Search

Measurement error is common in epidemiologic research and may affect the validity of research results. It is therefore important to scrutinise the effects of measurement error in epidemiologic research. Even simple forms of measurement error, for instance random measurement error in an exposure, can introduce bias in exposure-outcome associations. And even though there are situations in which measurement error does not introduce bias in the exposure-outcome association, for instance in case of random measurement error in a continuous outcome, it nearly always affects the precision and power of a study. In addition, other forms of measurement error, for example systematic measurement error or differential measurement error in an exposure, covariate or outcome, can affect exposure-outcome associations in complex ways that may not easily be anticipated. Adjusting for measurement error using measurement error correction methods may thus be necessary to obtain reliable estimates of exposure-outcome associations.To facilitate measurement error correction, information about the underlying measurement error mechanism (i.e., model) and its parameters is needed. The measurement error model can sometimes be estimated from internal or external validation data, replicates data or calibration data. Collection and the use of such measurement error mechanism data will likely improve the quality of epidemiologic analyses in the presence of measurement error. This can be done through the application of measurement error correction methods, which adjust the analyses taking into account the information from the measurement error mechanism. Alternatively, in the absence of concrete data about the mechanisms or the parameters of measurement error, sensitivity analysis for measurement error can be used, in which the impact on the epidemiologic analyses of one or a range of hypothesised measurement error mechanisms or their parameters can be investigated. The studies described in the thesis were set out to improve the understanding of the impact of measurement error, to facilitate the application of measurement error correction methods, to improve the design of epidemiologic studies when measurement error in a variable is suspected and, to develop tools to quantitatively assess the impact of measurement error in epidemiologic research.

Supplemental Digital Content is Available in the Text., Purpose: Innovative and sustainable sampling strategies for bioanalytical quantification of drugs and metabolites have gained considerable interest. Scavenging can be stratified as a sustainable sampling strategy using residual material because it aligns with the green principles of waste reduction and sampling optimization. Scavenged sampling includes all biological fluids' (eg, blood, liquor, and urine) leftover from standard clinical care. This review elaborates on the past and current landscape of sustainable sampling within therapeutic drug monitoring, with a focus on scavenged sampling. Methods: In February 2021, 4 databases were searched to assess the literature on the clinical use of innovative and sustainable sampling techniques without applying publication date restrictions. Studies reporting the clinical use of scavenged blood sampling and bridging studies of scavenged sampling and normal blood sampling were eligible for inclusion. Results: Overall, 19 eligible studies concerning scavenged sampling were identified from 1441 records. Scavenged sampling is mainly applied in the pediatric population, although other patient groups may benefit from this strategy. The infrastructure required for scavenged sampling encounters several challenges, including logistic hurdles, storage and handling conditions, and documentation errors. A workflow is proposed with identified opportunities that guide the implementation of scavenged sampling. Conclusions: This review presents current evidence on the clinical use of scavenged sampling strategies. Scavenged sampling can be a suitable approach for drug quantification to improve dosage regimens, perform pharmacokinetic studies, and explore the value of therapeutic drug monitoring without additional sample collection.

In the livestock sector, the monitoring of gas concentrations plays a relevant role in the implementation of mitigation strategies, as well as the improvement in the indoor air quality for both workers and animals in barns. In the present study, measurements of gas concentrations (NH3, CH4, and CO2) at different locations were carried out in an open dairy barn using a commercial photo-acoustic spectroscopy system. Measurement variability for different datasets was computed to contribute to the evaluation of the sampling strategy in the measurement campaign. The results showed that the position of sampling locations (SLs) significantly influenced (p < 0.001) the variability in the gas distribution. Specifically, the SLs located on the perimeter of the building had the highest variability. The number of SLs utilised for determining the mean value of gas concentration in the barn had a significant influence on NH3 (p < 0.001), CO2 (p < 0.001), and CH4 (p < 0.001) in both the central zone and the perimeter one. At least three SLs were necessary to obtain a mean value of gas concentration that reduced the variability to under the 10% in the central zone and 16% in the perimeter one. Moreover, the NH3 concentrations obtained as the mean value of the gas measurements at two SLs with a 10 m distance had a lower variability compared to those recorded at two SLs located at 5 m.

