Optimal Sampling Strategy Research Articles

How precipitation intermittency sets an optimal sampling distance for temperature reconstructions from Antarctic ice cores

Abstract. Many palaeoclimate proxies share one challenging property: they are not only driven by the climatic variable of interest, e.g. temperature, but they are also influenced by secondary effects which cause, among other things, increased variability, frequently termed noise. Noise in individual proxy records can be reduced by averaging the records, but the effectiveness of this approach depends on the correlation of the noise between the records and therefore on the spatial scales of the noise-generating processes. Here, we review and apply this concept in the context of Antarctic ice-core isotope records to determine which core locations are best suited to reconstruct local- to regional-scale temperatures. Using data from a past-millennium climate model simulation equipped with stable isotope diagnostics we intriguingly find that even for a local temperature reconstruction the optimal sampling strategy is to combine a local ice core with a more distant core ∼ 500–1000 km away. A similarly large distance between cores is also optimal for reconstructions that average more than two isotope records. We show that these findings result from the interplay of the two spatial scales of the correlation structures associated with the temperature field and with the noise generated by precipitation intermittency. Our study helps to maximize the usability of existing Antarctic ice cores and to optimally plan future drilling campaigns. It also broadens our knowledge of the processes that shape the isotopic record and their typical correlation scales. Finally, many palaeoclimate reconstruction efforts face the similar challenge of spatially correlated noise, and our presented method could directly assist further studies in also determining optimal sampling strategies for these problems.

Dose individualization of intravenous busulfan in pediatric patients undergoing bone marrow transplantation: impact and in vitro evaluation of infusion lag-time.

To apply therapeutic drug monitoring and dose-individualization of intravenous Busulfan to paediatric patients and evaluate the impact of syringe-pump induced Busulfan infusion lag-time after in vitro estimation. 76 children and adolescents were administered 2 h intravenous Busulfan infusion every 6 h (16 doses). Busulfan plasma levels, withdrawn by an optimized sampling scheme and measured by a validated HPLC-PDA method, were used to estimate basic PK parameters, AUC, Cmax, kel, t1/2, applying Non-Compartmental Analysis. In vivo infusion lag-time was simulated in vitro and used to evaluate its impact on AUC estimation. Mean (%CV) Busulfan AUC, Cmax, clearance and t1/2 for pediatric population were found 962.3 μm × min (33.1), 0.95 mg/L (41.4), 0.27 L/h/kg (33.3), 2.2 h (27.8), respectively. TDM applied to 76 children revealed 6 (7.9%) being above and 25 (32.9%) below therapeutic-range (AUC: 900-1350 μm × min). After dose correction, all patients were measured below toxic levels (AUC < 1500 μm × min), no patient below 900 μm × min. Incorporation of infusion lag-time revealed lower AUCs with 17.1% more patients and 23.1% more younger patients, with body weight <16 kg, being below the therapeutic-range. TDM, applied successfully to 76 children, confirmed the need for Busulfan dose-individualization in paediatric patients. Infusion lag-time was proved clinically significant for younger, low body-weight patients and those close to the lower therapeutic-range limit.

Portable ED-XRF as a tool for optimizing sampling strategy: The case study of a Hellenistic amphora assemblage from Paphos (Cyprus)

Provenance studies of archaeological ceramics based on their elemental composition illuminate the production and distribution of pottery vessels and, in the case of transport containers, of the commodities that they contained. A basic assumption is that the elemental composition of ceramics from a specific workshop or production area can be distinguished from other production groups, mainly because of the use of geochemically different clays, either singly or in combination. In some cases, however, the compositional differences between production groups are quite small. Thus, laboratory methods with high performance, in terms of precision and accuracy, such as neutron activation analysis (NAA) or wavelength dispersive X-ray fluorescence (WDXRF), are often preferred for analyzing archaeological ceramics, especially for effective comparison with reference groups and published data from other studies. Handheld portable energy dispersive XRF systems (pXRF), although increasingly used during recent years, offer lesser analytical performance, which may obscure compositional differences and currently do not offer the same potential for comparison to known reference groups. However, due to their potential for fast and non-invasive measurements, considerably larger numbers of samples can be analysed by pXRF, offering an array of advantages. We argue that pXRF offers the opportunity for an initial analytical survey of a large ceramic assemblage as the basis for efficient sample selection for laboratory analysis, covering a large number of samples and avoiding for the generation of redundant measurements. We present the application of such a stepped analytical approach to a well-studied assemblage of amphorae of the Hellenistic period at Nea Paphos, Cyprus. The analysis of 287 amphora fragments by pXRF, the grouping of that data to select samples for further analysis of 97 individuals by NAA, and the comparison of the grouping of data from both chemical techniques is presented. This leads not only to archaeological insights on the production and circulation of amphorae, but tests an innovative methodology that offers the chance to maximize and extend the application of geochemical techniques of high accuracy and precision on an assemblage-wide scale.

