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Introduction With increasing global unrest and military physician shortages potentially leading to a surgeon draft, we sought to evaluate the readiness of graduating general surgery residents to care for casualties of war. Materials and Methods We evaluated the National Data Reports of Surgery Case Logs for general surgery residents from 2009 to 2018 to quantify experience with key procedures that provide critical skills required for wartime surgery. Reported cases from the Accreditation Council for Graduate Medical Education for graduating residents from civilian and military residency programs were analyzed for 28 individual procedures determined to be critical for the care of combat casualties. These included central and peripheral vascular procedures, as well as neck, thoracic, abdominal, and peripheral interventions. Results From 2009 to 2018, there has been a significant decrease in wartime-relevant cases by graduating residents. Notably, these include aorto-iliac/femoral bypasses (50% reduction; 7.1%/year; P Conclusions Graduating general surgery residents has limited exposure to wartime critical skills due to a significant reduction in open vascular, head and neck, thoracic, and operative trauma cases. As the threat of global war persists and new graduates continue to deploy worldwide, residency training must be augmented to ensure adequate preparation in case a surgeon draft is required to fulfill demand for military surgeons.

Recombinant engineering for protein production commonly employs plasmid-based gene templates for introduction and expression of genes in a candidate cell system in vitro. Challenges to this approach include identifying cell types that can facilitate proper post-translational modifications and difficulty expressing large multimeric proteins. We hypothesized that integration of the CRISPR/Cas9-synergistic activator mediator (SAM) system into the human genome would be a powerful tool capable of robust gene expression and protein production. SAMs are comprised of a “dead” Cas9 (dCas9) linked to transcriptional activators viral particle 64 (VP64), nuclear factor-kappa-B p65 subunit (p65), and heat shock factor 1 (HSF1) and are programmable to single or multiple gene targets. We integrated the components of the SAM system into human HEK293, HKB11, SK-HEP1, and HEP-g2 cells using coagulation factor X (FX) and fibrinogen (FBN) as proof of concept. We observed upregulation of mRNA in each cell type with concomitant protein expression. Our findings demonstrate the capability of human cells stably expressing SAM for user-defined singleplex and multiplex gene targeting and highlight their broad potential utility for recombinant engineering as well as transcriptional modulation across networks for basic, translational, and clinical modeling and applications.

Wastewater-based epidemiology during the COVID-19 pandemic has proven useful for public health decision-making but is often hampered by sampling methodology constraints, particularly at the building- or neighborhood-level. Time-weighted composite samples are commonly used; however, autosamplers are expensive and can be affected by intermittent flows in sub-sewershed contexts. In this study, we compared time-weighted composite, grab, and passive sampling via Moore swabs, at four locations across a college campus to understand the utility of passive sampling. After optimizing the methods for sample handling and processing for viral RNA extraction, we quantified SARS-CoV-2 N1 and N2, as well as a fecal strength indicator, PMMoV, by ddRT-PCR and applied tiled amplicon sequencing of the SARS-CoV-2 genome. Passive samples compared favorably with composite samples in our study area: for samples collected concurrently, 42 % of the samples agreed between Moore swab and composite samples and 58 % of the samples were positive for SARS-CoV-2 using Moore swabs while composite samples were below the limit of detection. Variant profiles from Moore swabs showed a shift from variant BA.1 to BA.2, consistent with in-person saliva samples. These data have implications for the broader implementation of sewage surveillance without advanced sampling technologies and for the utilization of passive sampling approaches for other emerging pathogens.