Accurate mensuration of forest stands for pre-harvest planning will pose high costs if carried out by a professional forester as an on-site evaluation. The costs could be reduced if a person with limited mensuration expertise could collect the required data using a smartphone-based system such as TRESTIMA® Forest Inventory System. Without prior information, the field sample with sufficient number of measurement points over the whole stand should be selected, so that the entire variation will be covered. We present and test a rational framework based on selecting the sampling locations according to auxiliary data. As auxiliary variables, we use various spatial data sources indicating forests’ structural or spectral variation, as well as previously predicted inventory variables. We construct two variants of sampling schemes based on the local pivotal method, weighted by the auxiliary data, and compare the results to simple random sampling (SRS) with corresponding sample sizes. According to our findings, the benefits of auxiliary data depend on the considered stand, species and timber assortment. The use of auxiliary data leads generally to improved results and up to three times higher efficiency (i.e. lower variance) as compared with SRS. We conclude that the framework of applying auxiliary data has high capabilities in rationalizing the sampling efforts with little drawbacks, consequently providing potential to improve the results with similar sample size and possibility to use of non-specialists for the pre-harvest inventory.

Background Early detection and prediction of type two diabetes mellitus incidence by baseline measurements could reduce associated complications in the future. The low incidence rate of diabetes in comparison with non-diabetes makes accurate prediction of minority diabetes class more challenging. Methods Deep neural network (DNN), extremely gradient boosting (XGBoost), and random forest (RF) performance is compared in predicting minority diabetes class in Tehran Lipid and Glucose Study (TLGS) cohort data. The impact of changing threshold, cost-sensitive learning, over and under-sampling strategies as solutions to class imbalance have been compared in improving algorithms performance. Results DNN with the highest accuracy in predicting diabetes, 54.8%, outperformed XGBoost and RF in terms of AUROC, g-mean, and f1-measure in original imbalanced data. Changing threshold based on the maximum of f1-measure improved performance in g-mean, and f1-measure in three algorithms. Repeated edited nearest neighbors (RENN) under-sampling in DNN and cost-sensitive learning in tree-based algorithms were the best solutions to tackle the imbalance issue. RENN increased ROC and Precision-Recall AUCs, g-mean and f1-measure from 0.857, 0.603, 0.713, 0.575 to 0.862, 0.608, 0.773, 0.583, respectively in DNN. Weighing improved g-mean and f1-measure from 0.667, 0.554 to 0.776, 0.588 in XGBoost, and from 0.659, 0.543 to 0.775, 0.566 in RF, respectively. Also, ROC and Precision-Recall AUCs in RF increased from 0.840, 0.578 to 0.846, 0.591, respectively. Conclusion G-mean experienced the most increase by all imbalance solutions. Weighing and changing threshold as efficient strategies, in comparison with resampling methods are faster solutions to handle class imbalance. Among sampling strategies, under-sampling methods had better performance than others.

As long as critical levels of vaccination have not been reached to ensure heard immunity, and new SARS-CoV-2 strains are developing, the only realistic way to reduce the infection speed in a population is to track the infected individuals before they pass on the virus. Testing the population via sampling has shown good results in slowing the epidemic spread. Sampling can be implemented at different times during the epidemic and may be done either per individual or for combined groups of people at a time. The work we present here makes two main contributions. We first extend and refine our scalable agent-based COVID-19 simulator to incorporate an improved socio-demographic model which considers professions, as well as a more realistic population mixing model based on contact matrices per country. These extensions are necessary to develop and test various sampling strategies in a scenario including the 62 largest cities in Spain; this is our second contribution. As part of the evaluation, we also analyze the impact of different parameters, such as testing frequency, quarantine time, percentage of quarantine breakers, or group testing, on sampling efficacy. Our results show that the most effective strategies are pooling, rapid antigen test campaigns, and requiring negative testing for access to public areas. The effectiveness of all these strategies can be greatly increased by reducing the number of contacts for infected individual. This work has been supported by the Carlos III Institute of Health under the project grant 2020/00183/001, the project grant BCV-2021-1-0011, of the Spanish Supercomputing Network (RES) and the European Union’s Horizon 2020 JTI-EuroHPC research and innovation program under grant agreement No 956748. The role of all study sponsors was limited to financial support and did not imply participation of any kind in the study and collection, analysis, and interpretation of data, nor in the writing of the manuscript.

