation using SCADA data with consensus-based optimization

Abstract. Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.

17 WIND ENERGY↗
NASA NTRS2020

Is Structured Agile an Oxymoron? Tales from Implementing and Executing Agile in a US Government Environment

To paraphrase a famous quote, "No plan survives contact with the reality." Software (SW) development is often a classic example of this: whatever the plan was for a particular development, it often does not survive contact with technical realities, budget realities, program realities and schedule realities. Traditionally, SW development has followed a waterfall methodology with requirements being rigorously specified before the design, which was completed before the coding and unit testing started, which were in turn finished before validation and verification started. This model of SW engineering derives much from the HW engineering of large systems, and has been the standard methodology used in US government software acquisitions and systems for decades, with highly variable results. US Government SW requirements are built around Waterfall concepts, which assume that the plan will survive contact with reality, or at least that modifications to the plan are relatively small, and relatively few.Because of the inefficiencies and difficulties inherent in Waterfall, the commercial SW world started using a different SW development methodology called Agile more than 20 years ago. Agile believes that a plan should evolve and learn rapidly in response to the realities encountered. At its core, there are a few key elements of Agile:- A small team of people which is highly flexible and adaptive. The team collaborates and interoperates through sophisticated development architectures and release environments- An iterative, incremental development and release approach which is based upon the concept that knowledge comes from experience within the team, and that the team makes decisions based upon what it knows- A team culture which prizes transparency, inspection and adaptation. These values are necessary so that the team experience and decision making is transparent and responsive to the realities encountered during development and testingSo, how to use Agile in a US Government environment? GMSEC (Goddard Mission Services Evolution Center) develops satellite ground system software for NASA and other US Government agencies. The SW developed by the team contains a large code base of many applications used within satellite mission operations centers. It spans the full gamut of SW development types: from SW which is in a classic maintenance and sustainment mode, to new developments with a fairly well understood scope and approach, to new developments whose scope and approach are quite unclear and which require significant research and prototyping. Team members move between all of these different types of SW development. Waterfall was inadequate to the programmatic and technical needs of the team, as well as the various types of SW development being done. The software plan was not surviving contact with the technical and programmatic realities experienced by the team. To address this, the team started a small pilot project in 2016 to test the use of Agile within a small subset of the team for a new web services application. In early 2018, the use of Agile was expanded to the whole team and all the software, but we had to fulfill the NASA SW development requirements. And we needed to do this while still remaining true to the key Agile elements of transparency, inspection and adaption. In order to do this, the team worked very closely with the Software Process Improvement (SPI) team at NASA Goddard, as well as NASA engineering manageme

Beech, Theresa W.↗
DOE OSTI2020

Cyberguardians Webinar

Robust solar industry growth is creating a shortage of U.S. workers who are knowledgeable in emerging Distributed Energy Resource (DER) technologies including networking, operational software, smart advanced inverters, smart advanced PV modules, and cybersecurity. Retraining military veterans and other qualified individuals to fill workforce gaps in those technologies is the project focus. The project leverages the experience and contributions of DER and cybersecurity experts, educational organizations offering accreditation, and groups serving our nation’s veterans to meet its goals. To bridge this workforce skills gap, the Cyberguardians and STEM Warriors project will establish a complete learning and career advancement system for veterans, transitioning military personnel and other qualified individuals into the DER workforce. The project will guide participants successfully through the process of program/vocation selection, curriculum enrollment, academic mentoring, degree or certificate attainment, job board enrollment, introduction to hiring managers at DER companies, and successful job acquisition. This webinar was used to promote the Cyberguardians program and to ensure ample news coverage to reach veterans, employers, and others that would benefit from this program.

14 SOLAR ENERGY↗
DOE OSTI2023

Dissecting components of the Campylobacter jejuni fetMP-fetABCDEF gene cluster under iron limitation

Campylobacter jejuni is a leading cause of bacterial gastroenteritis worldwide. Acute infection can be an antecedent to highly debilitating long-term sequelae. The expression of iron acquisition systems is vital for C. jejuni to survive the low iron availability within the human gut. The C. jejuni fetMP-fetABCDEF gene cluster is known to be upregulated during human infection and under iron limitation. While FetM and FetP have been functionally linked to iron transport in prior work, here we assess the contribution of each of the downstream genes (fetABCDEF) to C. jejuni growth during both iron-depleted and iron-replete conditions. Significant growth impairment was observed upon disruption of fetA, fetB, fetC, and fetD, suggesting a role in FetMP-mediated iron acquisition for each encoded protein. FetA expression was not dependent on the presence of FetB, FetC, FetD, FetE, or FetF. The functions of the putative thioredoxins FetE and FetF were redundant under iron-limited growth, requiring a double deletion (ΔfetEF) to exhibit a growth defect. C. jejuni FetE was expressed, and the structure was solved to 1.50 Å, revealing structural similarity to thiol-disulfide oxidases. Functional characterization in biochemical assays showed that FetE reduced insulin at a slower rate than Escherichia coli Trx and that together, FetEF promoted substrate oxidation in cell extracts, suggesting that FetE (and presumably FetF) are oxidoreductases that can mediate oxidation in vivo. This study advances our understanding of the contributions of the fetMP-fetABCDEF gene cluster to virulence at a genetic and functional level, providing foundational knowledge toward mitigating C. jejuni-related morbidity and mortality.

59 BASIC BIOLOGICAL SCIENCES↗