Optimum second call imputation in PPS sampling.
Ontology highlight
ABSTRACT:
SUBMITTER: Sohil F
PROVIDER: S-EPMC8782356 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Widely recognized in many fields including economics, engineering, epidemiology, health sciences, technology and wildlife management, length-biased sampling generates biased and right-censored data but often provide the best information available for statistical inference. Different from traditional right-censored data, length-biased data have unique aspects resulting from their sampling procedures. We exploit these unique aspects and propose a general imputation-based estimation method for analyzing length-biased data under a class of flexible semiparametric transformation models. We present new computational algorithms that can jointly estimate the regression coefficients and the baseline function semiparametrically. The imputation-based method under the transformation model provides an unbiased estimator regardless whether the censoring is independent or not on the covariates. We establish large-sample properties using the empirical processes method. Simulation studies show that under small to moderate sample sizes, the proposed procedure has smaller mean square errors than two existing estimation procedures. Finally, we demonstrate the estimation procedure by a real data example.
| S-EPMC3440536 | biostudies-literature
Project description:The development of next-generation sequencing technology has enabled researchers to explore and understand the gut microbiome from a broader and deeper perspective. However, the results of different studies on gut microbiota are highly variable even in the same disease, which makes it difficult to guide clinical diagnosis and treatment. The ideal sampling method should be non-invasive, involve little cross-contamination or bowel preparation, and collect gut microbiota at different sites. Currently, sequencing technologies are usually based on samples collected from feces, mucosal biopsy, intestinal fluid, etc. However, different parts of the gastrointestinal tract possess various physiological characteristics that are essential for particular species of living microbiota. Moreover, current sampling methods are somewhat defective. For example, fecal samples are just a proxy for intestinal microbiota, while biopsies are invasive for patients and not suitable for healthy controls. In this review, we summarize the current sampling methods and their advantages and shortcomings. New sampling technologies, such as the Brisbane Aseptic Biopsy Device and the intelligent capsule, are also mentioned to inspire the development of future precise description methods of the gut microbiome.
| S-EPMC7161087 | biostudies-literature
Project description:BackgroundThe use of visible-near infrared (vis-NIR) spectroscopy for rapid soil characterisation has gained a lot of interest in recent times. Soil spectra absorbance from the visible-infrared range can be calibrated using regression models to predict a set of soil properties. The accuracy of these regression models relies heavily on the calibration set. The optimum sample size and the overall sample representativeness of the dataset could further improve the model performance. However, there is no guideline on which sampling method should be used under different size of datasets.MethodsHere, we show different sampling algorithms performed differently under different data size and different regression models (Cubist regression tree and Partial Least Square Regression (PLSR)). We analysed the effect of three sampling algorithms: Kennard-Stone (KS), conditioned Latin Hypercube Sampling (cLHS) and k-means clustering (KM) against random sampling on the prediction of up to five different soil properties (sand, clay, carbon content, cation exchange capacity and pH) on three datasets. These datasets have different coverages: a European continental dataset (LUCAS, n = 5,639), a regional dataset from Australia (Geeves, n = 379), and a local dataset from New South Wales, Australia (Hillston, n = 384). Calibration sample sizes ranging from 50 to 3,000 were derived and tested for the continental dataset; and from 50 to 200 samples for the regional and local datasets.ResultsOverall, the PLSR gives a better prediction in comparison to the Cubist model for the prediction of various soil properties. It is also less prone to the choice of sampling algorithm. The KM algorithm is more representative in the larger dataset up to a certain calibration sample size. The KS algorithm appears to be more efficient (as compared to random sampling) in small datasets; however, the prediction performance varied a lot between soil properties. The cLHS sampling algorithm is the most robust sampling method for multiple soil properties regardless of the sample size.DiscussionOur results suggested that the optimum calibration sample size relied on how much generalization the model had to create. The use of the sampling algorithm is beneficial for larger datasets than smaller datasets where only small improvements can be made. KM is suitable for large datasets, KS is efficient in small datasets but results can be variable, while cLHS is less affected by sample size.
