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At least 757 records · Page 42

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DOE OSTI2020

A Free‐Radical Prompted Barrierless Gas‐Phase Synthesis of Pentacene

Abstract A representative, low‐temperature gas‐phase reaction mechanism synthesizing polyacenes via ring annulation exemplified by the formation of pentacene (C 22 H 14 ) along with its benzo[ a ]tetracene isomer (C 22 H 14 ) is unraveled by probing the elementary reaction of the 2‐tetracenyl radical (C 18 H 11 . ) with vinylacetylene (C 4 H 4 ). The pathway to pentacene—a prototype polyacene and a fundamental molecular building block in graphenes, fullerenes, and carbon nanotubes—is facilitated by a barrierless, vinylacetylene mediated gas‐phase process thus disputing conventional hypotheses that synthesis of polycyclic aromatic hydrocarbons (PAHs) solely proceeds at elevated temperatures. This low‐temperature pathway can launch isomer‐selective routes to aromatic structures through submerged reaction barriers, resonantly stabilized free‐radical intermediates, and methodical ring annulation in deep space eventually changing our perception about the chemistry of carbon in our universe.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

A Free-Radical Prompted Barrierless Gas-Phase Synthesis of Pentacene

A representative, low-temperature gas-phase reaction mechanism synthesizing polyacenes via ring annulation exemplified by the formation of pentacene (C 22 H 14 ) along with its benzo[ a ]tetracene isomer (C 22 H 14 ) is unraveled by probing the elementary reaction of the 2-tetracenyl radical (C 18 H 11 . ) with vinylacetylene (C 4 H 4 ). The pathway to pentacene—a prototype polyacene and a fundamental molecular building block in graphenes, fullerenes, and carbon nanotubes—is facilitated by a barrierless, vinylacetylene mediated gas-phase process thus disputing conventional hypotheses that synthesis of polycyclic aromatic hydrocarbons (PAHs) solely proceeds at elevated temperatures. This low-temperature pathway can launch isomer-selective routes to aromatic structures through submerged reaction barriers, resonantly stabilized free-radical intermediates, and methodical ring annulation in deep space eventually changing our perception about the chemistry of carbon in our universe.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Connecting research and teaching introductory cell and molecular biology using an Arabidopsis mutant screen

A complex research project was translated into a Course-based Undergraduate Research Experience (CURE), which was implemented in sections of an introductory Cell and Molecular Biology laboratory course. The research laboratory generated an engineered plant line producing a growth-inhibiting, lipid-derived plant hormone and mutagenized this line. Students in the CURE cultured the mutagenized plant population and selected and characterized suppressor mutants. They learned to observe phenotypes related to the biosynthesis and perception of the plant hormone and explored the genetic and biochemical basis of these phenotypes. As the students studied the relevant genetic, molecular and biochemical concepts during this CURE, they were able to translate this knowledge into practice and develop scientific arguments. This CURE was a successful collaboration between the teaching lab and the research lab. It benefited both parties as the students had a real-life, deep learning experience in scientific methodology, while the research lab gathered data and materials for further studies

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A novel methodology to integrate outcomes regarding perioperative pain experience into a composite score: Prediction model development and validation

Abstract Background An integrated score that globally assesses perioperative pain experience and rationally weights each component has not yet been developed. Methods A development dataset specific to adult Chinese patients undergoing orthopaedic surgery was obtained from PAIN OUT (1985 qualified patients of 2244). A more recent validation dataset obeying the same conditions was obtained from the Chinese Anaesthesia Shared‐database Platform (1004 qualified patients of 1032). Outcomes were assessed using the International Pain Outcomes Questionnaire (IPO‐Q), which comprises key patient‐level outcomes of perioperative pain management, including pain experience and perceptions of care. Using principal component analysis and regression models, a composite score (CS) was inferred to integrate pain experience. The discrimination of the CS for dissatisfaction and desire for more pain treatment was compared with that of the worst pain score. Results A CS was developed from the 12 items of the IPO‐Q regarding pain experience. The weight for calculating the CS was worst pain 11, least pain 17, time spent in severe pain 11, interference with activity in bed 9, interference with breathing deeply or coughing 10, interference with sleep 9, anxiety 12, helplessness 12, nausea 0, drowsiness 2, itch 5 and dizziness 2. In external validation, the CS indicated superior discrimination to the worst pain in predicting dissatisfaction ( p < 0.001) and desire for more pain treatment ( p < 0.001). Conclusions This study introduced a methodology to integrate outcomes regarding perioperative pain experience into a CS, which was based on the weight of each item. Significance This novel methodology sheds additional light on the riveting issue of carefully integrating several measures into a composite endpoint, which may be useful for quality improvement purposes when addressing the impact of a change in clinical practice.

