Pruning-Based Oversampling Technique with Smoothed Bootstrap Resampling for Imbalanced Clinical Dataset of Covid-19

Abstract In the present work, supercritical fluid extraction (SFE) of hemp (Cannabis sativa) seed oil at various ranges of SFE parameters is performed. These parameters and respective ranges are temperature (40–80) °C, pressure (200–350) bar, solvent (CO2) flow rate (5–15) g/min, particle size (0.43–1.02) mm and amount of co-solvent (ethanol) (0–10) % of solvent flow rate. Central composite design (CCD) suggests 32 experimental runs to perform through SFE. The obtained oil is analysed through gas chromatography to identify its fatty acids concentrations. The ratio of ω-6 linoleic and ω-3 α-linolenic fatty acids (ω-6/ω-3) is optimized through CCD to obtain the desired amount of 3:1 as this ratio is highly preferred for various health benefits. Ratio of ω-6/ω-3 is obtained in the range from 2.11 to 3.06:1 for all experimental runs. The effect of SFE parameters on this ratio is investigated. Further, cross-validation is peformed on the experimental data obtained for the concentrations of both fatty acids by jackknife and bootstrap resampling to authenticate the obtained data. Small value of standard deviation (~1), less standard error of the mean (SEM) (<0.8) and less variance coefficient (<0.11) confirms the validity of the obtained data. All the estimators’ values such as standard deviation, variance coefficients and SEM are observed in 95 % of confidence intervals.

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A methodology and clinical dataset with ground-truth to evaluate registration accuracy quantitatively in computer-assisted Laparoscopic Liver Resection

Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization ◽

10.1080/21681163.2021.1997642 ◽

2021 ◽

pp. 1-10

Author(s):

N. Rabbani ◽

L. Calvet ◽

Y. Espinel ◽

B. Le Roy ◽

M. Ribeiro ◽

...

Keyword(s):

Liver Resection ◽

Laparoscopic Liver Resection ◽

Ground Truth ◽

Computer Assisted ◽

Registration Accuracy ◽

Clinical Dataset

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A Novel Nomogram for Individualized Gonadotropin Starting Dose in GnRH Antagonist Protocol

Frontiers in Endocrinology ◽

10.3389/fendo.2021.688654 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yubin Li ◽

Yuwei Duan ◽

Xi Yuan ◽

Bing Cai ◽

Yanwen Xu ◽

...

Keyword(s):

Regression Model ◽

Ovarian Stimulation ◽

Multivariate Regression ◽

Gnrh Antagonist ◽

Bootstrap Resampling ◽

Multivariate Regression Model ◽

Starting Dose ◽

Gnrh Antagonist Protocol ◽

Antagonist Protocol

Controlled ovarian stimulation (COS) is one of the most vital parts of in vitro fertilization-embryo transfer (IVF-ET). At present, no matter what kinds of COS protocols are used, clinicians have to face the challenge of selection of gonadotropin starting dose. Although several nomograms have been developed to calculate the appropriate gonadotropin starting dose in gonadotropin releasing hormone (GnRH) agonist protocol, no nomogram was suitable for GnRH antagonist protocol. This study aimed to develop a predictive nomogram for individualized gonadotropin starting dose in GnRH antagonist protocol. Single-center prospective cohort study was conducted, with 198 women aged 20-45 years underwent IVF/intracytoplasmic sperm injection (ICSI)-ET cycles. Blood samples were collected on the second day of the menstrual cycle. All women received ovarian stimulation using GnRH antagonist protocol. Univariate and multivariate analysis were performed to identify predictive factors of ovarian sensitivity (OS). A nomogram for gonadotropin starting dose was developed based on the multivariate regression model. Validation was performed using concordance statistics and bootstrap resampling. A multivariate regression model based on serum anti-Müllerian hormone (AMH) level, antral follicle count (AFC), and body mass index (BMI) was developed and accounted for 59% of the variability of OS. An easy-to-use predictive nomogram for gonadotropin starting dose was established with excellent accuracy. The concordance index (C-index) of the nomogram was 0.833 (95% CI, 0.829-0.837). Internal validation using bootstrap resampling further showed the good performance of the nomogram. In conclusion, gonadotropin starting dose in antagonist protocol can be predicted precisely by a novel nomogram.