The snowpack is important for water resources, tourism, ecology, and the global energy budget. Over the past century, we have gone from point measurements of snow water equivalent (SWE) to estimate spring and summer runoff volumes, to remote sensing of various snowpack properties at continuously finer spatial and temporal resolutions, to various complexities of snowpack and hydrological modeling, to the current fusion of field data with remote sensing and modeling, all to improve our estimates of the snowpack and the subsequent runoff. However, we are still limited by the uncertainty induced by scaling from point field measurements to the area represented by remote sensing and modeling. This paper uses several examples of fine-resolution sampling to issue a call to snow hydrologists and other earth scientists to collect more data, or at least to thoroughly evaluate their sampling strategy for collecting ground-truth measurements. Recommendations are provided for different approaches to have more representative sampling, when at all possible, to collect at least a few more samples or data points.

Background We evaluated whether occupancy modeling, an approach developed for detecting rare wildlife species, could overcome inherent accuracy limitations associated with rapid disease tests to generate fast, accurate, and affordable SARS-CoV-2 prevalence estimates. Occupancy modeling uses repeated sampling to estimate probability of false negative results, like those linked to rapid tests, for generating unbiased prevalence estimates. Methods We developed a simulation study to estimate SARS-CoV-2 prevalence using rapid, low-sensitivity, low-cost tests and slower, high-sensitivity, higher cost tests across a range of disease prevalence and sampling strategies. Results Occupancy modeling overcame the low sensitivity of rapid tests to generate prevalence estimates comparable to more accurate, slower tests. Moreover, minimal repeated sampling was required to offset low test sensitivity at low disease prevalence (0.1%), when rapid testing is most critical for informing disease management. Conclusions Occupancy modeling enables the use of rapid tests to provide accurate, affordable, real-time estimates of the prevalence of emerging infectious diseases like SARS-CoV-2.

Fast, reliable, and comprehensive biodiversity monitoring data are needed for environmental decision making and management. Recent work on fish environmental DNA (eDNA) metabarcoding shows that aquatic diversity can be captured fast, reliably, and non-invasively at moderate costs. Because freshwater ecosystems act as sinks in the landscape, they also collect traces of terrestrial species via surface runoff or when specimens come into direct contact with water (e.g., for drinking purposes). Thus, fish eDNA metabarcoding data can provide information on fish but also on other, even terrestrial vertebrate species that live in riparian habitats. This data become available and may offer a much more comprehensive approach for assessing vertebrate diversity at no additional costs. Studies on how the sampling strategy affects species detection especially of stream-associated communities, however, are scarce. We therefore performed an analysis on the effects of biological replication on both fish as well as (semi-)terrestrial species detection. Along a 2-km stretch of the river Mulde (Germany), we collected 18 1L water samples and analyzed the relation of detected species richness and quantity of biological replicates taken. We detected 58 vertebrate species, of which 25 were fish and lamprey, 18 mammals, and 15 birds, which account for 50%, 24% and 7% of all native species to the German federal state of Saxony-Anhalt. However, while increasing the number of biological replicates resulted in only 25 % more detected fish and lamprey species, mammal and bird species richness increased disproportionately by 69 % and 84 %, respectively. Contrary, PCR replicates showed little stochasticity. We thus emphasize to increase the number of biological replicates when the aim is to improve general species detections. This holds especially true, when the focus is on rare aquatic taxa or on (semi-)terrestrial species, the so-called ‘bycatch’. As a clear advantage, this information can be obtained without any additional sampling or laboratory effort when the sampling strategy regarding biological replication is chosen carefully. With the consideration of frequent eDNA metabarcoding as part of national biomonitoring programs, the additional information provided by the bycatch can be used to further investigate the state of the environment and its biodiversity on a much broader scale.

