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Image guided radiation therapy (IGRT) is widely used, but data directly relating set-up errors to patient outcome are scarce. This study investigates the relationship between residual IGRT shifts and overall patient survival and uses the observed relations to identify structures sensitive to radiation dose.
Background: Using myocardial perfusion scintigraphy (MPS), an increase in left ventricular (LV) volumes or a decrease in ejection fraction (EF) from rest to stress may be clinically important. The variation in these measures between the low-dose stress acquisition and high-dose rest acquisition in a one-day stress-rest protocol has not been established. We assessed the reproducibility of gated volumetric indices between stress and rest and the normal variation in ungated TID ratio for a one-day stress-rest {sup 99m}Tc-tetrofosmin protocol. Methods: Two thousand and one hundred and fifty eight (2158) {sup 99m}Tc-tetrofosmin MPS patient studies were analyzed retrospectively. Studies were excluded for incomplete data, significant technical difficulties, or (for gated analysis but not for analysis of TID ratio) if the LV EF was > 75%. An analysis of gated data was undertaken to establish the reproducibility of ventricular volumes and EF between stress and rest scans. Ungated volume data were analyzed to determine the confidence limits of TID ratio according to ventricular volume. Results: Gated data were analyzed for 621 patients without inducible hypoperfusion. Mean EF at rest was slightly higher than after stress (62.4% ± 10.3% vs 61.2% ± 10.4%, P < 0.001), and the standard deviation of the difference was 5.2% (95% CI 4.9% to 5.5%). Ungated volumes were available for 992 non-ischaemic patients. The upper 95% CI for TID ratio was 1.23. This increased from 1.20 to 1.37 between the highest and lowest deciles of rest ungated volume. Conclusion: Using a one-day stress-rest {sup 99m}Tc-tetrofosmin protocol, a fall in LV EF between rest and stress of > 11.6% or a TID ratio of > 1.23 is likely to be clinically reliable. The upper limit of normal for TID ratio needs to be increased for patients with small LV chamber volumes.
Cues in the micro-environment are key determinants in the emergence of complex cellular morphologies and functions. Primary among these is the presence of neighboring cells that form networks. For high-resolution analysis, it is crucial to develop micro-environments that permit exquisite control of network formation. This is especially true in cell science, tissue engineering, and clinical biology. We introduce a new approach for assembling polydimethylsiloxane (PDMS)-based microfluidic environments that enhances cell network formation and analyses. We report that the combined processes of PDMS solvent-extraction and hydrothermal annealing create unique conditions that produce high-strength bonds between solvent-extracted PDMS (E-PDMS) and glass—properties not associated with conventional PDMS. Extraction followed by hydrothermal annealing removes unbound oligomers, promotes polymer cross-linking, facilitates covalent bond formation with glass, and retains the highest biocompatibility. Herein, our extraction protocol accelerates oligomer removal from 5 to 2 days. Resulting microfluidic platforms are uniquely suited for cell-network studies owing to high adhesion forces, effectively corralling cellular extensions and eliminating harmful oligomers. We demonstrate the simple, simultaneous actuation of multiple microfluidic domains for invoking ATP- and glutamate-induced Ca 2+ signaling in glial-cell networks. These E-PDMS modifications and flow manipulations further enable microfluidic technologies for cell-signaling and network studies as well as novel applications.
Diagnosis and prognosis of tumorigenesis are generally performed with CT, PET, or biopsy. Such methods are accurate, but have the limitations of high cost and posing additional health risks to patients. In this study, we introduce an alternative computer aided diagnostic tool that can locate malignant sites caused by tumorigenesis in a non-invasive and low-cost way. Our hypothesis is that exhaled aerosol distribution is unique to lung structure and is sensitive to airway structure vari-ations. With appropriate approaches, it is possible to locate the disease site, determine the disease severity, and subsequently formulate a targeted drug delivery plan to treat the disease. This study numerically evaluated the feasibility of the proposed breath test in an image-based lung model with varying pathological stages of a bronchial squamous tumor. Large eddy simulations and a Lagran-gian tracking approach were used to model respiratory airflows and aerosol dynamics. Respira-tions of tracer aerosols of 1 µm at a flow rate of 20 L/min were simulated, with the distributions of exhaled aerosols recorded on a filter at the mouth exit. Aerosol patterns were quantified with multiple analytical techniques such as concentration disparity, spatial scanning and fractal analysis. We demonstrated that a growing bronchial tumor induced notable variations in both the airflow and exhaled aerosol distribution. These variations became more apparent with increasing tumor severity. The exhaled aerosols exhibited distinctive pattern parameters such as spatial probability, fractal dimension, and multifractal spectrum. Results of this study show that morphometric measures of the exhaled aerosol pattern can be used to detect and monitor the pathological states of respiratory diseases in the upper airway. The proposed breath test also has the potential to locate the site of the disease, which is critical in developing a personalized, site-specific drug de-livery protocol.