Current hemostatic agents are obtained from pooled plasma from multiple donors requiring costly pathogen screening and processing. Recombinant DNA-based production represents an engineering solution that could improve supply, uniformity, and safety. Current approaches are typically for single gene candidate peptides and often employ non-human cells. We devised an approach where multiple gene products could be produced from a single population of cells. We identified gene specific Synergistic Activation Mediators (SAM) from the CRISPR/Cas9 system for targeted overexpression of coagulation factors II, VII, IX, X, and fibrinogen. The components of the CRISPR-SAM system were expressed in Human Embryonic Kidney Cells (HEK293), and single (singleplex) or multi-gene (multiplex) upregulation was assessed by quantitative RT-PCR (qRT-PCR) and protein expression by ELISA analysis. Factor II, VII, IX, and X singleplex and multiplex activation resulted in 120–4700-fold and 60–680-fold increases in gene expression, respectively. Fibrinogen sub-unit gene activation resulted in a 1700–92,000-fold increases and 80–5500-fold increases in singleplex or multiplex approaches, respectively. ELISA analysis showed a concomitant upregulation of candidate gene products. Our findings demonstrate the capability of CRISPR/Cas9 SAMs for single or multi-agent production in human cells and represent an engineering advance that augments current recombinant peptide production techniques.

Tri-structural isotropic (TRISO) fuel particles are a key component of next generation nuclear fuels. Using x-ray computed tomography (CT) to characterize TRISO particles is challenging because of the strong attenuation of the x-ray beam by the uranium core, leading to severe photon starvation in a substantial fraction of the measurements. Furthermore, the overall acquisition time for a high-resolution CT scan can be very long when using conventional laboratory-based x-ray systems and reconstruction algorithms. Specifically, when analytic methods such as the Feldkamp–Davis–Kress (FDK) algorithm are used for reconstruction, it results in severe streak artifacts and noise in the corresponding 3D volume, which makes subsequent analysis of the particles challenging. In this paper, we develop and apply model-based image reconstruction (MBIR) algorithms to improve the quality of CT reconstructions for TRISO particles to facilitate better characterization. We demonstrate that the proposed MBIR algorithms can significantly suppress artifacts with minimal pre-processing compared to conventional approaches. We also demonstrate that the proposed MBIR approach can obtain high-quality reconstruction compared to the FDK approach even when using a fraction of the typically acquired measurements, thereby enabling dramatically faster measurement times for TRISO particles.

Long femoral nails for neck of femur fractures and prophylactic fixation have a risk of anterior cortex perforation. Previous studies have demonstrated the radius of curvature (ROC) of a femoral nail influencing the finishing point of a nail and the risk of anterior cortex perforation. This study aims to calculate a patients femoral ROC using preoperative XR and CT and therefore nail finishing position.We conducted a retrospective study review of patients with long femoral cephalomedullary nailing for proximal femur fractures (OTA/AO 31(A) and OTA/AO 32) or impending pathological fractures at a level 1 trauma centre between January 1, 2015 and December 31, 2020 with both full length lateral X-ray and CT imaging. Femoral ROC was calculated on both imaging modalities. Outcomes measured including nail finishing position, anterior cortex encroachment and impingement. The mean femoral ROC was 1026mm on CT and 1244mm on XR. CT femoral ROC strongly correlated with nail finishing point with a spearmans coefficient of 0.77. Additionally, femurs with a ROC To our knowledge this is the first study to compare a measured femoral ROC to nail finishing position. The use of CT to measure femoral ROC and to a lesser extent XR was able to predict both nail finishing position and risk of anterior cortex encroachment. Preoperative XRs and CTs were able to identify patients with a small femoral ROC. This predicted patients at risk of anterior cortex impingement, anterior cortex encroachment and nail finishing position. We may be able to select femoral nails that resemble the native femoral ROC and mitigate the risk of anterior cortex perforation.

Following the 3.15 MJ fusion milestone at the National Ignition Facility, the further development of inertial confinement fusion, both as a source for future electricity generation and for high-energy-density physics applications, requires the development of more robust ignition concepts at current laser facility energy scales. This can potentially be achieved by auxiliary heating the hotspot of low convergence wetted foam implosions where hydrodynamic and parametric instabilities are minimized. This paper presents the first multi-dimensional Vlasov–Maxwell and particle-in-cell simulations to model this collisionless interaction, only recently made possible by access to the largest modern supercomputers. The key parameter of interest is the maximum fraction of energy that can be extracted from the electron beams into the hotspot plasma. The simulations indicate that significant coupling efficiencies are achieved over a wide range of beam parameters and spatial configurations. The implications for experimental tests on the National Ignition Facility are discussed.