Jiang, Bailin↗

DOE OSTI2021

Perceived Conflicts of Systems Engineering in Early-Stage Research and Development 1

Early Systems Research and Development (ESR&D) is one of the most crucial phases in the product development process. It both blends and blurs the lines between science and engineering, and requires a risk-based, disciplined, and graded approach to effectively manage scope, cost, and complexity of the final product. Many leaders, program managers, and scientists are unwilling to involve systems engineering because of the perception that systems engineering is heavily process oriented, adds unnecessary costs, and should be applied only to mature technologies. The value of systems engineering as applied to ESR&D is unclear to these key individuals. The unfortunate result is that system engineering is not applied to ESR&D. This results in R&D efforts that may have solved the wrong problem, selected the wrong architecture, require technical rework, have difficulty transitioning later maturity levels, and result in higher R&D costs and extended development timelines. This work discusses the difficulty of introducing systems engineering to the research and early development process and their inclination perspectives of researchers, engineers, and managers. The article shall offer potential means to manage the cultural transformation of early adoption of right-sized systems engineering in ESR&D and reverse the attitudinal positions.

42 ENGINEERING↗

DOE OSTI2019

Automatic detection of small bowel tumors in wireless capsule endoscopy images using ensemble learning

Purpose Wireless Capsule Endoscopy (WCE) is a minimally invasive diagnosis tool for lesion detection in the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the significant amount of acquired data leads to difficulties in the diagnosis by the physicians; which can be eased with computer assistance. This paper addresses a method for the automatic detection of tumors in WCE by using a two‐step based procedure: region of interest selection and classification. Methods The first step aims to separate abnormal from normal tissue by using automatic segmentation based on a Gaussian Mixture Model (GMM). A modified version of the Anderson method for convergence acceleration of the expectation–maximization (EM) algorithm is proposed. The proposed features for both segmentation and classification are based on the CIELab color space, as a way of bypassing lightness variations, where the L component is discarded. Tissue variability among subjects, light inhomogeneities and even intensity differences among different devices can be overcome by using simultaneously features from both regions. In the second step, an ensemble system with partition of the training data with a new training scheme is proposed. At this stage, the gating network is trained after the experts have been trained decoupling the joint maximization of both modules. The partition module is also used at the test step, leading the incoming data to the most likely expert allowing incremental adaptation by preserving data diversity. Results This algorithm outperforms others based on texture features selected from Wavelets and Curvelets transforms, classified by a regular support vector machine (SVM) in more than 5%. Conclusions This work shows that simpler features can outperform more elaborate ones if appropriately designed. In the current case, luminance was discarded to cope with saturated tissue, facilitating the color perception. Ensemble systems remain an open research field. In the current case, changes in both topology and training strategy have led to significant performance improvements. A system with this level of performance can be used in current clinical practice.

Vieira, Pedro M.↗

DOE OSTI2019

Simulation of discrete cracks driven by nearly incompressible fluid via 2D combined finite-discrete element method

Here, we propose a novel hydraulic solver in order to simulate key mechanisms that control fluid-driven cracks in the framework of the combined finite-discrete element method (FDEM). The main innovative aspect of the present work is the independence of the fluid's critical time step size with the fracture opening. This advantage is extremely important because it means that very fine meshes can be used around areas of interest, such as boreholes, without penalizing the computational cost as fractures propagate (ie, open) and the fluid flows through them. This is a great advantage over other recently introduced approaches that exhibit a dependency of the time step in the form of Δt crit ∝ (l/a) 2 where l is the element size and a is the fracture aperture. This paper presents a series of benchmark cases for the proposed solver. The rationale adopted by the authors was to benchmark and validate the implementation of the hydraulic solver in an incremental fashion, starting from the simplest cases and building in complexity. The calculations shown in this work clearly demonstrate that the proposed approach is able to reproduce analytical results for fluid flow through a single crack. The results presented in this paper also demonstrate that the new approach is robust enough to deal with complex fracture patterns and complex geometries; the obtained fluid-driven fracture patterns in the vicinity of a borehole certainly stand to the scrutiny of human visual perception.

