Machine learning has proven to be a powerful tool for accelerating biofuel development. Although numerous models are available to predict a range of properties using chemical descriptors, there is a ...trade-off between interpretability and performance. Neural networks provide predictive models with high accuracy at the expense of some interpretability, while simpler models such as linear regression often lack in accuracy. In addition to model architecture, feature selection is also critical for developing interpretable and accurate predictive models. We present a method for systematically selecting molecular descriptor features and developing interpretable machine learning models without sacrificing accuracy. Our method simplifies the process of selecting features by reducing feature multicollinearity and enables discoveries of new relationships between global properties and molecular descriptors. To demonstrate our approach, we developed models for predicting melting point, boiling point, flash point, yield sooting index, and net heat of combustion with the help of the Tree-based Pipeline Optimization Tool (TPOT). For training, we used publicly available experimental data for up to 8351 molecules. Our models accurately predict various molecular properties for organic molecules (mean absolute percent error (MAPE) ranges from 3.3% to 10.5%) and provide a set of features that are well-correlated to the property. This method enables researchers to explore sets of features that significantly contribute to the prediction of the property, offering new scientific insights. To help accelerate early stage biofuel research and development, we also integrated the data and models into a open-source, interactive web tool.
•Developed method for selecting chemical descriptors and minimizing collinearity•Trained five property prediction models using diverse data sets•Models are interpretable and yield excellent peformance•Feature importances are consistent and agree with previous research•Webtool available at feedstock-to-function.lbl.gov
The dynamics of microbial communities involved in anaerobic digestion of mixed organic waste are notoriously complex and difficult to model, yet successful operation of anaerobic digestion is ...critical to the goals of diverting high-moisture organic waste from landfills. Machine learning (ML) is ideally suited to capturing complex and nonlinear behavior that cannot be modeled mechanistically. This study uses 8 years of data collected from an industrial-scale anaerobic co-digestion (AcoD) operation at a municipal wastewater treatment plant in Oakland, California, combined with a powerful automated ML method, Tree-based Pipeline Optimization Tool, to develop an improved understanding of how different waste inputs and operating conditions impact biogas yield. The model inputs included daily input volumes of 31 waste streams and 5 operating parameters. Because different wastes are broken down at varying rates, the model explored a range of time lags ascribed to each waste input ranging from 0 to 30 days. The results suggest that the waste types (including rendering waste, lactose, poultry waste, and fats, oils, and greases) differ considerably in their impact on biogas yield on both a per-gallon basis and a mass of volatile solids basis, while operating parameters were not good predictors of yield at this facility.
Technoeconomic analysis for biofuels and bioproducts Scown, Corinne D; Baral, Nawa Raj; Yang, Minliang ...
Current opinion in biotechnology,
February 2021, 2021-02-00, 20210201, 2021-02-01, Letnik:
67, Številka:
C
Journal Article
Recenzirano
Odprti dostop
Display omitted
•Technoeconomic analysis involves process design and simulation.•New studies have expanded to incorporate market size, policy incentives, and use.•There is a trend toward lightweight ...open-source TEA tools.•Inclusion both fuels and co-products complicates methods and performance metrics.•Integration of TEA with high-throughput experimental pipelines is promising.
Technoeconomic analysis (TEA) is an approach for conducting process design and simulation, informed by empirical data, to estimate capital costs, operating costs, mass balances, and energy balances for a commercial scale biorefinery. TEA serves as a useful method to screen potential research priorities, identify cost bottlenecks at the earliest stages of research, and provide the mass and energy data needed to conduct life-cycle environmental assessments. Recent studies have produced new tools and methods to enable faster iteration on potential designs, more robust uncertainty analysis, and greater accessibility through the use of open-source platforms. There is also a trend toward more expansive system boundaries to incorporate the impact of policy incentives, use-phase performance differences, and potential impacts on global market supply.