Characterization of Unintended Conducted Emissions (UCE) from electronic devices is important when diagnosing electromagnetic interference, performing nonintrusive load monitoring (NILM) of power systems, and monitoring electronic device health, among other applications. Prior work has demonstrated that UCE analysis can serve as a diagnostic tool for energy efficiency investigations and detailed load analysis. While explaining the feature selection of deep networks with certainty is often not fully comprehensive, or in other applications, quite lacking, additional tools/methods for further corroboration and confirmation can help further the understanding of the researcher. This is true especially in the subject application of the study in this paper. Often the focus of such efforts is the selected features themselves, and there is not as much understanding gained about the noise in the collected data. If selected feature and noise characteristics are known, it can be used to further shape the design of the deep network or associated preprocessing. This is additionally difficult when the available data are limited, as in the case which the authors investigated in this study. Here, the authors present a novel work (which is a proposed complementary portion of the overall solution to the deep network classification explainability problem for this application) by applying a systematic progression of preprocessing and a deep neural network (ResNet architecture) to classify UCE data obtained via current transformers. By using a methodical application of preprocessing techniques prior to a deep classifier, hypotheses can be produced concerning what features the deep network deems important relative to what it perceives as noise. For instance, it is hypothesized in this particular study as a result of execution of the proposed method and periodic inspection of the classifier output that the UCE spectral features are relatively close to each other or to the interferers, as systematically reducing the beta parameter of the Kaiser window produced progressively better classification performance, but only to a point, as going below the Beta of eight produced decreased classifier performance, as well as the hypothesis that further spectral feature resolution was not as important to the classifier as rejection of the leakage from a spectrally distant interference. This can be very important in unpredictable low-FNR applications, where knowing the difference between features and noise is difficult. As a side-benefit, much was learned regarding the best preprocessing to use with the selected deep network for the UCE collected from these low power consumer devices obtained via current transformers. Baseline rectangular windowed FFT preprocessing provided a 62% classification increase versus using raw samples. After performing a more optimal preprocessing, more than 90% classification accuracy was achieved across 18 low-power consumer devices for scenarios in which the in-band features-to-noise ratio (FNR) was very poor.

Models of fluid flow are used to improve the efficiency of oil and gas extraction and to estimate the storage and leakage of carbon dioxide in geologic reservoirs. Therefore, a quantitative understanding of key parameters of rock–fluid interactions, such as contact angles, wetting, and the rate of spontaneous imbibition, is necessary if these models are to predict reservoir behavior accurately. In this study, aqueous fluid imbibition rates were measured in fractures in samples of the Eagle Ford Shale using neutron imaging. Several liquids, including pure water and aqueous solutions containing sodium bicarbonate and sodium chloride, were used to determine the impact of solution chemistry on uptake rates. Uptake rate analysis provided dynamic contact angles for the Eagle Ford Shale that ranged from 51 to 90° using the Schwiebert–Leong equation, suggesting moderately hydrophilic mineralogy. When corrected for hydrostatic pressure, the average contact angle was calculated as 76 ± 7°, with higher values at the fracture inlet. Differences in imbibition arising from differing fracture widths, physical liquid properties, and wetting front height were investigated. For example, bicarbonate-contacted samples had average contact angles that varied between 62 ± 10° and ∼84 ± 6° as the fluid rose in the column, likely reflecting a convergence–divergence structure within the fracture. Secondary imbibitions into the same samples showed a much more rapid uptake for water and sodium chloride solutions that suggested alteration of the clay in contact with the solution producing a water-wet environment. The same effect was not observed for sodium bicarbonate, which suggested that the bicarbonate ion prevented shale hydration. This study demonstrates how the imbibition rate measured by neutron imaging can be used to determine contact angles for solutions in contact with shale or other materials and that wetting properties can vary on a relatively fine scale during imbibition, requiring detailed descriptions of wetting for accurate reservoir modeling.

