scholarly journals Quantifying Global Potential Marginal Land Resources for Switchgrass

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6197
Author(s):  
Peiwei Fan ◽  
Mengmeng Hao ◽  
Fangyu Ding ◽  
Dong Jiang ◽  
Donglin Dong
Keyword(s):  
High Performance ◽  
Panicum Virgatum ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Global Scale ◽  
Marginal Land ◽  
Land Resources ◽  
Marginal Lands ◽  
Boosted Regression Tree ◽  
Biofuel Feedstock

Switchgrass (Panicum virgatum L.) with its advantages of low maintenance and massive distribution in temperate zones, has long been regarded as a suitable biofuel feedstock with a promising prospect. Currently, there is no validated assessment of marginal land for switchgrass growth on a global scale. Although, on both regional and national scale there have been several studies evaluating the potential marginal lands for growing switchgrass. To obtain the first global map that presents the distribution of switchgrass growing in potential marginal land, we employed a boosted regression tree (BRT) modeling procedure integrated with released switchgrass records along with a series of high-spatial-resolution environmental variables. The result shows that the available marginal land resources satisfying switchgrass growing demands are mainly distributed in the southern and western parts of North America, coastal areas in the southern and eastern parts of South America, central and southern Africa, and northern Oceania, approximately 2229.80 million hectares. Validation reveals that the ensembled BRT models have a considerably high performance (area under the curve: 0.960). According to our analysis, annual cumulative precipitation accounts for 45.84% of the full impact on selecting marginal land resources for switchgrass, followed by land cover (14.97%), maximum annual temperature (12.51%), and mean solar radiation (10.25%). Our findings bring a new perspective on the development of biofuel feedstock.

scholarly journals Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

2018 ◽  
Vol 8 (8) ◽  
pp. 1369 ◽  
Author(s):  
Alireza Arabameri ◽  
Biswajeet Pradhan ◽  
Hamid Reza Pourghasemi ◽  
Khalil Rezaei ◽  
Norman Kerle
Keyword(s):  
Data Mining ◽  
Spatial Relationship ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Drainage Density ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Boosted Regression Tree ◽  
Area Index

Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.

scholarly journals Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment

2020 ◽  
Vol 12 (21) ◽  
pp. 3620
Author(s):  
Indrajit Chowdhuri ◽  
Subodh Chandra Pal ◽  
Alireza Arabameri ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Land Use Changes ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Gully Erosion ◽  
Support Vector ◽  
Boosted Regression Tree ◽  
Conditioning Factors ◽  
Susceptibility Maps ◽  
Chotanagpur Plateau ◽  
Additive Regression

The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study.

scholarly journals Spatial Mapping of the Groundwater Potential of the Geum River Basin Using Ensemble Models Based on Remote Sensing Images

2019 ◽  
Vol 11 (19) ◽  
pp. 2285 ◽  
Author(s):  
Jeong-Cheol Kim ◽  
Hyung-Sup Jung ◽  
Saro Lee
Keyword(s):  
Remote Sensing ◽  
Specific Capacity ◽  
Area Under The Curve ◽  
Regression Tree ◽  
Groundwater Potential ◽  
The Other ◽  
Thematic Maps ◽  
Remote Sensing Images ◽  
Boosted Regression Tree ◽  
Productivity Data

This study analyzed the Groundwater Productivity Potential (GPP) of Okcheon city, Korea, using three different models. Two of these three models are data mining models: Boosted Regression Tree (BRT) model and Random Forest (RF) model. The other model is the Logistic Regression (LR) model. The three models are based on the relationship between groundwater-productivity data (specific capacity (SPC) and transmissivity (T)) and the related hydro-geological factors from thematic maps, such as topography, lineament, geology, land cover, and etc. The thematic maps which are generated from the remote sensing images. Groundwater productivity data were collected from 86 wells locations. The resulting GPP maps were validated through area-under-the-curve (AUC) analysis using wells data that had not been used for training the model. When T was used in the BRT, RF, and LR models, the obtained GPP maps had 81.66%, 80.21%, and 85.04% accuracy, respectively, and when SPC was used, the maps had 81.53%, 78.57%, and 82.22% accuracy, respectively. The LR model, which is a statistical model, showed the highest verification accuracy, also the other two models showed high accuracies. These observations indicate that all three models can be useful for groundwater resource development.

