| 1. |
- Bang, Le T., et al.
(författare)
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Synthesis and assessment of metallic ion migration through a novel calcium carbonate coating for biomedical implants
- 2020
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Ingår i: Journal of Biomedical Materials Research. Part B - Applied biomaterials. - : Wiley. - 1552-4973 .- 1552-4981. ; 108:2, s. 429-438
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Tidskriftsartikel (refereegranskat)abstract
- Titanium (Ti) implants are commonly regarded as well accepted by the body. However, metal ion release is still a cause for concern. A small decrease in pH, which can be caused by inflammation, may produce a large increase in the corrosion rate of Ti implants. Coating the alloy with a buffer layer could have a significant protective effect. In this study, a calcium carbonate coating was developed on commercially pure Ti and a Ti-6Al-4V alloy through a hydrothermal treatment of previously NaOH-treated surfaces in calcium-citric acid chelate complexes. The results showed that a superstructured calcite coating layer formed on the Ti substrate after treatment at 170 degrees C for 3 hr. The coating was approx. 1 mu m thick and covered the substrate surface uniformly. When prolonging the hydrothermal treatment from 5 hr to 24 hr, the rhombohedral structure of calcite was observed in addition to the superstructure of calcite. Dissolution test results showed no significant differences in solution pH between the coated- and un-coated samples. However, the CaCO3 coating reduced by approx. 2-5 times the Ti and V ion release from the substrate as compared to the uncoated material, at pH 4. CaCO3 and hydroxyapatite (HA) coatings gave nonsignificant effects at neutral pH although the HA coating showed a trend for better results at the longer time points. The reduction in metal ion release from the substrate and the buffering ability of the CaCO3 coating encourage further studies on this coating for clinical applications.
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| 2. |
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| 3. |
- Kurilshikov, Alexander, et al.
(författare)
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Large-scale association analyses identify host factors influencing human gut microbiome composition
- 2021
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 53:2, s. 156-165
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Tidskriftsartikel (refereegranskat)abstract
- To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 of 410 genera were detected in more than 95% of samples. A genome-wide association study of host genetic variation regarding microbial taxa identified 31 loci affecting the microbiome at a genome-wide significant (P < 5 x 10(-8)) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (genome-wide association study signal: P = 1.28 x 10(-20)), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95 x 10(-10) < P < 5 x 10(-8)) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome might have causal effects in ulcerative colitis and rheumatoid arthritis.
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| 4. |
- Nguyen, Quang Hung, et al.
(författare)
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Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
- 2021
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Ingår i: Mathematical problems in engineering (Print). - UK : Hindawi Publishing Corporation. - 1024-123X .- 1563-5147. ; 2021, s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive capability of the models under different training and testing ratios. Besides, Monte Carlo simulation was simultaneously carried out to evaluate the performance of the proposed models, taking into account the random sampling effect. The results showed that although all three ML models performed well, the ANN was the most accurate and statistically stable model after 1000 Monte Carlo simulations (Mean R = 0.9348) compared with other models such as Boosted (Mean R = 0.9192) and ELM (Mean R = 0.8703). Investigation on the performance of the models showed that the predictive capability of the ML models was greatly affected by the training/testing ratios, where the 70/30 one presented the best performance of the models. Concisely, the results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.
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| 5. |
- Pham, Binh Thai, et al.
(författare)
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A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil
- 2020
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Ingår i: Sustainability. - Switzerland : MDPI. - 2071-1050. ; 12:6
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Tidskriftsartikel (refereegranskat)abstract
- Determination of shear strength of soil is very important in civilengineering for foundation design, earth and rock fill dam design, highway and airfield design,stability of slopes and cuts, and in the design of coastal structures. In this study, a novel hybrid softcomputing model (RF-PSO) of random forest (RF) and particle swarm optimization (PSO) wasdeveloped and used to estimate the undrained shear strength of soil based on the clay content (%),moisture content (%), specific gravity (%), void ratio (%), liquid limit (%), and plastic limit (%). Inthis study, the experimental results of 127 soil samples from national highway project Hai Phong-Thai Binh of Vietnam were used to generate datasets for training and validating models. Pearsoncorrelation coefficient (R) method was used to evaluate and compare performance of the proposedmodel with single RF model. The results show that the proposed hybrid model (RF-PSO) achieveda high accuracy performance (R = 0.89) in the prediction of shear strength of soil. Validation of themodels also indicated that RF-PSO model (R = 0.89 and Root Mean Square Error (RMSE) = 0.453) issuperior to the single RF model without optimization (R = 0.87 and RMSE = 0.48). Thus, theproposed hybrid model (RF-PSO) can be used for accurate estimation of shear strength which canbe used for the suitable designing of civil engineering structures.
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| 6. |
- Pham, Binh Thai, et al.
(författare)
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Extreme learning machine based prediction of soil shear strength : A sensitivity analysis using Monte Carlo simulations and feature backward elimination
- 2020
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 12:6, s. 1-29
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Tidskriftsartikel (refereegranskat)abstract
- Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In this study, the main objective is to analyze the sensitivity of an advanced ML method, namely the Extreme Learning Machine (ELM) algorithm under different feature selection scenarios for prediction of shear strength of soil. Feature backward elimination supported by Monte Carlo simulations was applied to evaluate the importance of factors used for the modeling. A database constructed from 538 samples collected from Long Phu 1 power plant project was used for analysis. Well-known statistical indicators, such as the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE), were utilized to evaluate the performance of the ELM algorithm. In each elimination step, the majority vote based on six elimination indicators was selected to decide the variable to be excluded. A number of 30,000 simulations were conducted to find out the most relevant variables in predicting the shear strength of soil using ELM. The results show that the performance of ELM is good but very different under different combinations of input factors. The moisture content, liquid limit, and plastic limit were found as the most critical variables for the prediction of shear strength of soil using the ML model.
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| 7. |
- Reverté, Sara, et al.
(författare)
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National records of 3000 European bee and hoverfly species : A contribution to pollinator conservation
- 2023
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Ingår i: Insect Conservation and Diversity. - 1752-458X. ; 16:6, s. 758-775
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Tidskriftsartikel (refereegranskat)abstract
- Pollinators play a crucial role in ecosystems globally, ensuring the seed production of most flowering plants. They are threatened by global changes and knowledge of their distribution at the national and continental levels is needed to implement efficient conservation actions, but this knowledge is still fragmented and/or difficult to access. As a step forward, we provide an updated list of around 3000 European bee and hoverfly species, reflecting their current distributional status at the national level (in the form of present, absent, regionally extinct, possibly extinct or non-native). This work was attainable by incorporating both published and unpublished data, as well as knowledge from a large set of taxonomists and ecologists in both groups. After providing the first National species lists for bees and hoverflies for many countries, we examine the current distributional patterns of these species and designate the countries with highest levels of species richness. We also show that many species are recorded in a single European country, highlighting the importance of articulating European and national conservation strategies. Finally, we discuss how the data provided here can be combined with future trait and Red List data to implement research that will further advance pollinator conservation.
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