Purpose: To determine an appropriate clinical target volume for partial breast radiation therapy (PBRT) based on the spatial distribution of residual invasive and in situ carcinoma after wide local excision (WLE) for early breast cancer or ductal carcinoma in situ (DCIS). Methods and Materials: We performed a prospective pathologic study of women potentially eligible for PBRT who had re-excision and/or completion mastectomy after WLE for early breast cancer or DCIS. A pathologic assessment protocol was used to determine the maximum radial extension (MRE) of residual carcinoma from the margin of the initial surgical cavity. Women were stratified by the closest initial radial margin width: negative (>1 mm), close (>0 mm and {<=}1 mm), or involved. Results: The study population was composed of 133 women with a median age of 59 years (range, 27-82 years) and the following stage groups: 0 (13.5%), I (40.6%), II (38.3%), and III (7.5%). The histologic subtypes of the primary tumor were invasive ductal carcinoma (74.4%), invasive lobular carcinoma (12.0%), and DCIS alone (13.5%). Residual carcinoma was present in the re-excision and completion mastectomy specimens in 55.4%, 14.3%, and 7.2% of women with an involved, close, and negative margin, respectively. In the 77 women with a noninvolved radial margin, the MRE of residual disease, if present, was {<=}10 mm in 97.4% (95% confidence interval 91.6-99.5) of cases. Larger MRE measurements were significantly associated with an involved margin (P<.001), tumor size >30 mm (P=.03), premenopausal status (P=.03), and negative progesterone receptor status (P=.05). Conclusions: A clinical target volume margin of 10 mm would encompass microscopic residual disease in >90% of women potentially eligible for PBRT after WLE with noninvolved resection margins.
Abstract Heterogeneous reduction of actinides in higher and more soluble oxidation states to lower more insoluble oxidation states by reductants such as Fe(II) has been the subject of intensive study for more than two decades. However, Fe(II)-induced reduction of sparingly soluble Pu(IV) to the more soluble lower oxidation state Pu(III) has been much less studied even though such reactions can potentially increase the mobility of Pu in the subsurface. Thermodynamic calculations are presented that show how differences in the free energy of various possible solid-phase Fe(III) reaction products can greatly influence aqueous Pu(III) concentrations resulting from reduction of PuO2(am) by Fe(II). We present the first experimental evidence that reduction of PuO2(am) to Pu(III) by Fe(II) was enhanced when the Fe(III) mineral goethite was spiked into the reaction. The effect of goethite on reduction of Pu(IV) was demonstrated by measuring the time-dependence of total aqueous Pu concentration, its oxidation state, and system pe/pH. We also re-evaluated established protocols for determining Pu(III) [(Pu(III) + Pu(IV)) - Pu(IV)] by using thenoyltrifluoroacetone (TTA) in toluene extractions; the study showed that it is important to eliminate dissolved oxygen from the TTA solutions for accurate determinations. More broadly, this study highlights the importance of the Fe(III) reaction product in actinide reduction rate and extent by Fe(II).
