Preliminary Designs for a Freeze-Safe Valve Stem Heat Pipe.
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This presentation was given at the 2023 U.S. Department of Energy National Energy Technology Laboratory Resource Sustainability Project Review Meeting. The presentation topics cover recent updates and current research directions for the Strategic Systems Analysis and Engineering Directorate in Water Management for Power Systems. The presentation includes preliminary results to meet objectives in reducing freshwater consumption and lowering the cost of treating effluent streams for energy production.
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In 2019, Secretary of the Air Force (SECAF) Heather Wilson launched the 2030 Science and Technology (S&T) Strategy by stating, “The advantage will go to those who create the best technologies and who integrate and field them in creative operational ways that provide military advantages.” In 2021, 15% of Air Force general officers responsible for creating and integrating this technological edge had a science, technology, engineering, and math (STEM) graduate degree, and less than 1% of generals had a STEM doctorate. As one point of comparison, at least 32% and 13% of founding CEOs of Fortune 500 technology companies had a master’s and doctoral STEM degree, respectively. The gulf is larger when you consider that these CEOs used their technical degrees throughout their careers whereas most general officers do not. In large organizations, if the leader does not possess technical knowledge, it is difficult to drive innovation systems and connect ideas to reality. The evidence for this disconnect is abundant in acquisition challenges for high-tech systems, significant pushback to the “revolution in military affairs”, and failure of the “Third Offset” to take hold and deliver capabilities to offset Chinese and Russian capabilities. Indeed, when the former Air Force and Space Force Chief Software Officer, Nicolas M. Chaillan, offered his resignation, one of the main reasons for leaving was “the failure of OSD and the Joint Staff to deliver on their own alleged top ‘priority’, JADC2 – they couldn’t ‘walk the walk.’” Senior leaders have recognized this disconnect, and several inquiries and studies regarding STEM competency have been conducted. Many have posed a false dilemma: a large, STEM-cognizant or small, STEM-expert force. However, current and future STEM human capital needs are managed at the unit and functional area level. This leads to “silos” and the tactical “needs of the present” dominating the strategic “needs of the future”, highlighted by only two functional areas systematically tracking future STEM needs. Therefore, the decision on force structure appears to have been historically made by default through these and other perceived structural realities, and a vast majority of recommendations have focused on the small STEM community – an exercise in diminishing returns. In essence, the solution often boils down to finding unicorns – officers that have chartered the unforgiving pathway through the traditional “gates” to general officer while obtaining sufficient STEM proficiency along the way. Fortunately for the Air Force, these officers exist, albeit at a rate below what the evidence would suggest is necessary. However, this approach fundamentally limits the ability to develop a deep pool of officers with the ability to create and integrate technologies and ensures the Dunning-Kruger effect is prevalent. The Dunning-Kruger effect is a cognitive bias where those with limited knowledge in an area lack the expertise necessary to recognize their lack of expertise and consequently are prone to overestimate their knowledge and performance. In short, we need less leaders overestimating their STEM knowledge and more with hard-earned STEM competency required to “walk the walk” to create and integrate technologies for military advantage. This will not be accomplished by restricting the emphasis to the traditional scientists and engineering (S&E) career fields – the pool, only 10% of line officers, and general officer progression is just too small. Fortunately, the recent SECAF’s Management Initiatives and Chief of Staff’s (CSAF) Action Orders, both containing emphasis on organic expertise to “accrue advantage in military-technological competition,” present an opportunity to truly develop a framework for the force of the future.