Drivers of diversity gradients of a highly mobile marine assemblage in a mesoscale seascape

2020 ◽  
Vol 638 ◽  
pp. 149-164
Author(s):  
GM Svendsen ◽  
M Ocampo Reinaldo ◽  
MA Romero ◽  
G Williams ◽  
A Magurran ◽  
...  
Keyword(s):  
Environmental Gradients ◽  
Regression Tree ◽  
Demersal Fish ◽  
Open Ocean ◽  
Shared Resources ◽  
Thermal Front ◽  
Boosted Regression Tree ◽  
Diversity Gradients ◽  
Α Diversity ◽  
Bottom Depth

With the unprecedented rate of biodiversity change in the world today, understanding how diversity gradients are maintained at mesoscales is a key challenge. Drawing on information provided by 3 comprehensive fishery surveys (conducted in different years but in the same season and with the same sampling design), we used boosted regression tree (BRT) models in order to relate spatial patterns of α-diversity in a demersal fish assemblage to environmental variables in the San Matias Gulf (Patagonia, Argentina). We found that, over a 4 yr period, persistent diversity gradients of species richness and probability of an interspecific encounter (PIE) were shaped by 3 main environmental gradients: bottom depth, connectivity with the open ocean, and proximity to a thermal front. The 2 main patterns we observed were: a monotonic increase in PIE with proximity to fronts, which had a stronger effect at greater depths; and an increase in PIE when closer to the open ocean (a ‘bay effect’ pattern). The originality of this work resides on the identification of high-resolution gradients in local, demersal assemblages driven by static and dynamic environmental gradients in a mesoscale seascape. The maintenance of environmental gradients, specifically those associated with shared resources and connectivity with an open system, may be key to understanding community stability.

scholarly journals Sphingolipid Analysis Indicate Lactosylceramide as a Potential Biomarker of Inflammatory Bowel Disease in Children

Biomolecules ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1083
Author(s):  
Aleksandra Filimoniuk ◽  
Agnieszka Blachnio-Zabielska ◽  
Monika Imierska ◽  
Dariusz Marek Lebensztejn ◽  
Urszula Daniluk
Keyword(s):  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
High Performance ◽  
Area Under The Curve ◽  
Study Groups ◽  
Diagnostic Value ◽  
Serum Samples ◽  
Specificity And Sensitivity ◽  
Potential Biomarker ◽  
Inflammatory Bowel

An altered ceramide composition in patients with inflammatory bowel disease (IBD) has been reported recently. The aim of this study was to evaluate the concentrations of sphingolipids in the serum of treatment-naive children with newly diagnosed IBD and to determine the diagnostic value of the tested lipids in pediatric IBD. The concentrations of sphingolipids in serum samples were evaluated using a quantitative method, an ultra-high-performance liquid chromatography-tandem mass spectrometry in children with Crohn’s disease (CD) (n=34), ulcerative colitis (UC) (n = 39), and controls (Ctr) (n = 24). Among the study groups, the most significant differences in concentrations were noted for C16:0-LacCer, especially in children with CD compared to Ctr or even to UC. Additionally, the relevant increase in C20:0-Cer and C18:1-Cer concentrations were detected in both IBD groups compared to Ctr. The enhanced C24:0-Cer level was observed only in UC, while C18:0-Cer only in the CD group. The highest area under the curve (AUC), specificity, and sensitivity were determined for C16:0-LacCer in CD diagnosis. Our results suggest that the serum LacC16-Cer may be a potential biomarker that distinguishes children with IBD from healthy controls and differentiates IBD subtypes. In addition, C20:0-Cer and C18:0-Cer levels also seem to be closely connected with IBD.