Ecological risk assessment (ERA) is charged with assessing the likelihood a chemical will have adverse environmental or ecological effects. When assessing the risk of a potential contaminant to biological organisms, ecologists are most concerned with the sustainability of populations of organisms, rather than protecting every individual. However, ERA most commonly relies on data on the effect of a potential contaminant on individuals because these experiments are more feasible than costly population-level exposures. In this work, we address the challenge of extrapolating these individual-level results to predict population-level effects. Previous per-capita population growth rate estimates calculated from individual-level exposures of Daphnia pulicaria to silver nanoparticles (AgNPs) at different food rations predict a critical daily food requirement for daphnid populations exposed to 200 μg/L AgNPs to avoid extinction. To test this, we exposed daphnid populations to the same AgNP concentration at three different food inputs, with the lowest ration close to the extinction threshold predicted from data on individuals. The two populations with the higher food inputs persisted, and the population with the lowest food input went extinct after 50 days but did persist through two generations. Further, we demonstrate that we can extrapolate between these levels of biological organization by parameterizing an individual-level biomass model with data on individuals’ response to AgNPs and using these parameters to predict the outcome for control and AgNP-exposed populations. Key to successful extrapolation is careful modeling of temporal changes in resource density, driven by both the experimental protocols and feedback from the consumer. The implication for ecotoxicology is that estimates of extinction thresholds based on studies of individuals may be reliable predictors of population outcomes, but only with careful treatment of resource dynamics.
Over the past two decades, the use of fragment-based lead generation has become a common, mature approach to identify tractable starting points in chemical space for the drug discovery process. This approach naturally involves the study of the binding properties of highly heterogeneous ligands. Such datasets challenge computational techniques to provide comparable binding free energy estimates from different binding modes. The performance of a range of statistically robust ensemble-based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), is evaluated. Ligands designed to target two binding pockets in the lactate dehydogenase, a target protein, which vary in size, charge, and binding mode, are studied. When compared to experimental results, excellent statistical rankings are obtained across this highly diverse set of ligands. In addition, three approaches to account for entropic contributions are investigated: 1) normal mode analysis, 2) weighted solvent accessible surface area (WSAS), and 3) variational entropy. Normal mode analysis and WSAS correlate strongly with each other—although the latter is computationally far cheaper—but do not improve rankings. Variational entropy corrects exaggerated discrimination of ligands bound in different pockets but creates three outliers which reduce the quality of the overall ranking.
The National Reactor Innovation Center (NRIC) has designed a testbed for advanced microreactor experiments that enables and encourages technology developers to bring advanced designs closer to commercial availability. The test bed, Demonstration of Microreactor Experiments (DOME), will be used to facilitate much of the operation experimentation process of the each advanced microreactor including fueling and operational power experiments. This report identifies and evaluates the options for initial fueling the microreactors in DOME. This report also discusses the optional locations that were considered for the initial fueling of microreactor; namely the Transient Reactor Test (TREAT) reactor building, and an unspecified temporary location. But various logistical and construction issues essentially eliminated these other options from further consideration. A brief discussion in this report outlines potential equipment and protocol associated with fueling in DOME.
Significantly reducing methane emissions from the oil and gas sector can decrease the rate of climate change over the next two decades, buying critical time for a global energy transition. However, emissions inventories that can be used by oil and gas operators and environmental regulators to identify optimal methane emission mitigation strategies are either based on conservative emission factor methods, or are inconsistent between studies due to differences in sampling strategies or survey technologies. We developed a new approach for methane emissions survey design that yields representative basinwide methane emissions inventories by surveying a subset of total assets in a given oil and gas basin. We identify several sampling and analysis principles, including large sample sizes, balanced sampling across oil and gas production, careful survey area definition, and a unified protocol for analysis, to be vital to producing an unbiased estimate of basin-scale emissions that can be reconciled with future studies. We further present results from deploying this strategy in two oil and gas producing regions in the United States: the Haynesville Basin in Texas and Louisiana, and the Woodford Shale in the Anadarko Basin in Oklahoma. Aerial surveys were performed in 2023 using the Insight M LeakSurveyor™ technology. Preliminary results from methane emissions detected by Insight M indicate that aerially detected emissions above roughly 30 kg(CH4)/hr by themselves contribute a fractional loss rate of 1.13% of gross gas production across oil and gas operations in the Haynesville Basin, with aerially detected emissions equivalent to 2.67% of gross gas production in the Woodford Shale. We supplement these aerial estimates with modeled emissions that are below the LeakSurveyor’s survey sensitivity using a recently published inventory-based model of methane emissions, which we update for our survey areas. We then combine our aerial detections with modeled emissions to yield methane emission distributions and inventories that incorporate the full range of potential methane emissions from the smallest to the largest. These results can be used to identify the most effective methane mitigation strategies for our study areas, and can be reconciled with future methane emissions surveys that use different technologies.