The structure of an anti-HIV-1 ribozyme-DNA abortive substrate complex was investigated by 750 MHz NMR and computer modeling experiments. The ribozyme was a chimeric molecule with 30 residues-18 DNA nucleotides, and 12 RNA residues in the conserved core. The DNA substrate analog had 17 residues. The chimeric ribozyme and the DNA substrate formed a shortened ribozyme-abortive substrate complex of 47 nucleotides with two DNA stems (stems I and III) and a loop consisting of the conserved core residues. Circular dichroism spectra showed that the DNA stems assume A-family conformation at the NMR concentration and a temperature of 15 degrees C, contrary to the conventional wisdom that DNA duplexes in aqueous solution populate entirely in the B-form. It is proposed that the A-family RNA residues at the core expand the A-family initiated at the core into the DNA stems because of the large free energy requirement for the formation of A/B junctions. Assignments of the base H8/H6 protons and H1' of the 47 residues were made by a NOESY walk. In addition to the methyl groups of all T's, the imino resonances of stems I and III and AH2's were assigned from appropriate NOESY walks. The extracted NMR data along with available crystallographic data, were used to derive a structural model of the complex. Stems I and III of the final model displayed a remarkable similarity to the A form of DNA; in stem III, a GC base pair was found to be moving into the floor of the minor groove defined by flanking AT pairs; data suggest the formation of a buckled rhombic structure with the adjacent pair; in addition, the base pair at the interface of stem III and the loop region displayed deformed geometry. The loop with the catalytic core, and the immediate region of the stems displayed conformational multiplicity within the NMR time scale. A catalytic mechanism for ribozyme action based on the derived structure, and consistent with biochemical data in the literature, is proposed. The complex between the anti HIV-1 gag ribozyme and its abortive DNA substrate manifests in the detection of a continuous track of A.T base pairs; this suggests that the interaction between the ribozyme and its DNA substrate is stronger than the one observed in the case of the free ribozyme where the bases in stem I and stem III regions interact strongly with the ribozyme core region (Sarma, R. H., et al. FEBS Letters 375, 317-23, 1995). The complex formation provides certain guidelines in the design of suitable therapeutic ribozymes. If the residues in the ribozyme stem regions interact with the conserved core, it may either prevent or interfere with the formation of a catalytically active tertiary structure.
Abstract Here we show that compressive sensing allows 4‐dimensional (4‐D) STEM data to be obtained and accurately reconstructed with both high‐speed and reduced electron fluence. The methodology needed to achieve these results compared to conventional 4‐D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide dataset shows that it is possible to recover over 25 dB peak signal‐to‐noise ratio in the recovered phase using 0.3% of the total data. Lay abstract : Four‐dimensional scanning transmission electron microscopy (4‐D STEM) is a powerful technique for characterizing complex nanoscale structures. In this method, a convergent beam electron diffraction pattern (CBED) is acquired at each probe location during the scan of the sample. This means that a 2‐dimensional signal is acquired at each 2‐D probe location, equating to a 4‐D dataset. Despite the recent development of fast direct electron detectors, some capable of 100kHz frame rates, the limiting factor for 4‐D STEM is acquisition times in the majority of cases, where cameras will typically operate on the order of 2kHz. This means that a raster scan containing 256^2 probe locations can take on the order of 30s, approximately 100‐1000 times longer than a conventional STEM imaging technique using monolithic radial detectors. As a result, 4‐D STEM acquisitions can be subject to adverse effects such as drift, beam damage, and sample contamination. Recent advances in computational imaging techniques for STEM have allowed for faster acquisition speeds by way of acquiring only a random subset of probe locations from the field of view. By doing this, the acquisition time is significantly reduced, in some cases by a factor of 10‐100 times. The acquired data is then processed to fill‐in or inpaint the missing data, taking advantage of the inherently low‐complex signals which can be linearly combined to recover the information. In this work, similar methods are demonstrated for the acquisition of 4‐D STEM data, where only a random subset of CBED patterns are acquired over the raster scan. We simulate the compressive sensing acquisition method for 4‐D STEM and present our findings for a variety of analysis techniques such as ptychography and differential phase contrast. Our results show that acquisition times can be significantly reduced on the order of 100‐300 times, therefore improving existing frame rates, as well as further reducing the electron fluence beyond just using a faster camera.
Biomass crops engineered to accumulate energy-dense triacylglycerols (TAG or ‘vegetable oils’) in their vegetative tissues have emerged as potential feedstocks to meet the growing demand for renewable diesel and sustainable aviation fuel (SAF). Unlike oil palm and oilseed crops, the current commercial sources of TAG, vegetative tissues, such as leaves and stems, only transiently accumulate TAG. In this report, we used grain (Texas430 or TX430) and sugar-accumulating ‘sweet’ (Ramada) genotypes of sorghum, a high-yielding, environmentally resilient biomass crop, to accumulate TAG in leaves and stems. We initially tested several gene combinations for a ‘push-pull-protect’ strategy. The top TAG-yielding constructs contained five oil transgenes for a sorghum WRINKLED1 transcription factor (‘push’), a Cuphea viscosissima diacylglycerol acyltransferase (DGAT; ‘pull’), a modified sesame oleosin (‘protect’) and two combinations of specialized Cuphea lysophosphatidic acid acyltransferases and medium-chain acyl-acyl carrier protein thioesterases. Though intended to generate oils with medium-chain fatty acids, engineered lines accumulated oleic acid-rich oil to amounts of up to 2.5% DW in leaves and 2.0% DW in stems in the greenhouse, 36-fold and 49-fold increases relative to wild-type (WT) plants, respectively. Under field conditions, the top-performing event accumulated TAG to amount to 5.5% DW in leaves and 3.5% DW in stems, 78-fold and 58-fold increases, respectively, relative to WT TX430. Transcriptomic and fluxomic analyses revealed potential bottlenecks for increased TAG accumulation. Overall, our studies highlight the utility of a lab-to-field pipeline coupled with systems biology studies to deliver high vegetative oil sorghum for SAF and renewable diesel production.