The application Sinpol, a novel tool for neutron transmission data analysis, is used to simulate the effect of deformation and recrystallization crystallographic textures in fcc materials. The texture is characterized using an orientation distribution function (ODF), which describes the distribution of crystal orientations over the three-dimensional orientation space. The Sinpol code combines the transmission contribution of each single crystal grain in the ODF to generate neutron transmission patterns through a polycrystalline aggregate. A series of texture components, commonly found in fcc materials, are considered and their neutron transmission calculated for further analysis of experimental data. The effect of grain statistics is investigated and the simulation results on more complex ODF textures are also presented. Finally, the neutron transmission simulations are successfully benchmarked on experimental data recorded on an additive manufactured Inconel 718 specimen.

Wavelength-resolved neutron tomography (WRNT) is an emerging technique for characterizing samples relevant to the materials sciences in 3D. WRNT studies can be carried out at beam lines in spallation neutron or reactor-based user facilities. Because of the limited availability of experimental time, potential imperfections in the neutron source, or constraints placed on the acquisition time by the type of sample, the data can be extremely noisy resulting in tomographic reconstructions with significant artifacts when standard reconstruction algorithms are used. Furthermore, making a full tomographic measurement even with a low signal-to-noise ratio can take several days, resulting in a long wait time before the user can receive feedback from the experiment when traditional acquisition protocols are used. In this paper, we propose an interlaced scanning technique and combine it with a model-based image reconstruction algorithm to produce high-quality WRNT reconstructions concurrent with the measurements being made. The interlaced scan is designed to acquire data so that successive measurements are more diverse in contrast to typical sequential scanning protocols. The model-based reconstruction algorithm combines a data-fidelity term with a regularization term to formulate the wavelength-resolved reconstruction as minimizing a high-dimensional cost-function. Using an experimental dataset of a magnetite sample acquired over a span of about two days, we demonstrate that our technique can produce high-quality reconstructions even during the experiment compared to traditional acquisition and reconstruction techniques. In summary, the combination of the proposed acquisition strategy with an advanced reconstruction algorithm provides a novel guideline for designing WRNT systems at user facilities.

The collision of two real photons can result in the emission of axions. We investigate the performance of a modified light-shining-through-wall (LSW) axion search aiming to overcome the large signal suppression for axion masses $m_a\geq 1 \text{eV}$. We propose to utilise a third beam to stimulate the reconversion of axions into a measurable signal. We thereby find that with currently available high-power laser facilities we expect bounds at axion masses between $0.5-6\text{eV}$ reaching $g_{a\gamma\gamma}\geq 10^{-7}\text{GeV}^{-1}$. Combining the use of optical lasers with currently operating x-ray free electron lasers, we extend the mass range to $10-100\text{eV}$., Comment: 8 pages, 2 figures

Nondestructive evaluation (NDE) of additively manufactured (AM) parts is important for understanding the impacts of various process parameters and qualifying the built part. X-ray computed tomography (XCT) has played a critical role in rapid NDE and characterization of AM parts. However, XCT of metal AM parts can be challenging because of artifacts produced by standard reconstruction algorithms as a result of a confounding effect called “beam hardening.” Beam hardening artifacts complicate the analysis of XCT images and adversely impact the process of detecting defects, such as pores and cracks, which is key to ensuring the quality of the parts being printed. In this work, we propose a novel framework based on using available computer-aided design (CAD) models for parts to be manufactured, accurate XCT simulations, and a deep-neural network to produce high-quality XCT reconstructions from data that are affected by noise and beam hardening. Using extensive experiments with simulated data sets, we demonstrate that our method can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art. We also present promising preliminary results of applying the deep networks trained using CAD models to experimental data obtained from XCT of an AM jet-engine turbine blade.