Conventional Sampling Methods Research Articles

A pharmacometrics approach to assess the feasibility of capillary microsampling to replace venous sampling in clinical studies: Tafenoquine case study.

Microsampling has the advantage of smaller blood sampling volume and suitability in vulnerable populations compared to venous sampling in clinical pharmacokinetics studies. Current regulatory guidance requires correlative studies to enable microsampling as a technique. A post hoc population pharmacokinetic (POPPK) approach was utilized to investigate blood capillary microsampling as an alternative to venous sampling. Pharmacokinetic data from microsampling and venous sampling techniques during a paediatric study evaluating tafenoquine, a single-dose antimalarial for P. vivax, were used. Separate POPPK models were developed and validated based on goodness of fit and visual predictive checks, with pharmacokinetic data obtained via each sampling technique. Each POPPK model adequately described tafenoquine pharmacokinetics using a two-compartment model with body weight based on allometric scaling of clearance and volume of distribution. Tafenoquine pharmacokinetic parameter estimates including clearance (3.4 vs 3.7L/h) were comparable across models with slightly higher interindividual variability (38.3% vs 27%) in capillary microsampling-based data. A bioavailability/bioequivalence comparison demonstrated that the point estimate (90% CI) of capillary microsample versus venous sample model-based individual post hoc estimates for area under the concentration-time curve from time zero to infinity (AUC0-inf ) (100.7%, 98.0-103.5%) and Cmax (79.7%, 76.9-82.5%) met the 80-125% and 70-143% criteria, respectively. Overall, both POPPK models led to the same dose regimen recommendations across weight bins based on achieving target AUC. This analysis demonstrated that a POPPK approach can be employed to assess the performance of alternative pharmacokinetic sampling techniques. This approach provides a robust solution in scenarios where variability in pharmacokinetic data collected via venous sampling and microsampling may not result in a strong linear relationship. The findings also established that microsampling techniques may replace conventional venous sampling methods.

Diagnosis of SARS-CoV-2 infection from breath - a proof-of-concept study

Bioaerosol capture and analysis is emerging as a non-invasive diagnostic method for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this proof-of-concept study conducted in Lesotho, we evaluated the novel and simple AL2 bioaerosol detection device in comparison to conventional nasopharyngeal sampling methods. We demonstrated for the first time that SARS-CoV-2 can be detected using the AL2 bioaerosol capture device. However, studies with a larger sample size are needed to further evaluate this bioaerosol capture device for the detection of SARS-CoV-2.

Identification of soil type in Pakistan using remote sensing and machine learning.

Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning.

Coring Method for Dolomite Rocks With Well-Developed Joint Fissures Based on Permeability Reinforcement

A standardized preparation method for rock specimens is required before conducting mechanical tests. Conventional sampling methods produce good results for good-quality rocks. However, during the preparation of standard specimens of rocks found in areas with complex geological conditions, such as dolomite rocks with well-developed fractures, the internal fissures are easily disturbed, leading to sample preparation failure. Inspired by the effectiveness of organic resin solution-based reinforcements in protecting bone and porcelain artifacts during archaeological excavations, this study developed a method to prepare specimens for dolomite infiltration reinforcement using an acrylic resin-in-acetone solution, whereby the mechanical parameters of the rock samples could be recovered upon successful sample preparation. Several tests conducted under optimized ratios of acrylic resin to acetone revealed that a solution mass fraction of 20% produced the best effect on the reinforcement of fractured dolomite from the sampling location and increased the success rate of rock sample preparation from 15 to 54%. The physical and mechanical properties of the specimens after immersion in pure acetone for 48 hours were unaffected. The proposed method provides a reference for rock sampling from areas with developed joint fissures.

