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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Feasibility of Patient Reporting of Symptomatic Adverse Events via the Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) in a Chemoradiotherapy Cooperative Group Multicenter Clinical Trial

Purpose: To assess the feasibility of measuring symptomatic adverse events (AEs) in a multicenter clinical trial using the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). Methods and Materials: Patients enrolled in NRG Oncology's RTOG 1012 (Prophylactic Manuka Honey for Reduction of Chemoradiation Induced Esophagitis-Related Pain during Treatment of Lung Cancer) were asked to self-report 53 PRO-CTCAE items representing 30 symptomatic AEs at 6 time points (baseline; weekly ×4 during treatment; 12 weeks after treatment). Reporting was conducted via wireless tablet computers in clinic waiting areas. Compliance was defined as the proportion of visits when an expected PRO-CTCAE assessment was completed. Results: Among 226 study sites participating in RTOG 1012, 100% completed 35-minute PRO-CTCAE training for clinical research associates (CRAs); 80 sites enrolled patients, of which 34 (43%) required tablet computers to be provided. All 152 patients in RTOG 1012 agreed to self-report using the PRO-CTCAE (median age 66 years; 47% female; 84% white). Median time for CRAs to learn the system was 60 minutes (range, 30-240 minutes), and median time for CRAs to teach a patient to self-report was 10 minutes (range, 2-60 minutes). Compliance was high, particularly during active treatment, when patients self-reported at 86% of expected time points, although compliance was lower after treatment (72%). Common reasons for noncompliance were institutional errors, such as forgetting to provide computers to participants; patients missing clinic visits; Internet connectivity; and patients feeling “too sick.” Conclusions: Most patients enrolled in a multicenter chemoradiotherapy trial were willing and able to self-report symptomatic AEs at visits using tablet computers. Minimal effort was required by local site staff to support this system. The observed causes of missing data may be obviated by allowing patients to self-report electronically between visits, and by using central compliance monitoring. These approaches are being incorporated into ongoing studies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. Finally, while primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Machine learning strategies for systems with invariance properties

Here, in many scientific fields, empirical models are employed to facilitate computational simulations of engineering systems. For example, in fluid mechanics, empirical Reynolds stress closures enable computationally-efficient Reynolds-Averaged Navier-Stokes simulations. Likewise, in solid mechanics, constitutive relations between the stress and strain in a material are required in deformation analysis. Traditional methods for developing and tuning empirical models usually combine physical intuition with simple regression techniques on limited data sets. The rise of high-performance computing has led to a growing availability of high-fidelity simulation data, which open up the possibility of using machine learning algorithms, such as random forests or neural networks, to develop more accurate and general empirical models. A key question when using data-driven algorithms to develop these models is how domain knowledge should be incorporated into the machine learning process. This paper will specifically address physical systems that possess symmetry or invariance properties. Two different methods for teaching a machine learning model an invariance property are compared. In the first , a basis of invariant inputs is constructed, and the machine learning model is trained upon this basis, thereby embedding the invariance into the model. In the second method, the algorithm is trained on multiple transformations of the raw input data until the model learns invariance to that transformation. Results are discussed for two case studies: one in turbulence modeling and one in crystal elasticity. It is shown that in both cases embedding the invariance property into the input features yields higher performance with significantly reduced computational training costs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

PracticeCALM: Coaching Anxiety Lessening Methods for Radiation Therapists: A Pilot Study of a Skills-Based Training Program in Radiation Oncology

PracticeCALM is an 8-week on-the-job training program to teach radiation therapists (RTs) techniques for assisting patients who are experiencing treatment-related anxiety. Twelve clinical RTs in a regional oncology program were recruited on a volunteer basis to participate in the training. A mixed-method approach was undertaken to evaluate perceived benefits to clinical practice. The quantitative findings from the Perceived Stress Scale (PSS) showed there were no changes before and after training. Qualitative findings showed significant benefits to RTs, including (1) ability to empathically attune more effectively and earlier to signs of anxiety in patients; (2) improved confidence and self-efficacy for effectively intervening in difficult treatment situations; and (3) enhanced creative problem solving in partnership with patients to assist the acutely anxious patient. The PSS results suggest that intrinsic and extrinsic stressors are a variable but consistent part of the everyday reality of RT practice. A training program focusing on the therapeutic presence dimension of RT practice has a direct impact on effectiveness in assisting anxious patients undergoing RT. When embedded in the clinical life of a radiation therapy department, such a training program has the potential to capture the lived practice wisdom and creative skills of RTs, and effectively mediate these skills across a team through group discussions and documentation on patient charts.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Exploring the Experiences of Left-Sided Breast Cancer Patients Receiving Radiation Therapy Using the Active Breathing Coordinator