Convolutional Neural Networks (CNNs) have been proved to deliver great results in the area of computer vision, however, one fundamental bottleneck with CNNs is the fact that it is heavily dependant on the ground truth, that is, labeled training data. A labeled dataset is a group of samples that have been tagged with one or more labels. In this degree project, we mitigate the data greedy behavior of CNNs by applying deep active learning with various kinds of sampling strategies. The main focus will be on the sampling strategies random sampling, least confidence sampling, margin sampling, entropy sampling, and K- means sampling. We choose to study the random sampling strategy since it will work as a baseline to the other sampling strategies. Moreover, the least confidence sampling, margin sampling, and entropy sampling strategies are uncertainty based sampling strategies, hence, it is interesting to study how they perform in comparison with the geometrical based K- means sampling strategy. These sampling strategies will help to find the most informative/representative samples amongst all unlabeled samples, thus, allowing us to label fewer samples. Furthermore, the benchmark datasets MNIST and CIFAR10 will be used to verify the performance of the various sampling strategies. The performance will be measured in terms of accuracy and less data needed. Lastly, we concluded that by using least confidence sampling and margin sampling we reduced the number of labeled samples by 79.25% in comparison with the random sampling strategy for the MNIST dataset. Moreover, by using entropy sampling we reduced the number of labeled samples by 67.92% for the CIFAR10 dataset. Faltningsnätverk har visat sig leverera bra resultat inom området datorseende, men en fundamental flaskhals med Faltningsnätverk är det faktum att den är starkt beroende av klassificerade datapunkter. I det här examensarbetet hanterar vi Faltningsnätverkens giriga beteende av klassificerade datapunkter genom att använda deep active learning med olika typer av urvalsstrategier. Huvudfokus kommer ligga på urvalsstrategierna slumpmässigt urval, minst tillförlitlig urval, marginal baserad urval, entropi baserad urval och K- means urval. Vi väljer att studera den slumpmässiga urvalsstrategin eftersom att den kommer användas för att mäta prestandan hos de andra urvalsstrategierna. Dessutom valde vi urvalsstrategierna minst tillförlitlig urval, marginal baserad urval, entropi baserad urval eftersom att dessa är osäkerhetsbaserade strategier som är intressanta att jämföra med den geometribaserade strategin K- means. Dessa urvalsstrategier hjälper till att hitta de mest informativa/representativa datapunkter bland alla oklassificerade datapunkter, vilket gör att vi behöver klassificera färre datapunkter. Vidare kommer standard dastaseten MNIST och CIFAR10 att användas för att verifiera prestandan för de olika urvalsstrategierna. Slutligen drog vi slutsatsen att genom att använda minst tillförlitlig urval och marginal baserad urval minskade vi mängden klassificerade datapunkter med 79, 25%, i jämförelse med den slumpmässiga urvalsstrategin, för MNIST- datasetet. Dessutom minskade vi mängden klassificerade datapunkter med 67, 92% med hjälp av entropi baserad urval för CIFAR10datasetet.