Visual inspection of surface defects of extreme size based on an advanced FCOS

ABSTRACT Surface defects of industrial products are generally detected through anchor-based object detection methods during manufacturing. However, these methods are prone to missed and false detection for ultra-elongated and ultra-fine defects. An advanced fully convolutional one-stage object detector (FCOS) is proposed. This method is based on an anchor-free FCOS network model. First, a novel type of center-ness is proposed to reduce the suppression of off-centered positions of defects of extreme size. In addition, to eliminate background interference, a self-adaptive center sampling method is proposed as a replacement for the conventional center sampling method. The regularization method and the loss function are also improved according to the defect characteristics. Experimental results show that this advanced-FCOS-based method outperforms anchor-based methodson the surface defect dataset. The proposed method effectively detects defects of extreme size without affecting the detection of normal defects. The performance of the proposed method meets the requirements of real industrial applications.

A new sampling method with zirconium‐loaded resin for phosphate oxygen isotope analysis in oligotrophic freshwater systems

The phosphate oxygen isotope ratio ( ) is a useful technique to trace the sources and biogeochemical cycles of phosphorus (P) in aquatic ecosystems. However, has not been widely used in oligotrophic freshwater systems due to technical and methodological difficulties in collecting sufficient phosphate (PO4 ) for the analysis, which sometimes requires hundreds of liters of the water sample. In this study, a new approach (PaS-Zir) was developed for the analysis in oligotrophic freshwater systems using zirconium (Zr)-loaded (ZrIRC) resin, which has a high affinity for PO4 . ZrClO2 was added to Amberlite IRC748 to obtain the ZrIRC resin. The adsorption/desorption experiment using KH2 PO4 with a known value of was conducted to determine the adsorption/desorption properties of the resin and the likelihood of isotopic fractionation. By installing mesh bags filled with the resin, the PaS-Zir approach was used in two rivers with low PO4 concentrations (0.2 and 5.3μmol/L). A conventional sampling method was also performed in the study river with a higher PO4 concentration to validate the efficacy of the PaS-Zir method. The adsorption/desorption experiment demonstrated that the ZrIRC resin possessed a sufficient adsorption capacity (153 μmol/resin-mL) and exhibited little isotopic fractionation during the adsorption/desorption processes. Using the PaS-Zir method, we were able to collect sufficient PO4 samples for the analysis from the rivers within at least 4 days of mesh bag installation. The values (14.2‰ ±0.2‰) obtained using the PaS-Zir method were comparable to those obtained using the conventional method (14.0‰ ±0.03‰). We proved that the PaS-Zir method is applicable to oligotrophic freshwater systems and is generally more efficient than the conventional method. In addition, our method is useful for improving the understanding of the P dynamics of oligotrophic ecosystems because of the extremely low concentration of PO4 commonly found in them, which are often prone to P pollution.

Uncertainty measurement-guided iterative sample selection via shallow convolutional neural network for hyperspectral image classification

Continuously improving the accuracy is a hot topic in hyperspectral image (HSI) classification with small-scale samples, due to the high label noise of traditional labeling systems and the high cost of expert labeling systems. We focus on constructing a smaller and more informative training sample set, so an iterative sample selection method guided by uncertainty measurement (ISS-Un) is proposed. The method learns shallow and deep features in the spectral and spatial domains via a convolutional neural network (CNN), where an uncertainty measurement algorithm such as least confidence (LC), marginal sampling (MS) or entropy (Ent) is used to iteratively select high-quality samples for the training set. In addition, we propose a more efficient uncertainty measurement algorithm named margin-entropy fusion (MEF) algorithm to integrate multiple-criteria information. The proposed method is compared with the conventional random sampling method. Experimental results on three HSI datasets show that the proposed ISS-Un method can significantly alleviate the redundancy of training samples and form a more compact and efficient training set, thus improving the classification performance of pixel-oriented HSI. Meanwhile, training sets constructed based on different uncertainty measurement algorithms are applied to five popular CNN models to verify the quality and generalizability of the selected samples. The results show that these training sets work better than random training sampling. Moreover, the proposed MEF algorithm outperforms the LC, MS, and Ent algorithms in selecting samples and is the main recommended scheme.

Selecting Representative Samples From Complex Biological Datasets Using K-Medoids Clustering.