Longitudinal Analysis of Circulating Tumor Cells in Colorectal Cancer Patients by a Cytological and Molecular Approach: Feasibility and Clinical Application.

IntroductionLiquid biopsies allowing for individualized risk stratification of cancer patients have become of high significance in individualized cancer diagnostics and treatment. The detection of circulating tumor cells (CTC) has proven to be highly relevant in risk prediction, e.g., in colorectal cancer (CRC) patients. In this study, we investigate the clinical relevance of longitudinal CTC detection over a course of follow-up after surgical resection of the tumor and correlate these findings with clinico-pathological characteristics.MethodsIn total, 49 patients with histologically proven colorectal carcinoma were recruited for this prospective study. Blood samples were analyzed for CTC presence by two methods: first by marker-dependent immunofluorescence staining combined with automated microscopy with the NYONE® cell imager and additionally, indirectly, by semi-quantitative Cytokeratin-20 (CK20) RT-qPCR. CTC quantification data were compared and correlated with the clinico-pathological parameters.ResultsDetection of CTC over a post-operative time course was feasible with both applied methods. In patients who were pre-operatively negative for CTCs with the NYONE® method or below the cut-off for relative CK20 mRNA expression after analysis by PCR, a statistically significant rise in the immediate post-operative CTC detection could be demonstrated. Further, in the cohort analyzed by PCR, we detected a lower CTC load in patients who were adjuvantly treated with chemotherapy compared to patients in the follow-up subgroup. This finding was contrary to the same patient subset analyzed with the NYONE® for CTC detection.ConclusionOur study investigates the occurrence of CTC in CRC patients after surgical resection of the primary tumor and during postoperative follow-up. The resection of the tumor has an impact on the CTC quantity and the longitudinal CTC analysis supports the significance of CTC as a prognostic biomarker. Future investigations with an even more extended follow-up period and larger patient cohorts will have to validate our results and may help to define an optimal longitudinal sampling scheme for liquid biopsies in the post-operative monitoring of cancer patients to enable tailored therapy concepts for precision medicine.

Leakage locating and sampling optimization of small-rate leakage on medium-and-low-pressure unground natural gas pipelines

The leakage on underground natural gas pipelines leads to not only energy loss but also a significant threat to the nearby region. Because of atmospheric dilution, strong wind, and complex underground conditions, small-rate leakage is particularly challenging to locate accurately for reparation. In this study, a series of field experiments with real-gas are carried out. Base on experimental data, a steady dispersion model of underground gas leakage is established and calibrated. This model with inversion method can be used to guide the locating of leakage. To furtherly improve the locating accuracy, two optimized sampler placing strategies based on the Fisher information matrix (FIM) and the covariance matrix are compared to the currently-used method in both simulations and field experiments. with optimized sampling strategies, the locating errors can be reduced by 14–54% with the same number of sampling points. This study show that first-principle steady-distribution model can be used to locate the leakage base on underground concentration samples and achieve the required precision. The current sampling strategy can also be optimized to improve the accuracy. Overall, this work could improve the safety of gas supply and reduce the emission of greenhouse gas.