Regional Perspective of Safety Performance Functions and Their Application to Florida Intersections in Suburban Residential and Urban General Context Classification Categories

2021 ◽  
pp. 036119812110084
Author(s):  
Ghalia Gamaleldin ◽  
Haitham Al-Deek ◽  
Adrian Sandt ◽  
John McCombs ◽  
Alan El-Urfali
Keyword(s):  
Negative Binomial ◽  
Regression Tree ◽  
Influential Factors ◽  
Safety Performance ◽  
General Context ◽  
Boosted Regression Tree ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Intersection Types ◽  
The Individual

Safety performance functions (SPFs) are essential tools to help agencies predict crashes and understand influential factors. Florida Department of Transportation (FDOT) has implemented a context classification system which classifies intersections into eight context categories rather than the three classifications used in the Highway Safety Manual (HSM). Using this system, regional SPFs could be developed for 32 intersection types (unsignalized and signalized 3-leg and 4-leg for each category) rather than the 10 HSM intersection types. In this paper, eight individual intersection group SPFs were developed for the C3R-Suburban Residential and C4-Urban General categories and compared with full SPFs for these categories. These comparisons illustrate the unique and regional insights that agencies can gain by developing these individual SPFs. Poisson, negative binomial, zero-inflated, and boosted regression tree models were developed for each studied group as appropriate, with the best model selected for each group based on model interpretability and five performance measures. Additionally, a linear regression model was built to predict minor roadway traffic volumes for intersections which were missing these volumes. The full C3R and C4 SPFs contained four and six significant variables, respectively, while the individual intersection group SPFs in these categories contained six and nine variables. Factors such as major median, intersection angle, and FDOT District 7 regional variable were absent from the full SPFs. By developing individual intersection group SPFs with regional factors, agencies can better understand the factors and regional differences which affect crashes in their jurisdictions and identify effective treatments.

scholarly journals Validation and verification of predictive salivary biomarkers for oral health

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nagihan Bostanci ◽  
Konstantinos Mitsakakis ◽  
Beral Afacan ◽  
Kai Bao ◽  
Benita Johannsen ◽  
...  
Keyword(s):  
Oral Health ◽  
Predictive Accuracy ◽  
Statistical Tests ◽  
Area Under The Curve ◽  
Regression Tree ◽  
High Sensitivity ◽  
Oral Health Status ◽  
Classification And Regression Tree ◽  
Cart Analysis ◽  
Salivary Biomarkers

AbstractOral health is important not only due to the diseases emerging in the oral cavity but also due to the direct relation to systemic health. Thus, early and accurate characterization of the oral health status is of utmost importance. There are several salivary biomarkers as candidates for gingivitis and periodontitis, which are major oral health threats, affecting the gums. These need to be verified and validated for their potential use as differentiators of health, gingivitis and periodontitis status, before they are translated to chair-side for diagnostics and personalized monitoring. We aimed to measure 10 candidates using high sensitivity ELISAs in a well-controlled cohort of 127 individuals from three groups: periodontitis (60), gingivitis (31) and healthy (36). The statistical approaches included univariate statistical tests, receiver operating characteristic curves (ROC) with the corresponding Area Under the Curve (AUC) and Classification and Regression Tree (CART) analysis. The main outcomes were that the combination of multiple biomarker assays, rather than the use of single ones, can offer a predictive accuracy of > 90% for gingivitis versus health groups; and 100% for periodontitis versus health and periodontitis versus gingivitis groups. Furthermore, ratios of biomarkers MMP-8, MMP-9 and TIMP-1 were also proven to be powerful differentiating values compared to the single biomarkers.

scholarly journals Relationship between Plasma Trimethylamine N-Oxide Levels and Renal Dysfunction in Patients with Hypertension

2021 ◽  
pp. 1-12
Author(s):  
Jia Zhou ◽  
Dingkun Wang ◽  
Bingong Li ◽  
Xuelian Li ◽  
Xingjun Lai ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Kidney Disease ◽  
Renal Dysfunction ◽  
High Performance ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Hypertensive Patients ◽  
Ckd Stage ◽  
Liquid Chromatography Tandem Mass ◽  
The Relationship