Rapid growth of single-cell sequencing techniques enables researchers to investigate almost millions of cells with diverse properties in a single experiment. Meanwhile, it also presents great challenges for selecting representative samples from massive single-cell populations for further experimental characterization, which requires a robust and compact sampling with balancing diverse properties of different priority levels. The conventional sampling methods fail to generate representative and generalizable subsets from a massive single-cell population or more complicated ensembles. Here, we present a toolkit called Cookie which can efficiently select out the most representative samples from a massive single-cell population with diverse properties. This method quantifies the relationships/similarities among samples using their Manhattan distances by vectorizing all given properties and then determines an appropriate sample size by evaluating the coverage of key properties from multiple candidate sizes, following by a k-medoids clustering to group samples into several clusters and selects centers from each cluster as the most representatives. Comparison of Cookie with conventional sampling methods using a single-cell atlas dataset, epidemiology surveillance data, and a simulated dataset shows the high efficacy, efficiency, and flexibly of Cookie. The Cookie toolkit is implemented in R and is freely available at https://wilsonimmunologylab.github.io/Cookie/.

Spray drift evaluation with point clouds data of 3D LiDAR as a potential alternative to the sampling method.

Spray drift is an inescapable consequence of agricultural plant protection operation, which has always been one of the major concerns in the spray application industry. Spray drift evaluation is essential to provide a basis for the rational selection of spray technique and working surroundings. Nowadays, conventional sampling methods with passive collectors used in drift evaluation are complex, time-consuming, and labor-intensive. The aim of this paper is to present a method to evaluate spray drift based on 3D LiDAR sensor and to test the feasibility of alternatives to passive collectors. Firstly, a drift measurement algorithm was established based on point clouds data of 3D LiDAR. Wind tunnel tests included three types of agricultural nozzles, three pressure settings, and five wind speed settings were conducted. LiDAR sensor and passive collectors (polyethylene lines) were placed downwind from the nozzle to measure drift droplets in a vertical plane. Drift deposition volume on each line and the number of LiDAR droplet points in the corresponding height of the collecting line were calculated, and the influencing factors of this new method were analyzed. The results show that 3D LiDAR measurements provide a rich spatial information, such as the height and width of the drift droplet distribution, etc. High coefficients of determination (R2 > 0.75) were observed for drift points measured by 3D LiDAR compared to the deposition volume captured by passive collectors, and the anti-drift IDK12002 nozzle at 0.2 MPa spray pressure has the largest R2 value, which is 0.9583. Drift assessment with 3D LiDAR is sensitive to droplet density or drift mass in space and nozzle initial droplet spectrum; in general, larger droplet density or drift mass and smaller droplet size are not conducive to LiDAR detection, while the appropriate threshold range still needs further study. This study demonstrates that 3D LiDAR has the potential to be used as an alternative tool for rapid assessment of spray drift.

Leveraging network structure for efficient dynamic negative sampling in network embedding

Unsupervised network embedding learns low-dimensional vector representations of nodes based on the network structure. However, typical graphs only contain positive edges. Hence, most network embedding models take sampled negative edges as input to avoid trivial solutions. However, conventional negative sampling methods follow fixed distributions to generate negative samples. Such a static sampling strategy could dramatically hurt the performance of node embeddings in downstream tasks. Researchers have proposed several adaptive sampling strategies to improve the quality of negative samples. However, existing methods do not sufficiently use the network structure to explore high-quality samples, resulting in complex and inefficient negative sampling. This paper develops a novel dynamic negative sampling scheme, which maintains a candidate sample population for each node. We update the populations dynamically and retain high-quality samples in each iteration. We develop a novel network embedding algorithm based on the proposed model, which selects high-quality negative samples adaptively from the population. We conduct extensive experiments to evaluate its effectiveness based on several benchmark datasets. The experimental results verify the superiority of the proposed methods over state-of-the-art approaches.