Optimizing sampling strategies in high-resolution paleoclimate records

Abstract. The aim of paleoclimate studies resolving climate variability from noisy proxy records can in essence be reduced to a statistical problem. The challenge is to extract meaningful information about climate variability from these records by reducing measurement uncertainty through combining measurements for proxies while retaining the temporal resolution needed to assess the timing and duration of variations in climate parameters. In this study, we explore the limits of this compromise by testing different methods for combining proxy data (smoothing, binning, and sample size optimization) on a particularly challenging paleoclimate problem: resolving seasonal variability in stable isotope records. We test and evaluate the effects of changes in the seasonal temperature and the hydrological cycle as well as changes in the accretion rate of the archive and parameters such as sampling resolution and age model uncertainty in the reliability of seasonality reconstructions based on clumped and oxygen isotope analyses in 33 real and virtual datasets. Our results show that strategic combinations of clumped isotope analyses can significantly improve the accuracy of seasonality reconstructions compared to conventional stable oxygen isotope analyses, especially in settings in which the isotopic composition of the water is poorly constrained. Smoothing data using a moving average often leads to an apparent dampening of the seasonal cycle, significantly reducing the accuracy of reconstructions. A statistical sample size optimization protocol yields more precise results than smoothing. However, the most accurate results are obtained through monthly binning of proxy data, especially in cases in which growth rate or water composition cycles obscure the seasonal temperature cycle. Our analysis of a wide range of natural situations reveals that the effect of temperature seasonality on oxygen isotope records almost invariably exceeds that of changes in water composition. Thus, in most cases, oxygen isotope records allow reliable identification of growth seasonality as a basis for age modeling in the absence of independent chronological markers in the record. These specific findings allow us to formulate general recommendations for sampling and combining data in paleoclimate research and have implications beyond the reconstruction of seasonality. We briefly discuss the implications of our results for solving common problems in paleoclimatology and stratigraphy.

Supplementary data to: "Optimizing sampling strategies in high-resolution paleoclimate records: A statistical evaluation"

Supplementary data to: "Optimizing sampling strategies in high-resolution paleoclimate records: A statistical evaluation"

Supplementary data to: "Optimizing sampling strategies in high-resolution paleoclimate records: A statistical evaluation"

Supplementary data to: "Optimizing sampling strategies in high-resolution paleoclimate records: A statistical evaluation"

Bidirectional imputation of spatial GPS trajectories with missingness using sparse online Gaussian Process.

We propose a bidirectional GPS imputation method that can recover real-world mobility trajectories even when a substantial proportion of the data are missing. The time complexity of our online method is linear in the sample size, and it provides accurate estimates on daily or hourly summary statistics such as time spent at home and distance traveled. To preserve a smartphone's battery, GPS may be sampled only for a small portion of time, frequently <10%, which leads to a substantial missing data problem. We developed an algorithm that simulates an individual's trajectory based on observed GPS location traces using sparse online Gaussian Process to addresses the high computational complexity of the existing method. The method also retains the spherical geometry of the problem, and imputes the missing trajectory in a bidirectional fashion with multiple condition checks to improve accuracy. We demonstrated that (1) the imputed trajectories mimic the real-world trajectories, (2) the confidence intervals of summary statistics cover the ground truth in most cases, and (3) our algorithm is much faster than existing methods if we have more than 3 months of observations; (4) we also provide guidelines on optimal sampling strategies. Our approach outperformed existing methods and was significantly faster. It can be used in settings in which data need to be analyzed and acted on continuously, for example, to detect behavioral anomalies that might affect treatment adherence, or to learn about colocations of individuals during an epidemic.

The effects of sample size and sample prevalence on cellular automata simulation of urban growth

ABSTRACT This study investigates the effects of sample size and sample prevalence on cellular automata (CA) simulation of urban growth. We take the CA models based on an artificial neural network (ANN), logistic regression (LR), and support vector machine (SVM) as examples, to simulate the urban growth of Wuhan city in China and the Wuhan Metropolitan Area under different sampling schemes. The results of the CA models based on the ANN, LR, and SVM methods are generally consistent. The sampling scheme with a small sample size and a low sample prevalence should be discarded because of the high uncertainty. Sample size determines the robustness of a CA model, whereas sample prevalence affects the performance of a CA model when there are sufficient samples. In particular, the closer the sample prevalence is to the population prevalence, the higher the simulation accuracy and the lower the shape complexity and fragmentation of the simulated urban patterns. We suggest that the optimal sampling scheme has a sample rate of 1% and a sample prevalence that is the same as the population prevalence. The selection of the optimal sampling scheme is independent of the population sizes represented by different study areas.

DQN based single-pixel imaging.