| S-EPMC6173947 | biostudies-literature
Project description:Epileptic seizure is the common neurological disorder, which is generally identified by electroencephalogram (EEG) signals. In this paper, a new feature extraction methodology based on optimum allocation sampling (OAS) and Teager energy operator (TEO) is proposed for detection of seizure EEG signals. The OAS scheme selects the finite length homogeneous sequence from non-homogeneous recorded EEG signal. The trend of selected sequence by OAS is still non-linear, which is analyzed by non-linear operator TEO. The TEO convert non-linear but homogenous EEG sequence into amplitude-frequency modulated (AM-FM) components. The statistical measures of AM-FM components used as input features to least squares support vector machine classifier for classification of seizure and seizure-free EEG signals. The proposed methodology is evaluated on a benchmark epileptic seizure EEG database. The experimental results demonstrate that the proposed scheme has capability to effectively distinguish seizure and seizure-free EEG signals.
| S-EPMC5660011 | biostudies-other
Project description:Common ALL (cALL) is the most frequent entity of childhood ALL and carries an early pre-B cell phenotype. Expression patterns of 25 pediatric cALL samples were analyzed by use of high-density DNA microarrays HG-U133A. Leukemic patients’ bone marrow samples were compared to sorted B cells from cord blood of healthy donors expressing CD19 and CD10 surface antigens. Differential gene expression profiling of pediatric cALL versus non-malignant tissues enabled the identification of aberrantly expressed genes in malignant cells, facilitating discrimination of leukemic from normal cells and possibly revealing specific disease mechanisms. Principal component analysis clearly distinguished leukemia samples from normal controls. Significance analysis of microarrays revealed 487 genes significantly up-regulated, and 572 down-regulated genes in leukemic cells. A comparison to previous publications investigating genetically defined subsets of cALL revealed 465 genes previously not associated with cALL. Interestingly, terminal deoxynucleotidyl-transferase (DNTT) as well as in the context of cALL unknown genes, were found to be the strongest predictive genes for the malignant phenotype signifying the diagnostic value of our approach.
2012-06-30 | GSE34670 | GEO
Project description: Not available
| S-EPMC7675239 | biostudies-literature
Project description:Seismological constraints obtained from receiver function (RF) analysis provide important information about the crust and mantle structure. Here, we explore the utility of the free-surface multiple of the P-wave (PP) and the corresponding conversions in RF analysis. Using earthquake records, we demonstrate the efficacy of PPs-RFs before illustrating how they become especially useful when limited data is available in typical planetary missions. Using a transdimensional hierarchical Bayesian deconvolution approach, we compute robust P-to-S (Ps)- and PPs-RFs with InSight recordings of five marsquakes. Our Ps-RF results verify the direct Ps converted phases reported by previous RF analyses with increased coherence and reveal other phases including the primary multiple reverberating within the uppermost layer of the Martian crust. Unlike the Ps-RFs, our PPs-RFs lack an arrival at 7.2 s lag time. Whereas Ps-RFs on Mars could be equally well fit by a two- or three-layer crust, synthetic modeling shows that the disappearance of the 7.2 s phase requires a three-layer crust, and is highly sensitive to velocity and thickness of intra-crustal layers. We show that a three-layer crust is also preferred by S-to-P (Sp)-RFs. While the deepest interface of the three-layer crust represents the crust-mantle interface beneath the InSight landing site, the other two interfaces at shallower depths could represent a sharp transition between either fractured and unfractured materials or thick basaltic flows and pre-existing crustal materials. PPs-RFs can provide complementary constraints and maximize the extraction of information about crustal structure in data-constrained circumstances such as planetary missions.