Choosing the appropriate sampling strategy is significant while estimating the pollutant loads in a snow pile and assessing environmental impacts of dumping snow into water bodies. This paper compares different snow pile sampling strategies, looking for the most efficient way to estimate the pollutant loads in a snow pile. For this purpose, 177 snow samples were collected from nine snow piles (average pile area − 30 m2, height − 2 m) during four sampling occasions at Frihamnen, Ports of Stockholm’s port area. The measured concentrations of TSS, LOI, pH, conductivity, and heavy metals (Zn, Cu, Cd, Cr, Pb, and V) in the collected samples indicated that pollutants are not uniformly distributed in the snow piles. Pollutant loads calculated from different sampling strategies were compared against the load calculated using all samples collected for each pile (best estimate of mass load, BEML). The results/study showed that systematic grid sampling is the best choice when the objective of sampling is to estimate the pollutant loads accurately. Estimating pollutant loads from single snow column samples (collected at a point from the snow pile through the entire depth of the pile) produced up to 400% variation from BEML, whereas samples composed by mixing volume-proportional subsamples from all samples (horizontal composite samples) produced only up to 50% variation. Around nine samples were required to estimate the pollutant loads within 50% deviation from BEML for the studied snow piles. Converting pollutant concentrations in snow to equivalent concentrations in snowmelt and comparing it with available guideline values for receiving water, Zn was identified as the critical pollutant.

The ubiquity of organophosphate esters (OPEs) in various environmental matrices inevitably pose human exposure risks. Numerous studies have investigated human exposure pathways to OPEs, including air inhalation, dust ingestion, dermal contact, and dietary and drinking water intake, and have indicated that indoor dust and indoor air routes are frequently the two main human exposure pathways. This article reviews the literature on OPE contamination in indoor air and dust from various microenvironments and on OPE particle size distributions and bioavailability in dust conducted over the past 10 years. Ways in which sampling strategies are related to the uncertainty of exposure assessment results and comparability among different studies in terms of sampling tools, sampling sites, and sample types are addressed. Also, the associations of OPEs in indoor dust/air with human biological samples were summarized. Studies on two emerging matrices, hand wipes and silicone wristbands, are demonstrated to be more comprehensive and accurate in reflecting personal human exposure to OPEs in microenvironments and are summarized. Given the direct application of some diester OPEs (di-OPEs) in numerous products, research on their existence in indoor dust and food and on their effects on human urine are also discussed. Finally, related research trends and avenues for future research are prospected.

Surface-sourced organic compounds in infiltrating waters and percolates are transformed during their belowground passage. Biotic and abiotic processes thereby lead to continuously changing chemical environments in subsurface compartments. The investigation of such transformations of organic compounds aims for tracing subsurface fluxes as well as biotic and abiotic activity. To collect samples of soil solution, different kinds of lysimeters are available, spanning simple free-draining devices that sample water based on gravimetric flow and tension lysimeters allowing for approximating natural hydraulic conditions. Protocols for untargeted analytical profiling of organic soil solution constituents are scarce. We report here a solid phase extraction followed by GC–MS analysis, utilizing two long-term sampling devices in the Hainich Critical Zone Exploratory in Thuringia, Germany. In addition, we introduce a new lysimeter constructed exclusively from inert materials that allows for obtaining samples with little background signals in GC–MS. Polyvinylchloride (PVC)-based lysimeters introduce substantial background signals from plasticizers. We show how signals from these contaminants can be lowered during data analysis using chemometric background removal. Applying multivariate statistics for data analysis, we demonstrate the ability for monitoring of several sugars, fatty acids and phenolic acids at the topsoil-subsoil boundary and even beyond, via an untargeted analytical approach. Statistical tools facilitated the detection of differences in chemical signatures at three different land use sites. Data mining methods for metabolomics led to the identification of 3-carboxyphenylalanin as marker for a pasture site. The combined approach is suitable for the collection and extraction of topsoil and subsoil solution for untargeted metabolomics under near-natural flow conditions.