58 GEOSCIENCES↗

DOE OSTI2019

Flowering plant immune repertoires expand under mycorrhizal symbiosis

Immune perception in flowering plants is mediated by a repertoire of cytoplasmic and cell-surface receptors that detect invading microbes and their effects on cells. Here, we show that several large families of immune receptors exhibit size variations related to a plant's competence to host symbiotic root fungi (mycorrhiza). Plants that do not participate in mycorrhizal associations have significantly smaller immune repertoires, while the most promiscuous symbiotic hosts (ectomycorrhizal plant species) have significantly larger immune repertoires. By contrast, we find no significant increase in immune repertoire size among legumes competent to form a symbiosis with nitrogen-fixing bacteria (rhizobia). To explain these observations, we hypothesize that plant immune repertoire size expands with symbiote species diversity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Temporal dynamics of protein and post-translational modification abundances in Populus leaf across a diurnal period

Populus spp. are dedicated woody biomass feedstocks for advanced biofuels and bioproducts. Proper growth and fitness of poplar as a sustainable feedstock depends on timely perception and response to environmental signals (e.g., light, temperature, water). Poplar leaves, like other C3 photosynthesis plants, have evolved oscillating or circadian rhythms that play important roles in synchronizing biological processes with external cues. To characterize this phenomenon at a molecular level, we employed bottom-up proteomics using high-resolution mass spectrometry and de novo-assisted database searching to identify abundance changes in proteins and post-translational modifications in poplar leaf tissue sampled across a 12/12-hour light/dark diurnal period.

09 BIOMASS FUELS↗

DOE OSTI2023

Classifying metal‐binding sites with neural networks

Abstract To advance our ability to predict impacts of the protein scaffold on catalysis, robust classification schemes to define features of proteins that will influence reactivity are needed. One of these features is a protein's metal‐binding ability, as metals are critical to catalytic conversion by metalloenzymes. As a step toward realizing this goal, we used convolutional neural networks (CNNs) to enable the classification of a metal cofactor binding pocket within a protein scaffold. CNNs enable images to be classified based on multiple levels of detail in the image, from edges and corners to entire objects, and can provide rapid classification. First, six CNN models were fine‐tuned to classify the 20 standard amino acids to choose a performant model for amino acid classification. This model was then trained in two parallel efforts: to classify a 2D image of the environment within a given radius of the central metal binding site, either an Fe ion or a [2Fe‐2S] cofactor, with the metal visible (effort 1) or the metal hidden (effort 2). We further used two sub‐classifications of the [2Fe‐2S] cofactor: (1) a standard [2Fe‐2S] cofactor and (2) a Rieske [2Fe‐2S] cofactor. The accuracy for the model correctly identifying all three defined features was >95%, despite our perception of the increased challenge of the metalloenzyme identification. This demonstrates that machine learning methodology to classify and distinguish similar metal‐binding sites, even in the absence of a visible cofactor, is indeed possible and offers an additional tool for metal‐binding site identification in proteins.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

“I Would Purposely Try to Keep them Separated”: Language Ideologies, Language Policy, and Beliefs about Emergent Bilinguals in Career and Technical Education

Abstract In the policy context of college and career readiness, career and technical education (CTE) is growing in popularity, especially for students who are underserved in academic high school programs. While CTE has shown promise for some students, very little research exists on its potential for emergent bilingual students (EBs). Thus, building on research of deficit perceptions of EBs and language ideologies (LIs), this study examines how LIs and deficit perspectives are reproduced in CTE contexts across Pennsylvania. Using a sequential mixed‐methods design, we found that, while CTE educators held generally positive attitudes toward EBs, they were influenced by monoglossic language ideology and standard language ideology. These LIs, along with other institutional factors, led educators to implement restrictive language policies within their classrooms. These implications serve to inform how, with a lack of clear language policy within CTE programs, teachers relied on their personal beliefs and reproduced deficit ideologies in the creation of classroom‐level policies.