Previous research indicated that mouse bone marrow cells could be grown in conditions of simulated microgravity. This environment was created in rotating bioreactor vessels. On three attempts mouse cells were grown successfully in the vessels. The cells reached a stage where the concentrations were doubling daily. Phenotypic analysis using a panel of monoclonal antibodies indicated that the cell were hematopoietic pluripotent stem cells. One unsuccessful attempt was made to reestablish the immune system in immunocompromised mice using these cells. Since last summer, several unsuccessful attempts were made to duplicate these results. It was determined by electron microscopy that the cells successfully grown in 1989 contained virus particles. It was suggested that these virally parasitized cells had been immortalized. The work of this summer is a continuation of efforts to grow mouse bone marrow in these vessels. A number of variations of the protocol were introduced. Certified pathogen free mice were used in the repeat experiments. In some attempts the medium of last summer was used; in others Dexture Culture Medium containing Iscove's Medium supplemented with 20 percent horse serum and 10-6 M hydrocortisone. Efforts this summer were directed solely to repeating the work of last summer. Plans were made for investigations if stem cells were isolated. Immortalization of the undifferentiated stem cell would be attempted by transfection with an oncogenic vector. Selective differentiation would be induced in the stem cell line by growing it with known growth factors and immune response modulators. Interest is in identifying any surface antigens unique to stem cells that would help in their characterization. Another goal was to search for markers on stem cells that would distinguish them from stem cells committed to a particular lineage. If the undifferentiated hematopoietic stem cell was obtained, the pathways that would terminally convert it to myeloid, lyphoid, erythroid, or other cell lines would be studied. Transfection with a known gene would be attempted and then conversion to a terminally identifiable cell.
Sorghum bicolor (L.) Moench is the fifth most commonly grown cereal crop worldwide with unrivaled drought tolerance compared with other cereal crops. Drought and heat tolerance and high biomass yield potential make sorghum a promising bioenergy crop. However, stem lodging is a significant problem that results in substantial yield losses. Stem biomechanical traits influence the mechanical stability of crops and breeding for desirable biomechanical traits may result in improved lodging resistance. In this study, we report the identification of quantitative trait loci (QTL) for stem mechanical and morphological traits in three recombinant inbred line (RIL; populations from Tx623/Rio, Tx623/Della, and Tx631/Della) crosses between elite grain and sweet sorghum parents. The genetic architecture of stem biomechanical traits in the three RIL populations is multigenic and pleiotropic. Additionally, eight QTL affecting mechanical and morphological traits were detected; two main effects of these QTL were consistently found in all populations and colocated with previously identified dwarfing genes Dw1 and Dw3. These results indicate that dwarfing genes affect the mechanical properties of sorghum stems and their lodging resistance, while also having demonstrable effects on stem morphology. The identification of these QTL provides new opportunities for improving stem lodging resistance via genomics-assisted breeding.
Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM’s high affinity with various analytical options into account. As is well known, STEM’s image acquisition time needs to be further shortened to capture a targeted phenomenon in real-time as STEM’s current temporal resolution is far below the conventional TEM’s. However, rapid image acquisition in the millisecond per frame or faster generally causes image distortion, poor electron signals, and unidirectional blurring, which are obstacles for realizing video-rate STEM observation. Here we show an image correction framework integrating deep learning (DL)-based denoising and image distortion correction schemes optimized for STEM rapid image acquisition. By comparing a series of distortion corrected rapid scan images with corresponding regular scan speed images, the trained DL network is shown to remove not only the statistical noise but also the unidirectional blurring. This result demonstrates that rapid as well as high-quality image acquisition by STEM without hardware modification can be established by the DL. The DL-based noise filter could be applied to in-situ observation, such as dislocation activities under external stimuli, with high spatio-temporal resolution.