Background There is little consensus on how to sample hospitalizations and analyze multiple variables to model readmission risk. The purpose of this study was to compare readmission rates and the accuracy of predictive models based on different sampling and multivariable modeling approaches. Methods We conducted a retrospective cohort study of 17,284 adult diabetes patients with 44,203 discharges from an urban academic medical center between 1/1/2004 and 12/31/2012. Models for all-cause 30-day readmission were developed by four strategies: logistic regression using the first discharge per patient (LR-first), logistic regression using all discharges (LR-all), generalized estimating equations (GEE) using all discharges, and cluster-weighted (CWGEE) using all discharges. Multiple sets of models were developed and internally validated across a range of sample sizes. Results The readmission rate was 10.2% among first discharges and 20.3% among all discharges, revealing that sampling only first discharges underestimates a population’s readmission rate. Number of discharges was highly correlated with number of readmissions (r = 0.87, P Conclusions Hospital readmission risk models should be based on all discharges as opposed to just the first discharge per patient and utilize methods that account for clustered data.

Background Mate choice is a taxonomically wide-spread phenomenon, mostly exerted by females although male mate choice occurs as well. While costs and benefits of choosiness have been well studied, the underlying mechanisms are largely unclear. Different models exist, namely best-of-n or threshold rules, based on sequential or simultaneous sampling, which differ in the required cognitive demands. We applied an experimental approach to shed light on the underlying mechanisms of male mate choice in the sexually cannibalistic spider Argiope bruennichi. Males are limited to two copulations and preferentially monopolise large females, while they may leave smaller females after a single copulation and resume mate search. Here, we utilised significant size-differences between females from Northern and Southern populations and presented males with three different-sized females that were matched for origin: all three females originated either from the same Northern European population as the males or from Southern populations where the smallest female was about the same size as the largest Northern female. This allowed testing the hypothesis that males base their mating tactic on a fixed local size threshold. We predicted Northern males to be choosy among Northern females, but to accept all Southern females since they would all be above that threshold. Results Males copulated with the first female they encountered, which was independent of her body size. Regardless of the females’ origins, males chose a monogynous tactic with the largest female in the trio, while they left the smallest female after one copulation. The same pattern applied to Southern females even though the smallest females in the trio were of a similar size as monopolised Northern females. Since males have poor eyesight and did not actively sample all females, they likely have gained information about relative size differences between females based on volatile chemical cues only. Conclusions Our findings suggest that male A. bruennichi can assess relative differences in mate quality and adjust their mating tactic to the prevailing conditions (Northern vs. Southern). We reject the presence of a locally-adapted fixed threshold and argue that our results are best explained by an adjustable threshold that was raised under Southern conditions.

The definition of &ldquo, arable weeds&rdquo, remains contentious. Although much attention has been devoted to specialized, segetal weeds, many taxa found in arable fields also commonly occur in other habitats. The extent to which adjacent habitats are favorable to the weed flora and act as potential sources of colonizers in arable fields remains unclear. In addition, weeds form assemblages with large spatiotemporal variability, so that many taxa in weed flora are rarely observed in plot-based surveys. We thus addressed the following questions: How often do weeds occur in other habitats than arable fields? How does including field edges extend the taxonomic and ecological diversity of weeds? How does the weed flora vary across surveys at different spatial and temporal scales? We built a comprehensive dataset of weed taxa in France by compiling weed flora, lists of specialized segetal weeds, and plot-based surveys in agricultural fields, with different spatial and temporal coverages. We informed life forms, biogeographical origins and conservation status of these weeds. We also defined a broader dataset of plants occupying open habitats in France and assessed habitat specialization of weeds and of other plant species absent from arable fields. Our results show that many arable weeds are frequently recorded in both arable fields and non-cultivated open habitats and are, on average, more generalist than species absent from arable fields. Surveys encompassing field edges included species also occurring in mesic grasslands and nitrophilous fringes, suggesting spill-over from surrounding habitats. A total of 71.5% of the French weed flora was not captured in plot-based surveys at regional and national scales, and many rare and declining taxa were of Mediterranean origin. This result underlines the importance of implementing conservation measures for specialist plant species that are particularly reliant on arable fields as a habitat, while also pointing out biotic homogenization of agricultural landscapes as a factor in the declining plant diversity of farmed landscapes. Our dataset provides a reference species pool for France, with associated ecological and biogeographical information.