The Active Breathing Coordinator (ABC) to induce breath hold during radiation therapy is used with the intent to reduce the risk of long-term, radiation-induced cardiovascular morbidity. Many studies have explored the dosimetric and toxicity benefits of using the device, but limited research has been done on the patient's perspective. The aim of this study was to explore the patient's experience using the ABC device and to evaluate the teaching provided.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Radiation Therapy Students as Partners in the Development of Alternative Approaches to Assessing Treatment Planning Skills

The involvement of undergraduate students in assessment design is not a new concept. However, there is a paucity of evidence on radiation therapy students' participation in the design of teaching and learning activities. This article reports the development and initial evaluation of alternative approaches to assessing treatment planning skills and knowledge in undergraduate radiation therapy education.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Assessing Transgender-Related Content in Radiography Programs in the United States: A Survey of Educators

Imaging science technologists care for diverse populations of individuals and need to be adequately prepared to provide optimal patient care that reduces any potential disparities among these individuals. In particular, exposure to transgender individuals is increasing in today's society. The purpose of this research study was to examine the extent to which transgender-related content is taught in radiography programs in the United States by surveying educators to assess their knowledge of, experience with, and readiness to teach such content.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

What Students Think About (Nuclear) Radiation – Before and After Fukushima

Preparing successful science lessons is very demanding. One important aspect a teacher has to consider is the students' previous knowledge about the specific topic. This is why research about students' preconceptions has been, and continues to be, a major field in science education research. Following a constructivistic approach [R. Duit et al., International handbook of research on conceptual change, p. 629 (2008)], helping students learn is only possible if teachers know about students' ideas beforehand. Studies about students' conceptions regarding the major topics in physics education (e.g. mechanics, electrodynamics, optics, thermodynamics), are numerous and well-documented. The topic radiation, however, has seen very little empirical research about students' ideas and misconceptions. Some research was conducted after the events of Chernobyl [P. Lijnse et al., International Journal of Science Education 12, 67 (1990); B. Verplanken, Environment and Behavior 21, 7 (1989)] and provided interesting insight into some of the students' preconceptions about radiation. In order to contribute empirical findings to this field of research, our workgroup has been investigating the conceptions students have about the topic radiation for several years [S. Neumann et al., Journal of Science Education and Technology 21, 826 (2012)]. We used children's drawings and conducted short follow-up interviews with students (9 – 12 years old) and more detailed interviews with 15-year-old students. Both studies were originally done before the events in Fukushima and replicated a year later. We not only asked students about their general associations and emotions regarding the term radiation, but also examined the students' risk perceptions of different types of radiation. Through the use of open-ended questions we were able to examine students' conceptions about different types of radiation (including nuclear) that could be a hindrance to student learning. Our results show that students' associations with the term radiation are almost exclusively related to nuclear radiation. Their emotions concerning the word radiation are predominately negative, and the idea that radiation is something to be avoided is widespread among students. Since most students were not familiar with the idea of naturally occurring nuclear radiation, it does not seem surprising that a lot of them generally described radiation as something artificial and man-made. Also, none of the students interviewed mentioned applications of nuclear radiation in medicine or technology (besides its use in nuclear power plants). All of these results have shown to be even more prevalent in the interview session that was conducted after the tragic events in Fukushima in 2011. In this article, we will also include suggestions for improving the teaching of the topic radiation in school.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Backpropagation-based learning with local derivative approximation and memory replay in biologically plausible neural systems