<b><i>Introduction:</i></b> Trimethylamine N-oxide (TMAO) is a metabolite produced by gut bacteria. Although increased TMAO levels have been linked to hypertension (HTN) and chronic kidney disease (CKD) with poor prognosis, no clinical studies have directly addressed the relationship between them. In this study, we investigated the relationship between TMAO and renal dysfunction in hypertensive patients. <b><i>Methods:</i></b> We included healthy controls (<i>n</i> = 50), hypertensive patients (<i>n</i> = 46), and hypertensive patients with renal dysfunction (<i>n</i> = 143). Their blood pressure values were taken as the highest measured blood pressure. Renal function was evaluated using the estimated glomerular filtration rate. Plasma TMAO levels were measured using high-performance liquid chromatography tandem mass spectrometry. <b><i>Results:</i></b> We found significant differences in plasma TMAO levels among the 3 groups (<i>p</i> &#x3c; 0.01). The plasma TMAO of patients with HTN was significantly higher than that of healthy people, and the plasma TMAO of patients with HTN complicated by renal dysfunction was significantly higher than either of the other groups. Patients in the highest TMAO quartile were at a higher risk of developing CKD stage 5 than those in the lowest quartile. In the receiver operating characteristic curve, the area under the curve of TMAO combined with β 2-macroglobulin for predicting renal dysfunction in patients with HTN was 0.85 (95% confidence interval 0.80–0.90). <b><i>Conclusion:</i></b> An elevated TMAO level reflects higher levels of HTN and more severe renal dysfunction. TMAO, combined with β 2-macroglobulin levels, may assist in diagnosing CKD in hypertensive patients. Plasma TMAO has predictive value for early kidney disease in hypertensive patients.

scholarly journals PK/PD Analysis of Marbofloxacin by Monte Carlo Simulation against Mycoplasma agalactiae in Plasma and Milk of Lactating Goats after IV, SC and SC-Long Acting Formulations Administration

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1104
Author(s):  
Emilio Fernández-Varón ◽  
Edgar García-Romero ◽  
Juan M. Serrano-Rodríguez ◽  
Carlos M. Cárceles ◽  
Ana García-Galán ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
High Performance ◽  
Small Ruminants ◽  
Area Under The Curve ◽  
Hplc Method ◽  
Surrogate Markers ◽  
Contagious Agalactia ◽  
Lactating Goats ◽  
Long Acting

Contagious agalactia is a mycoplasmosis affecting small ruminants that have become an important issue in many countries. However, PK/PD studies of antibiotics to treat this problem in lactating goats affected by Mycoplasma (M.) agalactiae, the main CA-causing mycoplasma are almost non-existent. The aims of this study were to evaluate the plasma and milk disposition of marbofloxacin in lactating goats after intravenous (IV), subcutaneous (SC) and subcutaneous poloxamer P407 formulations with and without carboxy-methylcellulose (SC-P407-CMC and SC-P407) administration. Marbofloxacin concentrations were analysed by the High Performance Liquid Chromatography (HPLC) method. Minimum inhibitory concentrations (MIC) of M. agalactiae field isolates from mastitic goat’s milk were used to calculate surrogate markers of efficacy. Terminal half-lives of marbofloxacin after IV, SC, SC-P407 and SC-P407-CMC administration were 7.12, 6.57, 13.92 and 12.19 h in plasma, and the half-lives of elimination of marbofloxacin in milk were 7.22, 7.16, 9.30 and 7.74 h after IV, SC, SC-P407 and SC-P407-CMC administration, respectively. Marbofloxacin penetration from the blood into the milk was extensive, with Area Under the Curve (AUCmilk/AUCplasma) ratios ranged 1.04–1.23, and maximum concentrations (Cmax-milk/Cmax-plasma) ratios ranged 0.72–1.20. The PK/PD surrogate markers of efficacy fAUC24/MIC and the Monte Carlo simulation show that marbofloxacin ratio (fAUC24/MIC > 125) using a 90% of target attainment rate (TAR) need a dose regimen between 8.4 mg/kg (SC) and 11.57 mg/kg (P407CMC) and should be adequate to treat contagious agalactia in lactating goats.

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