Bedload transport analysis using image processing techniques

Bedload transport is an important factor to describe the hydromorphological processes of fluvial systems. However, conventional bedload sampling methods have large uncertainty, making it harder to understand this notoriously complex phenomenon. In this study, a novel, image-based approach, the Video-based Bedload Tracker (VBT), is implemented to quantify gravel bedload transport by combining two different techniques: Statistical Background Model and Large-Scale Particle Image Velocimetry. For testing purposes, we use underwater videos, captured in a laboratory flume, with future field adaptation as an overall goal. VBT offers a full statistics of the individual velocity and grainsize data for the moving particles. The paper introduces the testing of the method which requires minimal preprocessing (a simple and quick 2D Gaussian filter) to retrieve and calculate bedload transport rate. A detailed sensitivity analysis is also carried out to introduce the parameters of the method, during which it was found that by simply relying on literature and the visual evaluation of the resulting segmented videos, it is simple to set them to the correct values. Practical aspects of the applicability of VBT in the field are also discussed and a statistical filter, accounting for the suspended sediment and air bubbles, is provided.

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Various Aspects Involved in the Study of Tooth Bleaching Procedure: A Questionnaire-Based Study.

A beautiful smile is an important feature when it comes to a pleasant appearance of the face, and one of the most common situations that drive patients to book a dental appointment is tooth discoloration. Tooth bleaching is the treatment of choice for extrinsic tooth discoloration, as it is a cheap, fast, and minimally invasive procedure. This study aimed to provide comparative information on the perceptions of both patients and dentists regarding different whitening methods and on the factors involved in people’s willingness to recommend and use a bleaching procedure. In addition to this, this study evaluated the degree of satisfaction in relation to the bleaching methods and materials used; it also evaluated the following: negative side effects, economic characteristics and the patients’ perceptions of the aesthetic appearance of their dental arches, especially tooth color. The subjects that participated in the present study were selected based on their background and were then divided into two categories. The first group consisted of 120 patients who had received tooth-bleaching treatments in dental clinics during the study and the second group consisted of 127 dentists. A conventional sampling method was used. The study aimed to define a relationship between multiple aspects of the tooth-bleaching procedure, including the patients’ desires and their general knowledge of this procedure. Tooth color and the way it changes is a very important factor that motivates patients to come to the dentist for whitening procedures. Patients showed the highest levels of satisfaction with the results of in-office bleaching procedures. In the group consisting of dentists, satisfaction levels were higher for the procedure of home bleaching supervised by a dentist. Factors influencing the choice of bleaching materials are appreciated differently by dentists and patients. Furthermore, the rate of patients using OTC (over the counter) products was found to be high. Further research is needed to find more effective and safer alternatives to home tooth-bleaching procedures.

Passive Sampling of Organic Contaminants as a Novel Approach to Monitor Seawater Quality in Aquarium Ocean Tanks

The determination of trace pollutants in seawater is challenging, and sampling is a crucial step in the entire analytical process. Passive samplers combine in situ sampling and preconcentration, thus limiting the tedious treatment steps of the conventional sampling methods. Their use to monitor water quality in confined marine environment could bring several advantages. In this work, the presence of organic contaminants at trace and ultra-trace levels was assessed in the Genoa Aquarium supply-and-treated water using Polar Organic Integrative Samplers (POCIS). Both untargeted gas chromatography-mass spectrometry and targeted liquid chromatography-tandem mass spectrometry were employed. The untargeted approach showed the presence of hydrocarbons, diphenyl sulfone and 2,4-di-tert-butyl-phenol. Only hydrocarbons were detected in all the samples. Nineteen emerging contaminants, belonging to different classes (pharmaceuticals, UV-filters, hormones and perfluorinated compounds), were selected for the target analysis. Thirteen analytes were detected, mainly in supply water, even though the majority of them were below the quantitation limit. It is worthy to note that two of the detected UV-filters had never been reported in seawater using the POCIS samplers. The comparison of the analytes detected in supply and treated water indicated a good performance of the Aquarium water treatment system in the abatement of seawater contaminants.