| S-EPMC8597591 | biostudies-literature
Project description:BackgroundThe point prevalence survey of healthcare-associated infections (HAIs) and antimicrobial use organized by the European Centre for Disease Prevention and Control (ECDC-PPS) and the Global Point Prevalence Survey of antimicrobial consumption (Global-PPS) were simultaneously performed in Belgian acute care hospitals in 2017.MethodsBelgian acute care hospitals were invited to participate in either the ECDC or Global-PPS. Hospital/ward/patient-level data were collected between September-December 2017. All patients present in the wards at 8 a.m. on the day of the PPS were included. The data of the ECDC and Global-PPS on antimicrobial consumption were pooled. Detailed data on HAIs were analysed for ECDC-PPS.ResultsOverall, 110 Belgian acute care hospital sites participated in the ECDC and Global-PPS (countrywide participation rate: 81.4%, 28,007 patients). Overall, a crude prevalence of patients with at least one antimicrobial of 27.1% (95% confidence interval (CI) 26.5-27.6%) was found. The most frequently reported indications were pneumonia (23.2%), urinary tract infections (15.2%) and skin and soft tissue infections (11.9%). The reason for antimicrobial use was recorded for 81.9% of the prescriptions, a stop/review date for 40.8% and compliance with local antibiotic guidelines for 76.6%. In the ECDC-PPS, the crude prevalence of patients with at least one HAI was 7.3% (95%CI 6.8-7.7%). Most frequently reported HAIs were pneumonia (21.6%) and urinary tract infections (21.3%).ConclusionsHAI and antimicrobial use prevalence remained stable in comparison with the previous PPS (7.1% and 27.4% in 2011 and 2015, respectively). Belgian hospitals should be further stimulated to set local targets to improve antibiotic prescribing and reduce HAI.
| S-EPMC6958935 | biostudies-literature
Project description:Using the Southern African Bird Atlas Project (SABAP2) as a case study, we examine the possible determinants of spatial bias in volunteer sampling effort and how well such biased data represent environmental gradients across the area covered by the atlas. For each province in South Africa, we used generalized linear mixed models to determine the combination of variables that explain spatial variation in sampling effort (number of visits per 5' × 5' grid cell, or "pentad"). The explanatory variables were distance to major road and exceptional birding locations or "sampling hubs," percentage cover of protected, urban, and cultivated area, and the climate variables mean annual precipitation, winter temperatures, and summer temperatures. Further, we used the climate variables and plant biomes to define subsets of pentads representing environmental zones across South Africa, Lesotho, and Swaziland. For each environmental zone, we quantified sampling intensity, and we assessed sampling completeness with species accumulation curves fitted to the asymptotic Lomolino model. Sampling effort was highest close to sampling hubs, major roads, urban areas, and protected areas. Cultivated area and the climate variables were less important. Further, environmental zones were not evenly represented by current data and the zones varied in the amount of sampling required representing the species that are present. SABAP2 volunteers' preferences in birding locations cause spatial bias in the dataset that should be taken into account when analyzing these data. Large parts of South Africa remain underrepresented, which may restrict the kind of ecological questions that may be addressed. However, sampling bias may be improved by directing volunteers toward undersampled regions while taking into account volunteer preferences.
| S-EPMC5587490 | biostudies-literature
Project description:Onboard measuring the electrochemical impedance spectroscopy (EIS) for lithium-ion batteries is a long-standing issue that limits the technologies such as portable electronics and electric vehicles. Challenges arise from not only the high sampling rate required by the Shannon Sampling Theorem but also the sophisticated real-life battery-using profiles. We here propose a fast and accurate EIS predicting system by combining the fractional-order electric circuit model-a highly nonlinear model with clear physical meanings-with a median-filtered neural network machine learning. Over 1000 load profiles collected under different state-of-charge and state-of-health are utilized for verification, and the root-mean-squared-error of our predictions could be bounded by 1.1 mΩ and 2.1 mΩ when using dynamic profiles last for 3 min and 10 s, respectively. Our method allows using size-varying input data sampled at a rate down to 10 Hz and unlocks opportunities to detect the battery's internal electrochemical characteristics onboard via low-cost embedded sensors.
| S-EPMC10291323 | biostudies-literature