When learning, the brain modifies individual synaptic connections to reach a desired behavior. Animal and human brains have been shown to be incredibly capable of learning complex and varied functions across a wide variety of tasks. In recent years, artificial neural networks, inspired by human and animal brains, have shown great capabilities in learning a wide variety of difficult tasks. However, artificial neural networks primarily teach themselves through the use of backpropagation, a learning method which has no clear analogue within the brain. Additionally, Artificial Neural Networks primarily use continuous activation functions, which differ significantly from the spiking neuronal behavior present in the brain. In this paper, we discuss and demonstrate a biologically plausible learning method that approximates backpropagation through two techniques on Spiking Neural Networks. First, we show that the local temporal derivatives that are necessary for backpropagation can be approximately recovered through reconstruction using spike timings. Second, we show that through learning during a sleep phase, inspired by neuroscience research into memory replay, the localized parallel feedback path can learn to approximate the derivative through the forward path weight matrix, thus solving the weight transport problem. Lastly, we demonstrate that the combination of these two methods can approach or exceed the accuracy of backpropagation-based methods for a variety of neuromorphic vision tasks while maintaining biological plausibility.

42 ENGINEERING↗
DOE OSTI2024

Ambient energy for buildings: Beyond energy efficiency

The following Key Messages comprise the salient findings of this study: 1. Ambient energy (from sun, air, ground, and sky) can heat and cool buildings; provide hot water, ventilation and daylighting; dry clothes; and cook food. These services account for about three-quarters of building energy consumption and a third of total US demand. Biophilic design (direct and indirect connections with nature) is an intrinsic adjunct to ambient energy systems, and improves wellness and human performance. 2. The current strategy of electrification and energy efficiency for buildings will not meet our climate goals, because the transition to an all-renewable electric grid is too slow. Widespread adoption of ambient energy is needed. Solar-heated buildings also flatten the seasonal demand for electricity compared to all-electric buildings, reducing required production capacity and long-term energy storage. In addition, ambient-conditioned buildings improve resilience by remaining livable during power outages. 3. National policies, incentives, and marketing should be enacted to promote ambient energy use. Federal administrative priorities should reflect the importance of ambient energy for buildings. Use of ambient energy should be encouraged through existing and new building codes and standards. 4. Ambient energy system design tools are needed for architects, engineers, builders, building scientists, realtors, appraisers, and consumers. PVWatts is used over 100 million times per year for photovoltaic system design. A similar, simple, and accessible tool for ambient design is crucial. 5. Training on ambient energy is needed throughout secondary, post-secondary, and continuing education for workforce development. Currently, only about 10% of colleges teach courses on passive heating and cooling systems. 6. Ambient-conditioned buildings should be demonstrated in all US climate zones. Performance should be monitored and reported, with quantitative case studies made widely available. 7. While current technology is sufficient to build high-performance ambient buildings now, research is needed to develop new technologies to harness ambient energy more effectively and more economically. Such advancements will facilitate adoption of ambient energy technologies in a wider range of buildings, including retrofits. Examples include windows with much lower thermal losses, use of the building shell as thermal storage, alternative light-weight thermal storage systems, sky radiation cooling systems, automated controls for solar gains and passive cooling, and ground coupling.

14 SOLAR ENERGY↗
DOE OSTI2017

Relation of polymorphism of arsenic metabolism genes to arsenic methylation capacity and developmental delay in preschool children in Taiwan

Inefficient arsenic methylation capacity has been associated with developmental delay in children. The present study was designed to explore whether polymorphisms and haplotypes of arsenic methyltransferase (AS3MT), glutathione-S-transferase omegas (GSTOs), and purine nucleoside phosphorylase (PNP) affect arsenic methylation capacity and developmental delay. A case-control study was conducted from August 2010 to March 2014. All participants were recruited from the Shin Kong Wu Ho-Su Memorial Teaching Hospital. In total, 179 children with developmental delay and 88 children without delay were recruited. Urinary arsenic species, including arsenite (As{sup III}), arsenate (As{sup V}), monomethylarsonic acid (MMA{sup V}), and dimethylarsinic acid (DMA{sup V}) were measured using a high-performance liquid chromatography-linked hydride generator and atomic absorption spectrometry. The polymorphisms of AS3MT, GSTO, and PNP were performed using the Sequenom MassARRAY platform with iPLEX Gold chemistry. Polymorphisms of AS3MT genes were found to affect susceptibility to developmental delay in children, but GSTO and PNP polymorphisms were not. Participants with AS3MT rs3740392 A/G + G/G genotype, compared with AS3MT rs3740392 A/A genotype, had a significantly lower secondary methylation index. This may result in an increased OR for developmental delay. Participants with the AS3MT high-risk haplotype had a significantly higher OR than those with AS3MT low-risk haplotypes [OR and 95% CI, 1.59 (1.08–2.34)]. This is the first study to show a joint dose-response effect of this AS3MT high-risk haplotype and inefficient arsenic methylation capacity on developmental delay. Our data provide evidence that AS3MT genes are related to developmental delay and may partially influence arsenic methylation capacity. - Highlights: • AS3MT genotypes were found to affect susceptibility to developmental delay. • AS3MT rs3740392 A/G and G/G genotype had a significantly low SMI (DMA/MMA) index. • AS3MT high-risk haplotype was significantly associated with developmental delay.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Materials for learning use of GSAS-II