Chimeric hemagglutinins (cHA) appear to be promising for the design and development of universal influenza vaccines. Influenza A group 1 cHAs, cH5/1, cH8/1, and cH11/1, comprising an H1 stem attached to either an H5, H8, or H11 globular head, have been used sequentially as vaccine immunogens in human clinical trials and induced high levels of broadly protective antibodies. Using X-ray crystallography and negative-stain electron microscopy, we determined structures of cH5/1, cH8/1, and cH11/1 HAs in their apo (unliganded) and antibody Fab-bound states. Stem-reactive antibodies 3E1 and 31.b.09 recognize their cognate epitopes in cH5/1, cH8/1, and cH11/1 HAs. However, with cH5/1, the head domains are rotated by 35 to 45° around the threefold axis of the HA trimer compared to native HA with a more splayed-open conformation at the stem base. cH11/1 with 3E1 is structurally more native-like but resembles cH5/1 with 31.b.09, whereas cH8/1 with 31.b.09 exhibited a range of closed-to-open stem configurations with some separation of head and stem domains. Furthermore, all of these group 1 cHAs effectively bound a broad head trimer interface antibody and other broad stem antibodies. Thus, the cHAs exhibit structural plasticity without compromising the stem and head trimer interface epitopes for elicitation of influenza A group 1 cross-reactive antibodies.
Scanning transmission electron microscopy (STEM) is one of the most powerful characterization tools in materials science research. Due to instrumentation developments such as highly coherent electron sources, aberration correctors, and direct electron detectors, STEM experiments can examine the structure and properties of materials at length scales of functional devices and materials down to single atoms. STEM encompasses a wide array of flexible operating modes, including imaging, diffraction, spectroscopy, and 3D tomography experiments. This review outlines many common STEM experimental methods with a focus on quantitative data analysis and simulation methods, especially those enabled by open source software. The hope is to introduce both classic and new experimental methods to materials scientists and summarize recent progress in STEM characterization. The review also discusses the strengths and weaknesses of the various STEM methodologies and briefly considers promising future directions for quantitative STEM research.
This dataset contains biomass yield measurements and associated vegetation index data collected from commercial Miscanthus × giganteus fields in eastern Iowa during the 2022–2023 growing seasons. The data support the analyses presented in the article: “Yield From Iowa's First Commercial Miscanthus Fields: Implications of Spatial Variability for Productivity and Sustainability Beyond Research Plots.” We collected 105 ground-truth biomass samples from four mature commercial fields (>4 years old) covering 92.81 ha. Samples were taken from 3 m² quadrats that were hand-harvested in alignment with commercial harvest timing. Stem biomass (excluding leaves) was weighed, moisture-corrected, and converted to dry-matter yield expressed in Mg DM ha⁻¹. Sampling locations were selected to capture spatial variability visible in aerial imagery and were recorded using RTK GPS. Each biomass observation was paired with vegetation indices derived from high-resolution PlanetScope satellite imagery (3 m resolution). Images were acquired throughout the growing season, and indices were calculated to evaluate their ability to predict end-of-season biomass yield. Statistical and machine learning approaches were used to identify key predictors, and a linear regression model based on end-of-July Green Normalized Difference Vegetation Index (GNDVI) was developed and evaluated. This repository includes the data used in that modeling workflow. Management practices, economic data, full imagery time series, and additional methodological details are described in the associated publication and are not included here. The dataset consists of three comma-separated value (CSV) files: 1. Combine_Groundtruth_Yield_VI_22_23.csv This file contains ground-truth biomass yield measurements and associated key vegetation index values collected during the 2022 and 2023 growing seasons. Rows: 105 observations Columns: Year — Year of observation (2022 or 2023) Field — Field location identifier Sample_number — Unique sample identifier GNDVI_End_Jul — Green Normalized Difference Vegetation Index calculated at end of July GNDVI_End_Aug — Green Normalized Difference Vegetation Index calculated at end of August NDRE_End_Aug — Normalized Difference Red Edge index calculated at end of August Biomass_Stem_Yield_MgDM/ha — Measured stem biomass yield (megagrams dry matter per hectare) 2. trainData_GNDVI.csv This file contains the subset of observations used to train the predictive relationship between July GNDVI and biomass yield. Rows: 76 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Stem_Yield_MgDM/ha — Observed stem biomass yield (Mg DM ha⁻¹) 3. testData_GNDVI.csv This file contains the test dataset used to evaluate model performance. Rows: 29 observations Columns: Unnamed: 0 — Row index retained from the original data processing workflow GNDVI_End_Jul — GNDVI at end of July Predicted_Yield_MgDM/ha — Model-predicted stem biomass yield (Mg DM ha⁻¹) Observed_Yield_MgDM/ha — Measured stem biomass yield (Mg DM ha⁻¹)