Background
Gasketless laparoscopic insufflator systems are marketed for the ability to prevent desufflation of pneumoperitoneum during laparoscopy. However, surgeons raised concern for possible ...introduction of non-absorbable room air, including oxygen (O
2
), with these systems. A community-university collaborative was created to test this hypothesis.
Methods
An artificial abdomen, calibrated to equivalent compliance and volume of an average abdomen, was connected to a flow meter, oxygen concentration sensor, and commercially available laparoscopic gasketless cannula system. A commercially available gasketed cannula system was utilized as a control. Intra-abdominal concentration of oxygen was measured at 0–60 L per minute (L/min) of insufflated carbon dioxide (CO
2
) aspiration, as would occur during laparoscopic suctioning. For reference, a 5-mm laparoscopic suction device has an aspiration rate of approx. 42 L per minute. At the test facility, room air was 20.5% O
2
at 50% humidity. Descriptive and univariate statistics were calculated with
p
< 0.05 considered significant.
Results
At 0 L/min CO
2
aspiration, there was minimal (< 0.5%) oxygen detected intra-abdominally. However, with increasing rates of aspiration of pneumoperitoneum, increasing amounts of room air were detected intraabdominally in the gasketless versus gasketed cannula systems (mean ± standard deviation): 14.7 ± 1.2% versus 1.2 ± 0.5%,
p
< 0.0001 at 5 L/min aspiration, 18.1 ± 0.69% versus 1.1 ± 0.02%,
p
< 0.0001 at 10 L/min, 50.4 ± 2.19% vs 1.01 ± 0.003%,
p
< 0.0001 at 20 L/min. Above 25 L/min aspiration, the standard gasketed cannula systems experienced desufflation, but the gasketless system continued to entrain air to maintain insufflation: 64% room air at 30 L/min aspiration, 71% at 40 L/min aspiration, 77% at 50 L/min aspiration, and 84% at 60 L/min aspiration.
Conclusions
Gasketless cannula insufflation systems maintain abdominal insufflation by entraining non-medical room air. Especially at high aspiration rates, the majority of absorbable CO
2
was replaced by non-medical room air, increasing potential for gas embolism with poorly absorbed oxygen and nitrogen. Authors have reported these experimental findings to the FDA and companies marketing these devices.
CLIMATE ENGINE Huntington, Justin L.; Hegewisch, Katherine C.; Daudert, Britta ...
Bulletin of the American Meteorological Society,
11/2017, Letnik:
98, Številka:
11
Journal Article
Recenzirano
Abstract
The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for ...decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
West Nile virus (WNV) is the leading cause of epidemic arboviral encephalitis in the United States. As there are currently no proven antiviral therapies or licensed human vaccines, understanding the ...neuropathogenesis of WNV is critical for rational therapeutic design. In WNV-infected mice, the depletion of microglia leads to enhanced viral replication, increased central nervous system (CNS) tissue injury, and increased mortality, suggesting that microglia play a critical role in protection against WNV neuroinvasive disease. To determine if augmenting microglial activation would provide a potential therapeutic strategy, we administered granulocyte-macrophage colony-stimulating factor (GM-CSF) to WNV-infected mice. Recombinant human GM-CSF (rHuGMCSF) (sargramostim Leukine) is an FDA-approved drug used to increase white blood cells following leukopenia-inducing chemotherapy or bone marrow transplantation. Daily treatment of both uninfected and WNV-infected mice with subcutaneous injections of GM-CSF resulted in microglial proliferation and activation as indicated by the enhanced expression of the microglia activation marker ionized calcium binding adaptor molecule 1 (Iba1) and several microglia-associated inflammatory cytokines, including CCL2 (C-C motif chemokine ligand 2), interleukin 6 (IL-6), and IL-10. In addition, more microglia adopted an activated morphology as demonstrated by increased sizes and more pronounced processes. GM-CSF-induced microglial activation in WNV-infected mice was associated with reduced viral titers and apoptotic activity (caspase 3) in the brains of WNV-infected mice and significantly increased survival. WNV-infected
brain slice cultures (BSCs) treated with GM-CSF also showed reduced viral titers and caspase 3 apoptotic cell death, indicating that GM-CSF specifically targets the CNS and that its actions are not dependent on peripheral immune activity. Our studies suggest that stimulation of microglial activation may be a viable therapeutic approach for the treatment of WNV neuroinvasive disease.