For an orthogonal transform based single-pixel imaging (OT-SPI), to accelerate its speed while degrading as little as possible of its imaging quality, the normal way is to artificially plan the sampling path for optimizing the sampling strategy based on the characteristic of the orthogonal transform. Here, we propose an optimized sampling method using a Deep Q-learning Network (DQN), which considers the sampling process as decision-making, and the improvement of the reconstructed image as feedback, to obtain a relatively optimal sampling strategy for an OT-SPI. We verify the effectiveness of the method through simulations and experiments. Thanks to the DQN, the proposed single-pixel imaging technique is capable of obtaining an optimal sampling strategy directly, and therefore it requires no artificial planning of the sampling path there, which eliminates the influence of the imperfect sampling path planning on the imaging performance.

Whole lung tissue is the preferred sampling method for amplicon-based characterization of murine lung microbiota

BackgroundLow-biomass microbiome studies (such as those of the lungs, placenta, and skin) are vulnerable to contamination and sequencing stochasticity, which obscure legitimate microbial signal. While human lung microbiome studies have rigorously identified sampling strategies that reliably capture microbial signal from these low-biomass microbial communities, the optimal sampling strategy for characterizing murine lung microbiota has not been empirically determined. Performing accurate, reliable characterization of murine lung microbiota and distinguishing true microbial signal from noise in these samples will be critical for further mechanistic microbiome studies in mice.ResultsUsing an analytic approach grounded in microbial ecology, we compared bacterial DNA from the lungs of healthy adult mice collected via two common sampling approaches: homogenized whole lung tissue and bronchoalveolar lavage (BAL) fluid. We quantified bacterial DNA using droplet digital PCR, characterized bacterial communities using 16S rRNA gene sequencing, and systematically assessed the quantity and identity of bacterial DNA in both specimen types. We compared bacteria detected in lung specimens to each other and to potential source communities: negative (background) control specimens and paired oral samples. By all measures, whole lung tissue in mice contained greater bacterial signal and less evidence of contamination than did BAL fluid. Relative to BAL fluid, whole lung tissue exhibited a greater quantity of bacterial DNA, distinct community composition, decreased sample-to-sample variation, and greater biological plausibility when compared to potential source communities. In contrast, bacteria detected in BAL fluid were minimally different from those of procedural, reagent, and sequencing controls.ConclusionsAn ecology-based analytical approach discriminates signal from noise in this low-biomass microbiome study and identifies whole lung tissue as the preferred specimen type for murine lung microbiome studies. Sequencing, analysis, and reporting of potential source communities, including negative control specimens and contiguous biological sites, are crucial for biological interpretation of low-biomass microbiome studies, independent of specimen type.Aaejar9WP4PeeefSRNqdytVideo abstract

Predictive Wireless Based Status Update for Communication-Agnostic Sampling

In a wireless network that conveys status updates from sources (i.e., sensors) to destinations, one of the key issues studied by existing literature is how to design an optimal source sampling strategy on account of the communication constraints which are often modeled as queues. In this paper, an alternative perspective is presented—a novel status-aware communication scheme, namely parallel communications , is proposed which allows sensors to be communication-agnostic. Specifically, the proposed scheme can determine, based on an online prediction functionality, whether a status packet is worth transmitting considering both the network condition and status prediction, such that sensors can generate status packets without communication constraints. We evaluate the proposed scheme on a Software-Defined-Radio (SDR) test platform, which is integrated with a collaborative autonomous driving simulator, i.e., Simulation-of-Urban-Mobility (SUMO), to produce realistic vehicle control models and road conditions. The results show that with online status predictions, the channel occupancy is significantly reduced, while guaranteeing low status recovery error. Then the framework is applied to two scenarios: a multi-density platooning scenario, and a flight formation control scenario. Simulation results show that the scheme achieves better performance on the network level, in terms of keeping the minimum safe distance in both vehicle platooning and flight control.

Sampling Strategies for Internal Validation Samples for Exposure Measurement-Error Correction: A Study of Visceral Adipose Tissue Measures Replaced by Waist Circumference Measures.

