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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track

Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing. We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Switching from batch to continuous granulation: A case study of metoprolol succinate ER tablets

Continuous manufacturing (CM) has been used to produce several immediate release drug products. No extended-release (ER) product manufactured employing CM technology has been approved yet. Herein this study investigated the critical aspects of switching from the batch mode of high shear granulation to the continuous operation of twin-screw granulation for extended-release tablets. Metoprolol succinate ER tablets was used as a model ER formulation for this purpose. A central composite design (CCD) was employed to determine the effects of high shear granulator (HSG) parameters, namely impeller speed, granulation time, and binder liquid feeding rate, on the critical granulation characteristics important for product performance. These critical granulation characteristics served as a guide for switching from the batch processing to the continuous operation for achieving the same breaking strength and dissolution for this ER metoprolol tablets. The granulation time was the most critical factor affecting the bulk properties of granules which contributed to tablet dissolution. The higher density and lower compressibility of granules were attained at the longest granulation time of 5.4 min with the fastest liquid feeding rate of 75 g/min. The granules’ density was the primary factor negatively affecting the dissolution of metoprolol tablets. However, the breaking strength of tablets confounded the effect of granules density on metoprolol dissolution. Switching the processing parameters of high shear granulation to twin-screw granulation achieved similar dissolution profiles (F2 greater than 50). The screw speed was not found to affect bulk properties of granules. The root cause of granulation failures in twin-screw granulation, such as premature consolidation, excessive swelling, poor cohesion, inconsistent shearing effects, and formation of deformed agglomerates, were identified. In conclusion, the use of critical granulation characteristics through a performance-based approach of ER tablets facilitated the switching of manufacturing of an ER formulation form batch to continuous operation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Optimization of screw design for continuous wet granulation: A case study of metoprolol succinate ER tablets

This study aimed at understanding the effect of screw design on the critical characteristics of granules and tablets of an extended-release (ER) formulation for twin screw granulation process. The screw design parameters assessed included number of kneading elements (KEs) per kneading zone, distance separating kneading zones, staggering angle (SA) of kneading elements and number of sizing elements (SEs). These input variables were varied using a design of experiment (DoE) approach to manufacture granules. Particle size distribution (PSD), flow and bulk properties of the granules, breaking strength and dissolution of tablets manufactured using these granules were characterized. The results of least square fitting showed that KEs, SA, and SEs of the screws significantly (p -values < 0.05) affected the PSD, cohesion, compressibility (CPS), conditioned bulk density (CBD) and permeability of the granules. The KEs and SEs significantly (p -value < 0.05) affected the dissolution, which was attributed to their effects on CPS and CBD of the granules. The distance between kneading zones had no significant effect on granules and tablet characteristics. Finally, these results may be used to further study the interaction of the identified critical screw design parameters with other processing parameters for continuous manufacturing of this ER matrix-based tablet formulation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

An integrated data management and informatics framework for continuous drug product manufacturing processes: A case study on two pilot plants

The pharmaceutical industry continuously looks for ways to improve its development and manufacturing efficiency. In recent years, such efforts have been driven by the transition from batch to continuous manufacturing and digitalization in process development. To facilitate this transition, integrated data management and informatics tools need to be developed and implemented within the framework of Industry 4.0 technology. Here, in this regard, the work aims to guide the data integration development of continuous pharmaceutical manufacturing processes under the Industry 4.0 framework, improving digital maturity and enabling the development of digital twins. This paper demonstrates two instances where a data integration framework has been successfully employed in academic continuous pharmaceutical manufacturing pilot plants. Details of the integration structure and information flows are comprehensively showcased. Approaches to mitigate concerns in incorporating complex data streams, including integrating multiple process analytical technology tools and legacy equipment, connecting cloud data and simulation models, and safeguarding cyber-physical security, are discussed. Critical challenges and opportunities for practical considerations are highlighted.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Detection and attribution of long-term and fine-scale changes in spring phenology over urban areas: A case study in New York State

Spring phenology plays an essential role in climate change, terrestrial ecosystem, and public health. Field-based monitoring and understanding of changes in spring phenology for long periods and in large regions are challenging due to the limited in-site observations. Space-based remotely sensed observations offer great potentials for monitoring decadal spring phenology changes from regional to global scales. However, the coarse-scale remotely sensed observations are insufficient to capture fine-scale spring phenology dynamics, especially in urban areas, and this makes it challenging for understanding the combined effects of climate change and urbanization on spring phenology. We derived the start of phenology season (SOS) in New York State using 30 m Landsat observations from 1990 to 2015 to understand the impact of the environment and urbanization on SOS. The results show that SOS for different years reveals heterogeneous spatial distribution. Most regions of New York State have been experiencing significant spring phenology changes in form of earlier onset of vegetation greening, ranging from 0.2 to 0.6 day/year during 1990 to 2015, and this trend varies slightly with latitudes and urbanization levels. Further, spatial correlation analysis shows that the increase in temperature and urbanization could both promote the advancement of SOS. However, the effect of urbanization (partial correlation coefficient (R) ranges from −0.289 to −0.542) on SOS is greater than the effect of temperature (R ranges from 0.006 to −0.192). The study generates a high spatio-temporal resolution spring phenology dataset for ecological, environmental and public health studies, especially in urban areas, and reveals the importance of better accounting for the urbanization effects when quantifying the SOS dynamics in phenology models.