Emerick, Mark R.↗

DOE OSTI2018

Improving Automation Transparency: Addressing Some of Machine Learning’s Unique Challenges

Abstract. A variety of factors can affect one’s reliance on an automated aid. Some of these factors include one’s per-ception of the system’s trustworthiness, such as perceived reliability of the system or one’s ability to understand the system’s underlying reasoning. A mismatch between the operator’s perception and the true capabilities and charac-teristics of the system can lead to inappropriate reliance on the tool. This improper use of the system can manifest as either underutilization of the technology or complacency resulting from over-trusting the system. Increasing an au-tomated tool’s transparency is one approach that enables the operator to more appropriately rely on the technology. Transparent automated systems provide additional infor-mation that allows the user to see the system’s intent and understand its underlying processes and capabilities. Sev-eral researchers have developed frameworks to support the design of more transparent automation. However, these frameworks may not fully consider the particular challeng-es to transparency design introduced by automation that leverages machine learning. Like all automation, these systems can benefit from transparency. However, artificial intelligence poses new challenges that must be considered when designing for transparency. Unique considerations must be made in terms of the type, and amount or level of transparency information conveyed to the user.

Transparency, Machine Learning, Automation↗

DOE OSTI2020

The Earth has humans, so why don’t our climate models?

While climate models have rapidly advanced in sophistication over recent decades, they lack dynamic representation of human behavior and social systems despite strong feedbacks between social processes and climate. The impacts of climate change alter perceptions of risk and emissions behavior that, in turn, influence the rate and magnitude of climate change. Addressing this deficiency in climate models requires a substantial interdisciplinary effort to couple models of climate and human behavior. We suggest a multi-model approach that considers a range of theories and implementations of human behavior and social systems, similar to the multi-model approach that has been used to explore the physical climate system. In this work, we describe the importance of linking social factors with climate processes and identify four priorities essential to advancing the development of coupled social-climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Signature of Ferromagnetic Phase at Low Temperature in Low-Doped La{sub 0.88}Ca{sub 0.12}MnO{sub 3} Single Crystal

The confirmation of ferromagnetism at low temperature in a low-doped manganites system is always debatable. It is for the first time, electrical, magnetic, and thermal properties have been measured for manganites single crystal of La{sub 0.88}Ca{sub 0.12}MnO{sub 3} by using a floating-zone method. The electrical resistivity shows a perceptible change of resistance at a low temperature which can be viewed as a signature of the ferromagnetic phase. The ferromagnetic phase of the investigated La{sub 0.88}Ca{sub 0.12}MnO{sub 3} single crystal with Curie temperature of T{sub C} = 120 K which is in closed agreement with ac-susceptibility measurement. An analysis of the specific heat suggests the transition peak (T{sub P}) coincides with the T{sub C} and attains 1.2 J mol{sup −1} K at 116 K. The magnetoresistance (MR) and temperature coefficient of resistance (TCR) were found to be 69% and 10% respectively, which expands its subtle potential for applications in spintronics and thermistor.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2019

Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives

This paper presents a systematic way of understanding and modeling traveler behavior in response to on-demand mobility services. We explicitly consider the sequential and yet inter-connected decision-making stages specific to on-demand service usage. The framework includes a hybrid choice model for service subscription, and three logit mixture models with inter-consumer heterogeneity for the service access, menu product choice and opt-out choice. Different models are connected by feeding logsums. The proposed modeling framework is essential for accounting the impacts of real-time on-demand system's dynamics on traveler behaviors and capturing consumer heterogeneity, thus being greatly relevant for integrations in multi-modal dynamic simulators. The methodology is applied to a case study of an innovative personalized on-demand real-time system which incentivizes travelers to select more sustainable travel options. The data for model estimation is collected through a smartphone-based context-aware stated preference survey. Through model estimation, lower values of time are observed when the respondents opt to use the reward system. The perception of incentives and schedule delay by different population segments are quantified. These results are fundamental in setting the ground for different behavioral scenarios of such a new on-demand system. Here, the proposed methodology is flexible to be applied to model other on-demand mobility services such as ride-hailing services and the emerging mobility as a service.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Public Health Policies on E-Cigarettes