Volumetric absorptive microsampling (VAMS) for the quantification of ten kinase inhibitors and determination of their in vitro VAMS-to-plasma ratio

Personalized dosing of kinase inhibitors (KI) might be beneficial in oral anti-cancer therapy to overcome individual pharmacokinetic variability. Volumetric absorptive microsampling (VAMS) has emerged as an attractive alternative compared to conventional invasive sampling methods enabling remote and frequent specimen collection. Therefore, an LC-MS/MS VAMS method was developed and validated to monitor drug exposure of ten KI from 20 µL dried capillary blood. The assay includes the KI cabozantinib, dabrafenib, nilotinib, and osimertinib with a calibration range of 6–1500 ng/mL and afatinib, axitinib, bosutinib, lenvatinib, ruxolitinib and trametinib within a range of 2–500 ng/mL. Using acetonitrile containing isotope labelled internal standards (IS) as solid-liquid extraction solvent, analytes and IS were detected by multiple reaction monitoring (MRM) after electro-spray ionization (ESI) in positive ionization mode after chromatographic separation using a phenyl-hexyl column. The method was validated according to the FDA and EMA guidelines for bioanalytical method validation and in accordance with the guideline of the International Association for Therapeutic Drug Monitoring and Clinical Toxicology for dried blood spot-based methods. The calibration model was linear and reproducible for all KI (R2> 0.994). Furthermore, the validation demonstrated that the VAMS method is accurate, precise, and sensitive. The method fulfilled the acceptance criteria for matrix effects, recovery, carry over, selectivity as well as for the haematocrit effect and all substances proved to be stable in dried condition for at least six weeks at room temperature. In vitro experiments using spiked venous blood were conducted to establish a VAMS-to-plasma conversion factor for each analyte for comparison of VAMS and plasma concentrations. The method was successfully used in a real-life setting demonstrating its applicability in clinical routine. VAMS concentrations of afatinib, cabozantinib, dabrafenib, nilotinib, ruxolitinib and trametinib were assessed in capillary blood samples collected from either trained healthcare professionals or patients at home.

Accelerated Sampling Research Under Task Optimized Strategy for Multistate Systems

The complex multi-state systems joint operation simulation and two task strategies are explored. For Monte Carlo sampling, based on the indirect sampling method, the principle of accelerated sampling methods, including forced transition FT and fault biasing FB, is studied. It is found that the direct adoption of the current FT and FB will result in systematic bias for the calculation of task success metrics. For the bias, state-transition chain truncation is studied, and its definition and truncation rules are proposed. In case study, after the simulation of various parameter combinations, it is found that when the sailing time is relatively smaller than the equipment reliability, which means a small probability event for the sampling of equipment faults, the ASM based on indirect sampling is applicable. Otherwise, the conventional sampling method should be used. The proposed state-transition chain truncation rule is also verified in case study.

Comparison of channel sampling methods and brush heads in surveillance culture of endoscope reprocessing: A propensity score matching and paired study.

Background:Endoscopy-related infections have caused multiple outbreaks. The importance of surveillance culture is gradually recognized, but sampling techniques are not consistent in many guidelines. It is unclear whether the Flush-Brush-Flush sampling method (FBFSM) is more sensitive than the conventional flush sampling method (CFSM) and whether different sampling brushes have different effects.Methods:The propensity score matching method was done with two matching ways, 1:1 nearest neighbor propensity score matching and full matching was used to analyze the surveillance culture data collected by FBFSM and CFSM. We fit a confounder-adjusted multiple generalized linear logistic regression model to estimate the marginal odds ratio (OR). A paired study was applied to compare the sampling effect of polyurethane foam (PU) head brush and polyamide (PA) head brush.Result:From 2016 to 2020, 316 reprocessed endoscope samples were collected from all 59 endoscopy centers in Tianjin. About 279 (88.3%) reprocessed endoscopes met the threshold of Chinese national standards (<20 CFU/Channel). The qualified rate of reprocessed endoscopes sampling by CFSM (91.8%) and FBFSM (81.6%) was statistically different (p < 0.05). The adjusted OR by full matching for FBFSM was 7.98 (95% confidence interval: 3.35-21.78). Forty one pairs of colonoscopes, after reprocessing from 27 centers, were tested by PA and PU brushes, and no difference was found in microbial recovery.Conclusion:FBFSM was confirmed to be a more sensitive sampling technique. PU and PA brushes had no significant difference in sampling effect.