The General Structure Analysis System (GSAS-II) package provides materials and crystallographic analysis for all types of diffraction data. It was initially made available with very limited capabilities, but over much of the last decade the features have been expanded, so that GSAS-II is now a comprehensive tool for nearly all types of structural and materials characterization studies. The need to provide materials to teach use of GSAS-II, while the software has been undergoing constant revision and expansion, has required new approaches for documentation. This has included providing tutorials, as each major new capability has been added, and context-sensitive help for each section of the program. Comments in the code are also expanded into a software reference guide. Finally, most recently, video versions of more than half of the tutorials were created and others were provided with animated graphics. All GSAS-II documentation is web-based.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quasi-Two-Dimensional Heterostructures (K M 1 – x Te)(LaTe 3 ) ( M = Mn and Zn) with Charge Density Waves

Layered heterostructure materials with two different functional building blocks can teach us about emergent physical properties and phenomena arising from interactions between the layers. Here, we report intergrowth compounds KLaM 1 - x Te 4 (M = Mn and Zn; $x \approx$ 0.35) featuring two chemically distinct alternating layers [LaTe 3 ] and [KM 1 - x Te]. Their crystal structures are incommensurate, determined by single X-ray diffraction for the Mn compound and a transmission electron microscope study for the Zn compound. KLaMn 1 – x Te 4 crystallizes in the orthorhombic superspace group Pmnm(01/2 gamma)s00 with lattice parameters a = 4.4815(3) Å, b = 21.6649(16) Å, and c = 4.5220(3) Å. It exhibits charge density wave order at room temperature with a modulation wave vector q = 1/2b* + 0.3478c* originating from electronic instability of Te-square nets in [LaTe 3 ] layers. The Mn analog exhibits a cluster spin glass behavior with spin freezing temperature $T_f \approx$ 5 K attributed to disordered Mn vacancies and competing magnetic interactions in the [Mn 1 - x Te] layers. The Zn analog also has charge density wave order at room temperature with a similar q-vector having the c* component similar to 0.346 confirmed by selected-area electron diffraction. Electron transfer from [KM 1 - x Te] to [LaTe 3 ] layers exists in KLaM 1 – x Te 4 , leading to an enhanced electronic specific heat coefficient. The resistivities of KLaM 1 - x Te 4 (M = Mn and Zn) exhibit metallic behavior at high temperatures and an upturn at low temperatures, suggesting partial localization of carriers in the [LaTe 3 ] layers with some degree of disorder associated with the M atom vacancies in the [M 1 - x Te] layers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Introducing the Journal of Chemical Education’s Special Issue on Chemical Security

Chemical security can be described as the practice of protecting chemicals from people. This editorial introduces the Special Issue on Chemical Security. In this article, we present a concise history of the use of chemicals as weapons and briefly describe consolidated global approaches needed to decrease the security risks from chemicals. We briefly introduce the contributions to this Special Issue from scientists and educators around the world on the topic of chemical security. Given recent high-profile global events that involved chemicals as weapons, improving chemical security awareness and competency is a critical need. We hope this Special Issue continues to generate conversation and collaboration on this important topic and enables educators to teach chemical security principles in their classrooms and laboratories.