Although rare, WNV encephalitis poses a devastating health concern, with few treatment options and frequent long-term neurological sequelae. Currently, there are no human vaccines or specific antivirals against WNV infections, so further research into potential new therapeutic agents is critical. This study presents a novel treatment option for WNV infections using GM-CSF and lays the foundation for further studies into the use of GM-CSF as a treatment for WNV encephalitis as well as a potential treatment for other viral infections.
Display omitted
•Machine learning can be used to develop surrogate models from process simulations.•Surrogate models make technoeconomic models more accessible and fast to run.•Surrogate models are ...most useful when further design changes will not be made.•Automated design strategies can be complementary to machine learning approaches.•Advanced sampling strategies may yield further performance improvements.
Technoeconomic analysis and life-cycle assessment are critical to guiding and prioritizing bench-scale experiments and to evaluating economic and environmental performance of biofuel or biochemical production processes at scale. Traditionally, commercial process simulation tools have been used to develop detailed models for these purposes. However, developing and running such models can be costly and computationally intensive, which limits the degree to which they can be shared and reproduced in the broader research community. This study evaluates the potential of an automated machine learning approach to develop surrogate models based on conventional process simulation models. The analysis focuses on several high-value biofuels and bioproducts for which pathways of production from biomass feedstocks have been well-established. The results demonstrate that surrogate models can be an accurate and effective tool for approximating the cost, mass and energy balance outputs of more complex process simulations at a fraction of the computational expense.
The Renewable Fuel Standard (RFS) initially set ambitious goals for US cellulosic biofuel production and, although the total renewable fuel volume reached 80% of the established target for 2017, the ...cellulosic fuel volume reached just 5% of the original goal. This shortfall has, in part, been ascribed to the hesitance of farmers to plant the high-yielding, low-input perennial biomass crops identified as otherwise ideal feedstocks. Policy and market uncertainty also hinder investment in capital-intensive new cellulosic biorefineries. This study combines remote sensing land use data, yield predictions, a fine-resolution geospatial modeling framework, and a novel facility siting algorithm to evaluate the potential for near-term scale-up of cellulosic fuel production using a combination of lower-risk annual feedstocks more familiar to US farmers: corn stover and biomass sorghum. Potential strategies include expansion or retrofitting of existing corn ethanol facilities and targeted construction of new facilities in resource-rich areas. The results indicate that, with a maximum 10% conversion of pastureland and cropland to sorghum in suitable regions, more than 80 of the 214 existing corn ethanol biorefineries could be retrofitted or expanded to accept cellulosic feedstocks and an additional 71 new biorefineries could be built. The resulting land conversion for bioenergy sorghum totals to 4.5% of US cropland and 3.7% of pastureland. If this biomass is converted to ethanol, the total increase in annual production could be 17 billion gallons, just over the original RFS 2022 cellulosic biofuel production target and equivalent to 12% of US gasoline consumption.