Landsat↗
DOE OSTI2022

Revisit the VEC criterion in high entropy alloys (HEAs) with high-throughput ab initio calculations: A case study with Al-Co-Cr-Fe-Ni system

Valence electron concentration (VEC) was treated as a useful parameter to predict the stability of solid solution phases. However, the available experimental data to support this criterion is far from enough. In the current study, the high-throughput ab initio modeling is applied to investigate the relative stability of FCC and BCC single crystals of the Al-Co-Cr-Fe-Ni high entropy alloys (HEAs) by using the special quasi-random structure (SQS) approach. Furthermore, the predictions start with pure elements of the Al-Co-Cr-Fe-Ni system and are continued with binaries, ternaries, and quaternary compositions, which come up with 180 compositions (360 structures). After that, the reliability of the VEC criterion is testified. The results show that the VEC criterion not only works for the stable structure but also works effectively for metastable structure when both FCC and BCC are not thermodynamic stable. However, it is found that the old VEC criterion proposed by Guo et al. fails to work effectively for compositions containing high concentrations of light-weight metals such as Al at VEC<5. To solve this problem, the present work proposes a new VEC rule to define the stability of FCC and BCC structures at the ground state. With the implementation of the new VEC rule, the effectiveness of the VEC rule (EVEC) of both FCC and BCC structures is enhanced, especially for pure elements and binary compositions, indicating that this rule does not only work effectively for multicomponent systems but also works for low-order systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Passive and active peer effects in the spatial diffusion of residential solar panels: A case study of the Las Vegas Valley

This research analyzes the role of peer influences on the adoption of residential rooftop solar photovoltaic panels (PV) within the context of the Diffusion of Innovation Theory. PV literature indicates that adopters are influenced by word of mouth (WOM) information exchange with peers, i.e., active peer effects, while other studies suggest that living near households with visible rooftop PV installations influences adoption, i.e., passive peer effects. We bridge the gap in this literature by conducting a mixed methods analysis. We develop and administer a survey to Las Vegas Valley (LVV) residents to identify current and potential PV adopters' perceptions of peer-effects and consumer intention variables. We conduct a spatial analysis of Google's Project Sunroof data to identify LVV neighborhoods in the later stages of the PV diffusion process, i.e., those with the highest PV adoption rates. Key results show that current PV adopters living in early diffusion areas report significantly higher active and passive peer effects compared to adopters in later diffusion areas. Potential adopters in later diffusion areas report higher passive peer effects than those in early diffusion areas. Overall, because LVV has a low PV adoption rate (<3%), short term strategies aimed at increasing PV adoption should emphasize WOM active peer effects. Here, we caution against long-term green marketing strategies focusing solely on peer-effects as the PV market matures.

14 SOLAR ENERGY↗
DOE OSTI2020

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mitigating life cycle GHG emissions of roads to be built through 2030: Case study of a Chinese province

We report the expansion of road networks in emerging economies such as China causes significant greenhouse gas (GHG) emissions. This development is conflicting with China's commitment to achieve carbon neutrality. Thus, there is a need to better understand life cycle emissions of road infrastructure and opportunities to mitigate these emissions. Existing impact studies of roads in developing countries do not address recycled materials, improved pavement maintenance, or pavement-vehicle interaction and electric vehicle (EV) adoption. Combining firsthand information from Chinese road construction engineers with publicly available data, this paper estimates a comprehensive account of GHG emissions of the road pavement network to be constructed in the next ten years in the Shandong province in Northern China. Further, we estimate the potential of GHG emission reductions achievable under three scenario sets: maintenance optimization, alternative pavement material replacement, and EV adoption. Results show that the life cycle GHG emissions of highways and Class 1–4 roads to be constructed in the next 10 years amount to 147 Mt CO2-eq. Considering the use phase in our model reveals that it is the dominant stage in terms of emissions, largely due to pavement-vehicle interaction. Vehicle electrification can only moderately mitigate these emissions. Other stages, such as materials production and road maintenance and rehabilitation, contribute substantially to GHG emissions as well, highlighting the importance of optimizing the management of these stages. Surprisingly, longer, not shorter maintenance intervals, yield significant emission reductions. Another counter-intuitive finding is that thicker and more material-intensive pavement surfaces cause lower emissions overall. Taken together, optimal maintenance and rehabilitation schedules, alternative material use, and vehicle electrification provide GHG reduction potentials of 11%, 4%–16% and 2%–6%, respectively.

54 ENVIRONMENTAL SCIENCES↗