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

InteraChem: Virtual Reality Visualizer for Reactive Interactive Molecular Dynamics

Interactive molecular dynamics in virtual reality (IMD-VR) simulations provide a digital molecular playground for students as an alternative or complement to traditional molecular modeling kits or 2D illustrations. Previous IMD-VR studies have used molecular mechanics to enable simulations of macromolecules such as proteins and nanostructures for the classroom setting with considerable success. Here, we present the InteraChem molecular visualizer, intended for reactive IMD-VR simulation using semiempirical and ab initio methods. InteraChem visualizes not only the molecular geometry but also (1) isosurfaces such as molecular orbitals and electrostatic potentials and (2) two-dimensional graphs of time-varying simulation quantities such as kinetic/potential energy, internal coordinates, and user-applied force. Additionally, InteraChem employs speech recognition to facilitate user interaction and introduces a novel “atom happiness” visualization using emojis to indicate the energetic feasibility of a particular bonding arrangement. Furthermore, we include a set of accompanying exercises that we have used to teach chemical reactivity in small molecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Practical Guide to Chemometric Analysis of Optical Spectroscopic Data

The methodology and mathematical treatment of several classic multivariate methods for the analysis of spectroscopic data is demonstrated in a straightforward way that can be used as a basis for teaching an undergraduate introductory course on chemometric analysis. The multivariate techniques of classical least squares (CLS), principal component regression (PCR), and partial least squares (PLS), as well as the univariate Beer’s law method have been described and compared, building students’ understanding by starting with the univariate method and progressing step by step into the multivariate methods. Equations for the production of regression vectors from training set spectral data is described and their use demonstrated for the prediction of constituent concentrations on a separate validation set of spectra. Extreme care is taken to ensure consistency in variable formatting of data matrices. This provides a key foundation to understanding how spectral data are manipulated using these different mathematical approaches for building quantitative regression models. Each method is applied to a real-world data set, and the results are discussed to show students the types of information that can be gleaned from each method. A training set comprised of 20 infrared absorbance spectra containing 3 constituents (benzene, polystyrene, and gasoline) of known composition are used to demonstrate the matrix operations for each regression method. A separate set of 12 real-world napalm samples (containing benzene, polystyrene and gasoline) are used as a validation set to demonstrate the ability to utilize the regression models on an unknown dataset. A toolbox (PNNL Chemometric Toolbox) written in MATLAB language is supplied in the Supplemental Information file and can be used as a companion for understanding the development and deployment of the chemometric algorithms described in this paper. The datasets of the infrared spectra are also supplied, allowing users to build and inspect the chemometric models on their own. Finally, the Toolbox includes scripts to assist users in loading their own datasets into MATLAB and performing CLS, PCR, and PLS on their data.

Upper-Division Undergraduate, Analytical Chemistry↗
DOE OSTI2025

Developing and Demonstrating a Lab Method for Quantifying Hair Exposure to Environmental Tobacco Smoke with a Forensic Perspective

Forensic science and chemistry curricula often lack hands-on experimental designs that effectively simulate the impact of environmental tobacco smoke (ETS) or other common elements found at crime scenes, such as marijuana, on trace forensic evidence. Hair, a critical form of trace evidence, offers unique advantages for assessing long-term exposure to environmental pollutants, including ETS. This study presents a novel, noninvasive forensic laboratory module designed to evaluate ETS exposure on various human hair types (untreated, dyed, and bleached). The experimental procedure involved controlled cigarette smoke exposure, followed by analysis using UV−visible spectroscopy, FTIR spectroscopy, and zeta potential measurements. Thirteen students participated in the three-week lab module (three sessions per week). Pre- and postlab assessments were conducted to evaluate learning outcomes. The prelab assessment focused on baseline knowledge of forensic hair analysis, as well as student expectations and confidence. The postlab assessment evaluated knowledge gained, technical insights, application of techniques, self-reflection, conceptual understanding, and practical skill development. This design helped students comprehend the effects of chemical treatments that significantly influence hair’s capacity to adsorb ETS residues by altering its physical and chemical properties. Integration of this experiment into the forensic chemistry curriculum led to measurable gains in student understanding, technical competency, and appreciation for realworld forensic applications. This method offers a valuable teaching and investigative tool for assessing individual ETS exposure in forensic contexts.

FTIR↗