Diurnal fluctuations in power output have been well established with power loss typically occurring in morning (AM) times. Beetroot juice (BRJ) is a source of dietary nitrate that possess ergogenic ...properties, but it is unknown if ingestion can mitigate performance decrements in the morning. The purpose of this study was to examine the effects of acute BRJ supplementation on diurnal fluctuations in anaerobic performance in trained sprinters. Male Division 1 National Collegiate Athletic Association (NCAA) sprinters (
= 10) participated. In a double-blinded crossover study design, participants completed three counterbalanced exercise trials under different conditions: Morning-placebo (8:00 HR, AM-PL), Morning-BRJ (8:00 HR, AM-BRJ), and Afternoon-no supplement (15:00 HR, PM). For each trial, participants completed 3 × 15 s Wingate anaerobic tests separated by 2 min of rest. Each trial was separated by a 72 h washout period. Mean power output (
= 0.043), anaerobic capacity (
= 0.023), and total work (
= 0.026) were significantly lower with the AM-PL condition compared to PM. However, BRJ supplementation prevented AM losses of mean power output (
= 0.994), anaerobic capacity (
= 0.941), and total work (
= 0.933) in the AM-BRJ compared to the PM condition. Rate of perceived exertion was not significantly different between any conditions (
= 0.516). Heart rate was significantly lower during the AM-BRJ condition compared to AM-PL (
= 0.030) and PM (
< 0.001). Findings suggest anaerobic capacity suffers during AM versus PM times in trained sprinters, but BRJ ingestion abolishes AM-associated decrements in performance.
Groundwater dependent ecosystems (GDEs) rely on near-surface groundwater. These systems are receiving more attention with rising air temperature, prolonged drought, and where groundwater pumping ...captures natural groundwater discharge for anthropogenic use. Phreatophyte shrublands, meadows, and riparian areas are GDEs that provide critical habitat for many sensitive species, especially in arid and semi-arid environments. While GDEs are vital for ecosystem services and function, their long-term (i.e. ~30years) spatial and temporal variability is poorly understood with respect to local and regional scale climate, groundwater, and rangeland management. In this work, we compute time series of NDVI derived from sensors of the Landsat TM, ETM+, and OLI lineage for assessing GDEs in a variety of land and water management contexts. Changes in vegetation vigor based on climate, groundwater availability, and land management in arid landscapes are detectable with Landsat. However, the effective quantification of these ecosystem changes can be undermined if changes in spectral bandwidths between different Landsat sensors introduce biases in derived vegetation indices, and if climate, and land and water management histories are not well understood. The objective of this work is to 1) use the Landsat 8 under-fly dataset to quantify differences in spectral reflectance and NDVI between Landsat 7 ETM+ and Landsat 8 OLI for a range of vegetation communities in arid and semiarid regions of the southwestern United States, and 2) demonstrate the value of 30-year historical vegetation index and climate datasets for assessing GDEs. Specific study areas were chosen to represent a range of GDEs and environmental conditions important for three scenarios: baseline monitoring of vegetation and climate, riparian restoration, and groundwater level changes. Google's Earth Engine cloud computing and environmental monitoring platform is used to rapidly access and analyze the Landsat archive along with downscaled North American Land Data Assimilation System gridded meteorological data, which are used for both atmospheric correction and correlation analysis. Results from the cross-sensor comparison indicate a benefit from the application of a consistent atmospheric correction method, and that NDVI derived from Landsat 7 and 8 are very similar within the study area. Results from continuous Landsat time series analysis clearly illustrate that there are strong correlations between changes in vegetation vigor, precipitation, evaporative demand, depth to groundwater, and riparian restoration. Trends in summer NDVI associated with riparian restoration and groundwater level changes were found to be statistically significant, and interannual summer NDVI was found to be moderately correlated to interannual water-year precipitation for baseline study sites. Results clearly highlight the complementary relationship between water-year PPT, NDVI, and evaporative demand, and are consistent with regional vegetation index and complementary relationship studies. This work is supporting land and water managers for evaluation of GDEs with respect to climate, groundwater, and resource management.
•Differences in Landsat 7 and 8 reflectance and NDVI are shown with under-fly data.•Cloud computing used to process Landsat, climate, and meteorological archives.•Landsat and climate archives used to assess long term phreatophyte changes.•NDVI trends related to climate, groundwater, and resource management changes•Complementary relationship between precipitation, evaporative demand, and NDVI
PDF