We are concerned with the efficiency of stochastic gradient estimation methods for large-scale nonlinear optimization in the presence of uncertainty. These methods aim to estimate an approximate gradient from a limited number of random input vector samples and corresponding objective function values. Ensemble methods usually employ Gaussian sampling to generate the input samples. It is known from the optimal design theory that the quality of sample-based approximations is affected by the distribution of the samples. We therefore evaluate six different sampling strategies to optimization of a high-dimensional analytical benchmark optimization problem, and, in a second example, to optimization of oil reservoir management strategies with and without geological uncertainty. The effectiveness of the sampling strategies is analyzed based on the quality of the estimated gradient, the final objective function value, the rate of the convergence, and the robustness of the gradient estimate. Based on the results, an improved version of the stochastic simplex approximate gradient method is proposed based on UE(s2) sampling designs for supersaturated cases that outperforms all alternative approaches. We additionally introduce two new strategies that outperform the UE(s2) designs previously suggested in the literature.

This chapter discusses the importance of caring about ethics, sampling strategies and analysis methods in a digital humanitiesrelated context, using the concept of critical digital method. Applying a critical digital method entails 1) practical lessons derived from interdisciplinary intersections of digital methods, 2) a request for engaging in critical digital method applications, 3) applying new techniques, and 4) a program performing a particular set of tasks. Therefore, ethical considerations, as well as addressing the issues of sampling and analysis strategies and why they matter, are important.

We are concerned with the efficiency of stochastic gradient estimation methods for large-scale nonlinear optimization in the presence of uncertainty. These methods aim to estimate an approximate gradient from a limited number of random input vector samples and corresponding objective function values. Ensemble methods usually employ Gaussian sampling to generate the input samples. It is known from the optimal design theory that the quality of sample-based approximations is affected by the distribution of the samples. We therefore evaluate six different sampling strategies to optimization of a high-dimensional analytical benchmark optimization problem, and, in a second example, to optimization of oil reservoir management strategies with and without geological uncertainty. The effectiveness of the sampling strategies is analyzed based on the quality of the estimated gradient, the final objective function value, the rate of the convergence, and the robustness of the gradient estimate. Based on the results, an improved version of the stochastic simplex approximate gradient method is proposed based on UE(s2) sampling designs for supersaturated cases that outperforms all alternative approaches. We additionally introduce two new strategies that outperform the UE(s2) designs previously suggested in the literature. © 2019, The Author(s).

Introduction Time‐resolved three‐dimensional phase contrast MRI (4D flow) of aortic blood flow requires acceleration to reduce scan time. Two established techniques for highly accelerated 4D flow MRI are k‐t principal component analysis (k‐t PCA) and compressed sensing (CS), which employ either regular or random k‐space undersampling. The goal of this study was to gain insights into the quantitative differences between k‐t PCA‐ and CS‐derived aortic blood flow, especially for high temporal resolution CS 4D flow MRI. Methods The scan protocol consisted of both k‐t PCA and CS accelerated 4D flow MRI, as well as a 2D flow reference scan through the ascending aorta acquired in 15 subjects. 4D flow scans were accelerated with factor R = 8. For CS accelerated scans, we used a pseudo‐spiral Cartesian sampling scheme, which could additionally be reconstructed at higher temporal resolution, resulting in R = 13. 4D flow data were compared with the 2D flow scan in terms of flow, peak flow and stroke volume. A 3D peak systolic voxel‐wise velocity and wall shear stress (WSS) comparison between k‐t PCA and CS 4D flow was also performed. Results The mean difference in flow/peak flow/stroke volume between the 2D flow scan and the 4D flow CS with R = 8 and R = 13 was 4.2%/9.1%/3.0% and 5.3%/7.1%/1.9%, respectively, whereas for k‐t PCA with R = 8 the difference was 9.7%/25.8%/10.4%. In the voxel‐by‐voxel 4D flow comparison we found 13.6% and 3.5% lower velocity and WSS values of k‐t PCA compared with CS with R = 8, and 15.9% and 5.5% lower velocity and WSS values of k‐t PCA compared with CS with R = 13. Conclusion Pseudo‐spiral accelerated 4D flow acquisitions in combination with CS reconstruction provides a flexible choice of temporal resolution. We showed that our proposed strategy achieves better agreement in flow values with 2D reference scans compared with using k‐t PCA accelerated acquisitions., The goal of this study was to gain insights in the quantitative differences between compressed sensing and k‐t PCA‐derived aortic blood flow, especially for high temporal resolution compressed sensing 4D flow MRI. Random pseudo‐spiral sampling with compressed sensing reconstruction provides a more flexible choice of temporal resolution compared with regular sampling like k‐t PCA. We showed that our proposed strategy achieves better agreement in peak flow values with 2D reference scans compared with using k‐t PCA accelerated acquisitions.