Tobacco continues to kill about 0.48 million Americans per year and there are currently 34.3 million smokers in the USA. As a consequence of the First Surgeon General’s Report on Tobacco in 1964, tobacco control interventions on part of the government led to a significant decline in conventional tobacco product usage over the last few decades. However, more recently, a new entity in the form of electronic cigarettes has risen rapidly and has exposed a younger population to a plethora of dangerous consequences. Looking at e-cigarettes from the perspective of tobacco control however raises a lot of challenges. There is little doubt that existing smokers of combustible cigarettes who switch to e-cigarettes will be switching to a less harmful product. However, if the younger generation begins using e-cigarettes as a result of targeted marketing, appealing flavors and ‘safer alternative’ perception, decades of progress made in conventional tobacco control will be negated. Governments at the federal, state, and local levels have a mandate to once again implement new public health policies to ensure that non-conventional tobacco products like e-cigarettes are available as smoking cessation tools for existing smokers but at the same time do not play a role in ruining the health of future generations through addiction and disease. Purpose of Review: To review the present scenario of regulations and policies impacting public health with respect to electronic nicotine delivery systems (ENDS) with the objective of providing a meaningful and balanced view of the challenges at hand with plausible recommendations. Recent Findings: Nicotine in tobacco is known to cause addiction and dependence. It is particularly potent in children and young adults. E-cigarettes can deliver high concentrations of nicotine, and these concentrations can vary depending on the numerous constituents within the e-cigarette which vary greatly from one another. Use of e-cigarettes is implicated as a risk factor for future cigarette use in young adults. Moreover, e-cigarette usage patterns also depend on several sociodemographic factors. Banning tobacco products has shown to reduce smoking risk in youth and as such, strong e-cigarette regulation measures are needed for prevention. Summary: Effective regulation of ENDS faces a multitude of challenges. One such challenge is to prevent youth and non-smokers from getting habituated to nicotine through e-cigarettes. The intention of tobacco companies to sustain sales through harmful marketing strategies that tone down the risks and highlight e-cigarettes as a “much safer alternative” while promoting flavors appealing to children should be immediately prohibited. Another hazard is the endorsement of ENDS as devices meant for enhancing social interaction which opens a path for youth to make erroneous choices under peer pressure. On the other hand, several studies have reported that e-cigarettes significantly reduce an existing smoker’s risk of being exposed to toxic tobacco smoke constituents that are normally present in cigarette smoke. This leads to the conclusions that e-cigarettes can be a tool for smoking cessation for current smokers. Public policy must take a multi-dimensional approach to balance these two extremes.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Prospective assessment of taste impairment and nausea during radiotherapy for head and neck cancer

Dysgeusia and nausea are common side effects observed in head and neck cancer patients treated with either exclusive radiotherapy or combined modality treatment. The aim of the present study was to prospectively evaluate dysgeusia, during treatment and follow-up, using the chemotherapy-induced taste alteration scale (CiTAS), a metrics based on 18-items exploring three dimensions (quantitative and qualitative changes in taste perception, and diet-related issues) identified through a four-factor analysis: decline in basic taste, discomfort, phantogeusia–parageusia, and general taste alterations. Moreover, we scored, according to Common Toxicity Criteria Adverse Events, nausea and other treatment-related toxicities. Since, ginger is traditionally used to prevent and/or treat nausea and vomiting, we prophylactically employed a ginger-based supplement named Naumix/Naugin (Gamfarma, Milan, Italy), to potentially mitigate both nausea and taste impairment. Using the CiTAS scale, we highlighted a progressive increase in all dysgeusia dimensions, peaking at the VII week of treatment and a subsequent partial late recovery. In particular, we observed a recovery for discomfort, phantogeusia–parageusia, and general taste alterations at 6 months. Grade 2 nausea, observed to be as low as 12.9% potentially due to the use of ginger, peaked at the III week of treatment. Finally, for patients experiencing nausea, the dysgeusia dimension of discomfort was also relevant.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2021

Psychosocial drivers of land management behaviour: How threats, norms, and context influence deforestation intentions

Understanding how private landholders make deforestation decisions is of paramount importance for conservation. Behavioural frameworks from the social sciences have a lot to offer researchers and practitioners, yet these insights remain underutilised in describing what drives landholders' deforestation intentions under important political, social, and management contexts. Using survey data of private landholders in Queensland, Australia, we compare the ability of two popular behavioural models to predict future deforestation intentions, and propose a more integrated behavioural model of deforestation intentions. We found that the integrated model outperformed other models, revealing the importance of threat perceptions, attitudes, and social norms for predicting landholders' deforestation intentions. Social capital, policy uncertainty, and years of experience are important contextual moderators of these psychological factors. Here, we conclude with recommendations for promoting behaviour change in this deforestation hotspot and highlight how others can adopt similar approaches to illuminate more proximate drivers of environmental behaviours in other contexts.

54 ENVIRONMENTAL SCIENCES↗