This technical publication details part of an effort focused on the development of a standardized facesheet/core peel debonding test procedure. The purpose of the test is to characterize facesheet/core peel in sandwich structure, accomplished through the measurement of the critical strain energy release rate associated with the debonding process. Following an examination of previously developed tests and a recent evaluation of a selection of these methods, a single cantilever beam (SCB) specimen was identified as being a promising candidate for establishing such a standardized test procedure. The objective of the work described here was to begin development of a protocol for conducting a SCB test that will render the procedure suitable for standardization. To this end, a sizing methodology was developed to ensure appropriate SCB specimen dimensions are selected for a given sandwich system. Application of this method to actual sandwich systems yielded SCB specimen dimensions that would be practical for use. This study resulted in the development of a practical SCB specimen sizing method, which should be well-suited for incorporation into a standardized testing protocol.
During NEEMO missions, a crew of six Aquanauts lives aboard the National Oceanic and Atmospheric Administration (NOAA) Aquarius Underwater Laboratory the world's only undersea laboratory located 5.6 km off shore from Key Largo, Florida. The Aquarius habitat is anchored 62 feet deep on Conch Reef which is a research only zone for coral reef monitoring in the Florida Keys National Marine Sanctuary. The crew lives in saturation for a week to ten days and conducts a variety of undersea EVAs (Extra Vehicular Activities) to test a suite of long-duration spaceflight Engineering, Biomedical, and Geoscience objectives. The crew also tests concepts for future lunar exploration using advanced navigation and communication equipment in support of the Constellation Program planetary exploration analog studies. The Astromaterials Research and Exploration Science (ARES) Directorate and Behavioral Health and Performance (BHP) at NASA/Johnson Space Center (JSC), Houston, Texas support this effort to produce a high-fidelity test-bed for studies of human planetary exploration in extreme environments as well as to develop and test the synergy between human and robotic curation protocols including sample collection, documentation, and sample handling. The geoscience objectives for NEEMO missions reflect the requirements for Lunar Surface Science outlined by the LEAG (Lunar Exploration Analysis Group) and CAPTEM (Curation and Analysis Planning Team for Extraterrestrial Materials) white paper [1]. The BHP objectives are to investigate best meas-ures and tools for assessing decrements in cogni-tive function due to fatigue, test the feasibility study examined how teams perform and interact across two levels, use NEEMO as a testbed for the development, deployment, and evaluation of a scheduling and planning tool. A suite of Space Life Sciences studies are accomplished as well, ranging from behavioral health and performance to immunology, nutrition, and EVA suit design results of which will directly support the investigation of open questions and operational concepts that will enable NASA to continue its plan for planetary exploration.
Leakage from deep storage reservoirs is considered the major risk factor associated with geologic sequestration of CO2. Different schools of thought exist concerning the potential implications of such leakage for near-surface environments. We reviewed the current literature on how CO2 leakage (from storage reservoirs) would likely impact the geochemistry of overlying potable aquifers. Results from experimental and modeling studies point to the potential for both beneficial (e.g. contaminant immobilization) and deleterious (e.g. contaminant mobilization) consequences of CO2 intrusion into potable groundwater. However, there are significant discrepancies between studies particularly concerning, what contaminants are of concern and the geochemical processes involved. These discrepancies reflected the lack of a consensus on CO2-induced changes in subsurface geochemical processes and subsequent effects on groundwater chemistry. The development of consistent experimental protocols and the identification of pertinent factors driving CO2-induced geochemical changes in the subsurface were identified as key research needs. Geochemical modeling was used to systematically highlight why a standardization of experimental protocols and the consideration of experimental factors such as gas leakage rates, redox status and the influence of co-transported gases are pertinent. The role of analog studies, reactions occurring in the vadose zone, and the influence of organic contaminants are also discussed.
Thermococcales are among the most widely studied hyperthermophilic Archaea and have become key models for understanding life at extreme temperatures. Early work in the 1980s culminated in the isolation of novel Thermococcales species from hydrothermal vents that grew rapidly, tolerated extreme heat, and metabolized diverse substrates, making them uniquely amenable for laboratory studies. Their thermostable enzymes and emerging genetic tools facilitated detailed investigations of core processes such as DNA replication, repair, and transcription under conditions that challenge most life forms. These practical advantages, together with the accumulation of tools and protocols, cemented the role of Thermococcales as a model system. Here, we recount how chance discoveries, environmental adaptations, and experimental practicality intersected to establish Thermococcales as a central model for studying archaeal biology and extremophile physiology.