Statistical correction for measurement error in epidemiologic studies is possible, provided that information about the measurement error model and its parameters are available. Such information is commonly obtained from a randomly sampled internal validation sample. It is however unknown whether randomly sampling the internal validation sample is the optimal sampling strategy. We conducted a simulation study to investigate various internal validation sampling strategies in conjunction with regression calibration. Our simulation study showed that for an internal validation study sample of 40% of the main study’s sample size, stratified random and extremes sampling had a small efficiency gain over random sampling (10% and 12% decrease on average over all scenarios, respectively). The efficiency gain was more pronounced in smaller validation samples of 10% of the main study’s sample size (i.e., a 31% and 36% decrease on average over all scenarios, for stratified random and extremes sampling, respectively). To mitigate the bias due to measurement error in epidemiologic studies, small efficiency gains can be achieved for internal validation sampling strategies other than random, but only when measurement error is nondifferential. For regression calibration, the gain in efficiency is, however, at the cost of a higher percentage bias and lower coverage.

Research Techniques Made Simple: Stratum Corneum Tape Stripping

Stratum corneum (SC)-derived biomarkers can provide relevant information on the skin's antimicrobial, physical, and immunological barriers. The SC is easily accessible, and collection by adhesive tapes (tape stripping [TS]) is robust and minimally invasive. Given its minimal invasiveness and simplicity, TS is particularly useful for studies in the pediatric population and when repetitive sampling over time is desirable, for example, in clinical trials. The palette of SC biomarkers is expanding in a wide variety of research areas, benefiting from advances in multiplex immunoassays and omics approaches, including proteomics, lipidomics, and transcriptomics. Although there is increasing interest in collecting SC samples, the lack of TS standardization hampers its broader implementation in research and clinical practice. In this article, we address the TS procedure as well as methodological challenges that should be considered in the development of an optimal sampling strategy.

Explainable artificial intelligence: Linking domain knowledge and machine interpretation using cognitive sampling of acoustical datasets

Explainable artificial intelligence is gaining wider traction within the machine learning community and its application domains at large for its inherent motivation to explain and interpret large-scale machine interpretation of experimental datasets. We will present cognitive sampling as new way to implement explainable AI for acoustical signal processing. In particular, methods based on geometric signal processing and sparse sensing will be harnessed with machine cognition to interpret, classify and predict information autonomously from large-scale acoustic datasets spanning a wide variety of applications. We will compare the difference in performance between traditional supervised and semi-supervised learning architectures such as deep learning, and ensemble approaches, unsupervised learning networks. We will also present preliminary research on implementing cognitive sampling in machine-directed inverse problem-solving techniques such as autoencoders. The end goal is to discover efficient data encodings that enable hitherto unforeseen feature spaces using optimal or close to optimal sampling strategies. Specific applications will include a variety of acoustical environmental sensing applications involving spectral feature generation and interpretation such as sonar signal processing, undersea multipath channel sensing, as well as feature extraction in complex melodic structures in Indian classical music. [Work funded partially by ONR under Grants N00014-19-1-2436, N00014-19-1-2609, N00174-20-1-0016 and N00014-20-1-2626.]

Do Log Factors Matter? On Optimal Wavelet Approximation and the Foundations of Compressed Sensing

A signature result in compressed sensing is that Gaussian random sampling achieves stable and robust recovery of sparse vectors under optimal conditions on the number of measurements. However, in the context of image reconstruction, it has been extensively documented that sampling strategies based on Fourier measurements outperform this purportedly optimal approach. Motivated by this seeming paradox, we investigate the problem of optimal sampling for compressed sensing. Rigorously combining the theories of wavelet approximation and infinite-dimensional compressed sensing, our analysis leads to new error bounds in terms of the total number of measurements m for the approximation of piecewise \(\alpha \)-Hölder functions. Our theoretical findings suggest that Fourier sampling outperforms random Gaussian sampling when the Hölder exponent \(\alpha \) is large enough. Moreover, we establish a provably optimal sampling strategy. This work is an important first step towards the resolution of the claimed paradox and provides a clear theoretical justification for the practical success of compressed sensing techniques in imaging problems.