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Application of Bootstrap Resampling Technique to Obtain Confidence Interval for Prostate Specific Antigen (PSA) Screening Age

American Journal of Biomedical Science & Research ◽

10.34297/ajbsr.2020.09.001379 ◽

2020 ◽

Vol 9 (2) ◽

pp. 177-179

Author(s):

Shabih Manzar

Keyword(s):

Confidence Interval ◽

Prostate Specific Antigen ◽

Specific Antigen ◽

Bootstrap Resampling ◽

Psa Screening

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RESIDUAL BOOTSTRAP RESAMPLING METHOD FOR MULTIPLE LINEAR REGRESSION MODEL PARAMETER ESTIMATION

Jurnal Litbang Edusaintech ◽

10.51402/jle.v1i1.8 ◽

2020 ◽

Vol 1 (1) ◽

pp. 35-43

Author(s):

Fajar Prihatmono ◽

Moh Yamin Darsyah ◽

Abdul Karim

Keyword(s):

Parameter Estimation ◽

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Multiple Linear Regression Model ◽

Bootstrap Resampling ◽

Model Parameter ◽

Model Parameter Estimation ◽

Resampling Method

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Analysis of stress intensity factor for fatigue crack using bootstrap S-version finite element model

International Journal of Structural Integrity ◽

10.1108/ijsi-10-2019-0108 ◽

2020 ◽

Vol 11 (4) ◽

pp. 579-589

Author(s):

Muhamad Husnain Mohd Noh ◽

Mohd Akramin Mohd Romlay ◽

Chuan Zun Liang ◽

Mohd Shamil Shaari ◽

Akiyuki Takahashi

Keyword(s):

Stress Intensity Factor ◽

Finite Element ◽

Finite Element Model ◽

Simulation Analysis ◽

Element Model ◽

Bootstrap Resampling ◽

Confidence Bounds ◽

Content Type ◽

Resampling Method ◽

The Mean

PurposeFailure of the materials occurs once the stress intensity factor (SIF) overtakes the material fracture toughness. At this level, the crack will grow rapidly resulting in unstable crack growth until a complete fracture happens. The SIF calculation of the materials can be conducted by experimental, theoretical and numerical techniques. Prediction of SIF is crucial to ensure safety life from the material failure. The aim of the simulation study is to evaluate the accuracy of SIF prediction using finite element analysis.Design/methodology/approachThe bootstrap resampling method is employed in S-version finite element model (S-FEM) to generate the random variables in this simulation analysis. The SIF analysis studies are promoted by bootstrap S-version Finite Element Model (BootstrapS-FEM). Virtual crack closure-integral method (VCCM) is an important concept to compute the energy release rate and SIF. The semielliptical crack shape is applied with different crack shape aspect ratio in this simulation analysis. The BootstrapS-FEM produces the prediction of SIFs for tension model.FindingsThe mean of BootstrapS-FEM is calculated from 100 samples by the resampling method. The bounds are computed based on the lower and upper bounds of the hundred samples of BootstrapS-FEM. The prediction of SIFs is validated with Newman–Raju solution and deterministic S-FEM within 95 percent confidence bounds. All possible values of SIF estimation by BootstrapS-FEM are plotted in a graph. The mean of the BootstrapS-FEM is referred to as point estimation. The Newman–Raju solution and deterministic S-FEM values are within the 95 percent confidence bounds. Thus, the BootstrapS-FEM is considered valid for the prediction with less than 6 percent of percentage error.Originality/valueThe bootstrap resampling method is employed in S-FEM to generate the random variables in this simulation analysis.

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