Residency training amid the COVID-19 pandemic: exploring the impact on mental health and training, a lesson from Iran

BackgroundCOVID-19 has affected the training programs and the clinical schedules of surgical wards in many countries, including Iran. Also, the continuous involvement with COVID-19 patients has caused stress in health care workers; among them, residents are on the frontlines of care delivery. Therefore, we designed a study to assess the mental effects of these circumstances, and the effects on General Surgery and Obstetrics & Gynecology residency training in the busiest surgical departments of our university.MethodsParticipants of this cross-sectional study were residents of General Surgery and Obstetrics & Gynecology of Tehran University of Medical Sciences, and the conventional sampling method was used. We used a questionnaire consisting of 47 questions (mostly using multiple choice questions and answers on the Likert scale) about personal, familial, and demographic characteristics; training activities, and mental effects of COVID-19.ResultsThe response rate was 63.5%. (127 filled questionnaires). Around 96% of the residents had emotional problems, 85.9% were highly stressed about contracting COVID-19, 81.3% were worried about transferring it to their families; and 78% believed that their residency training had been impaired.ConclusionOverall, our study shows the negative impact of COVID-19 on mental health and the training of residents. We propose that appropriate emotional support and suitable planning for compensation of training deficits is provided for residents.

Pressure distribution feature-oriented sampling for statistical analysis of supercritical airfoil aerodynamics

In the field of supercritical wing design, various principles and rules have been summarized through theoretical and experimental analyses. Compared with black-box relationships between geometry parameters and performances, quantitative physical laws about pressure distributions and performances are clearer and more beneficial to designers. With the advancement of computational fluid dynamics and computational intelligence, discovering new rules through statistical analysis on computers has become increasingly attractive and affordable. This paper proposes a novel sampling method for the statistical study on pressure distribution features and performances, so that new physical laws can be revealed. It utilizes an adaptive sampling algorithm, of which the criteria are developed based on Kullback–Leibler divergence and Euclidean distance. In this paper, the proposed method is employed to generate airfoil samples to study the relationships between the supercritical pressure distribution features and the drag divergence Mach number as well as the drag creep characteristic. Compared with conventional sampling methods, the proposed method can efficiently distribute samples in the pressure distribution feature space rather than directly sampling airfoil geometry parameters. The corresponding geometry parameters are searched and found under constraints, so that supercritical airfoil samples that are well distributed in the pressure distribution space are obtained. These samples allow statistical studies to obtain more reliable and universal aerodynamic rules that can be applied to supercritical airfoil designs.

Quantum Representations and Scaling Up Algorithms of Adaptive Sampled-Data in Log-Polar Coordinates.

In log-polar coordinates, the conventional data sampling method is to sample uniformly in the log-polar radius and polar angle directions, which makes the sample at the fovea of the data denser than that of the peripheral. The central oversampling phenomenon of the conventional sampling method gives no more efficient information and results in computational waste. Fortunately, the adaptive sampling method is a powerful tool to solve this problem in practice, so the paper introduces it to quantum data processing. In the paper, the quantum representation model of adaptive sampled data is proposed first, in which the upper limit of the sampling number of the polar angles is related to the log-polar radius. Owing to this characteristic, its preparation process has become relatively complicated. Then, in order to demonstrate the practicality of the model given in the paper, the scaling up algorithm with an integer scaling ratio based on biarcuate interpolation and its circuit implementation of quantum adaptive sampled data is given. However, due to the special properties of the adaptive sampling method in log-polar coordinates, the interpolation process of adaptive sampled data becomes quite complicated as well. At the end of this paper, the feasibility of the algorithm is verified by a numerical example.