GIS-Based Approach to Spatio-Temporal Interpolation of Atmospheric CO2 Concentrations in Limited Monitoring Dataset

Understanding the magnitude and distribution of the mixes of the near-ground carbon dioxide (CO2) components spatially (related to the surface characteristics) and temporally (over seasonal timescales) is critical to evaluating present and future climate impacts. Thus, the application of in situ measurement approaches, combined with the spatial interpolation methods, will help to explore variations in source contribution to the total CO2 mixing ratios in the urban atmosphere. This study presents the spatial characteristic and temporal trend of atmospheric CO2 levels observed within the city of Wroclaw, Poland for the July 2017–August 2018 period. The seasonal variability of atmospheric CO2 around the city was directly measured at the selected sites using flask sampling with a Picarro G2201-I Cavity Ring-Down Spectroscopy (CRDS) technique. The current work aimed at determining the accuracy of the interpolation techniques and adjusting the interpolation parameters for estimating the magnitude of CO2 time series/seasonal variability in terms of limited observations during the vegetation and non-vegetation periods. The objective was to evaluate how different interpolation methods will affect the assessment of air pollutant levels in the urban environment and identify the optimal sampling strategy. The study discusses the schemes for optimization of the interpolation results that may be adopted in areas where no observations are available, which is based on the kriging error predictions for an appropriate spatial density of measurement locations. Finally, the interpolation results were extended regarding the average prediction bias by exploring additional experimental configurations and introducing the limitation of the future sampling strategy on the seasonal representation of the CO2 levels in the urban area.

Noninvasive quantification of nonhuman primate dynamic 18F-FDG PET imaging

18F-FDG uptake rate constant Ki is the main physiology parameter measured in dynamic PET studies. A model-independent graphical analysis using Patlak plot with plasma input function (PIF) is a standard approach used to estimate Ki . The PIF is the 18F-FDG time activity curve (TAC) in plasma that is obtained by serial arterial blood sampling. The purpose of the study is to evaluate a Patlak plot-based optimization approach with reduced blood samples for noninvasive quantification of dynamic 18F-FDG PET imaging. Eight 60 min rhesus monkey brain dynamic 18F-FDG PET scans with arterial blood samples were collected. The measured PIF (mPIF) was determined by arterial blood samples. TACs of seven cerebral regions of interest were generated from each study. With a given number of blood samples, the population-based PIF (pPIF) was determined by either interpolation or extrapolation method using scale calibrated population mean of normalized PIF. The optimal sampling scheme with given blood sample size was determined by maximizing the correlations between the Ki estimated from pPIF and those obtained by mPIF. A leave-two-out cross-validation method was used for evaluation. The linear correlations between the Ki estimates from pPIF with optimal sampling schemes and those from mPIF were: Ki (pPIF 1 sample at 40 min) = 1.015 Ki (mPIF) − 0.000, R 2 = 0.974; Ki (pPIF 2 samples at 35 and 50 min) = 1.052 Ki (mPIF) − 0.001, R 2 = 0.976; Ki (pPIF 3 samples at 12, 40, and 50 min) = 1.030 Ki (mPIF) − 0.000, R 2 = 0.985; and Ki (pPIF 4 samples at 10, 20, 40, and 50 min) = 1.016 Ki (mPIF)- 0.000, R 2 = 0.993. As the sample size became greater or equal to 4, the Ki estimates from pPIF with the optimal protocol were almost identical to those from mPIF. The Patlak plot-based optimization approach is a reliable method to estimate PIF for noninvasive quantification of non-human primate dynamic 18F-FDG PET imaging and is potentially extendable to further translational human studies.

An effector\u2013reporter system to study cellular signal transduction in strawberry fruit (Fragaria ananassa)

An effector–reporter system is a powerful tool used to study cellular signal transduction, but this technique has been traditionally used in protoplasts. A similar system to study cellular signal transduction in fruits has not yet been established. In this study, we aimed to establish an effector–reporter system for strawberry fruit, a model nonclimacteric fruit. We first investigated the characteristics of transient gene expression in strawberry fruits and found marked variation in gene expression levels among individual fruits, and this variation has complicated the establishment of a technical system. To overcome this difficulty, we investigated a sampling strategy based on a statistical analysis of the activity pattern of four different reporters (GUS, GFP, FLuc, and RLuc) among individual fruits and combinations of pairs of reporters (GUS/GFP and RLuc/FLuc). Based on an optimized sampling strategy, we finally established a step-by step protocol for the effector/reporter assay. Using FaMYB10 and FaWRKY71 as the effectors and GUS driven by the FaCHS promoter as the reporter, we demonstrated that this effector/reporter system was practical and reliable. This effector/reporter technique will contribute to an in-depth exploration of the signaling mechanism for the regulation of strawberry fruit ripening.