Exercise in microgravity is one of the most promising countermeasures to the dual problems of space flight-induced bone loss and muscle atrophy. Although exercise in microgravity has been studied extensively from a metabolic standpoint, little research has focused on the efficacy of different forms of exercise for maintaining musculoskeletal integrity. Exercise protocols have not been effective in preventing muscle atrophy and bone loss during space flight, especially in the lower extremities. In 1-G, however, animal experiments have clearly indicated that: (1) certain bone strains and strain rates do stimulate bone deposition, and (2) repetitive loading of the lower extremity can increase osteonal bone formation even as proximally as the vertebral column. Such studies have also indicated that a relatively small number of appropriate loading cycles may lead to bone deposition. This suggests that an optimal exercise regimen might be able to maintain bone and muscle integrity during space flight. Since there is evidence that the bones and muscles of the lower limbs are particularly affected by space flight, the present study addressed two major aims: (1) quantify externally applied impact loads and rates of loading under the feet during tethered jumping exercises, and (2) determine the amount of eccentric and concentric whole-muscle activity during these jumping exercises in true and in simulated zero-gravity.
Clinical pathways (CPs) are structured healthcare plans designed to implement evidence-based clinical guidelines, medical algorithms, and protocols. In recent years, a community called BPM+ Health has worked to establish a shareable and computer-consumable representation of CP, leveraging standard notations. These notations, collectively referred to as BPM+, include the Business Process Management and Notation (BPMN), Case Management Model and Notation (CMMN), and Decision Model and Notation (DMN), which aim to support clinical management and standardized communication between different stakeholders. However, the adaptation of these notations for the existing guidelines has largely been left unexplored. This paper introduces procedural steps and criteria considerations to apply components of BPM+ notations to reconstruct a guideline for Stable Ischemic Heart Disease. This paper describes how each of the three different notations is mapped to a medical guideline and discusses the advantages and limitations of representing CPs with BPM+ as compared with paper-based medical guidelines.
The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: Designed a network architecture for automated multi-domain service provisioning: Designed information exchange schemes between autonomous systems (domains) Designed application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networks belonging to different autonomous systems Experimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents Designed a broker-based orchestrated multi-domain optical networking framework supporting observe-analyze-act cycle based cognitive inter-domain service provisioning: Designed an inter-domain operation mechanism, e.g., topology abstraction, for the broker plane and domain managers to collaboratively accomplish inter-domain service provisioning while respecting the autonomy of each administrative domain Designed an alien wavelength monitoring scheme and an artificial neural network based optical signal-to-noise ratio estimator for quality-of-transmission aware inter-domain service provisioning Developed a deep neural network based multi-domain traffic predictor and a cognitive routing and spectrum assignment algorithm to realize knowledge-based autonomous traffic engineering in multi-domain networks Implemented an optical signal-to-noise ratio estimator and traffic predictor with Google’s Tensor flow and assessed their performance using experimental and synthetic data Demonstrated the benefits of the proposed cognitive schemes with experiments ona two-domain elastic optical network testbed and evaluations on a six-domain simulation framework Built a two-domain software-defined networking control plane testbed using the ONOS platform and demonstrated the workflow of broker and domain manager cooperation, including hierarchical performance monitoring, topology abstraction and cognitive resource allocation. Designed self-learning cognitive service provisioning schemes using advanced machine learning technologies: Designed a self-taught anomaly detection framework for optical networks, including an unsupervised data clustering module for pattern recognition, a supervised data regression and classification module for online anomaly detection, and the system operation principle Experimentally validated the effectiveness of the self-taught anomaly detection framework with various failure/anomaly scenarios. Designed a self-learning autonomic routing, modulation and spectrum assignment (RMSA) provisioning framework for elastic optical networks with deep reinforcement learning. Designed effective modeling and training mechanisms for the self-learning RMSA framework Conducted extensive numerical simulations with various traffic patterns and topologies to verify the advantage of the self-learning RMSA framework.