Plastic pollution is a global problem since 2016 when its production reached 322 million tonnes, excluding fibers. Daily discharges of microplastics (MPs, defined as, The financial support from the Distinguished Scientist Fellowship Program (DSFP) from King Saud University, Saudi Arabia, is gratefully acknowledged.

Sampling approaches used to census and monitor populations of flora and fauna are diverse, ranging from simple random sampling to complex hierarchal stratified designs. Usually the approach taken is determined by the spatial and temporal distribution of the study population, along with other characteristics of the focal species. Long-term monitoring programs used to assess seabird population trends are facilitated by their high site fidelity, but are often hampered by large and difficult to access colonies, with highly variable densities that require intensive survey. We aimed to determine the sampling effort required to (a) estimate population size with a high degree of confidence, and (b) detect different scenarios of population change in a regionally important species in the Atlantic, the Manx shearwater (Puffinus puffinus). Analyses were carried out using data collected from tape-playback surveys on four islands in the North Atlantic. To explore how sampling effort influenced confidence around abundance estimates, we used the heuristic approach of imagining the areas sampled represented the total population, and bootstrapped varying proportions of subsamples. This revealed that abundance estimates vary dramatically when less than half of all plots (n dependent on the size of the site) is randomly subsampled, leading to an unacceptable lack of confidence in population estimates. Confidence is substantially improved using a multi-stage stratified approach based on previous information on distribution in the colonies. In reality, this could lead to reducing the number of plots required by up to 80%. Furthermore, power analyses suggested that random selection of monitoring plots using a matched pairs approach generates little power to detect overall population changes of 10%, and density-dependent changes as large as 50%, because variation in density between plots is so high. Current monitoring programs have a high probability of failing to detect population-level changes due to inappropriate sampling efforts. Focusing sampling in areas of high density with low plot to plot variance dramatically increases the power to detect year to year population change, albeit at the risk of not detecting increases in low density areas, which may be an unavoidable strategy when resources are limited. We discuss how challenging populations with similar features to seabirds might be censused and monitored most effectively.

Once the research question and the research design have been finalised, it is important to select the appropriate sample for the study. The method by which the researcher selects the sample is the ‘ Sampling Method’. There are essentially two types of sampling methods: 1) probability sampling – based on chance events (such as random numbers, flipping a coin etc.); and 2) non-probability sampling – based on researcher's choice, population that accessible & available. Some of the non-probability sampling methods are: purposive sampling, convenience sampling, or quota sampling. Random sampling method (such as simple random sample or stratified random sample) is a form of probability sampling. It is important to understand the different sampling methods used in clinical studies and mention this method clearly in the manuscript. The researcher should not misrepresent the sampling method in the manuscript (such as using the term ‘ random sample’ when the researcher has used convenience sample). The sampling method will depend on the research question. For instance, the researcher may want to understand an issue in greater detail for one particular population rather than worry about the ‘ generalizability’ of these results. In such a scenario, the researcher may want to use ‘ purposive sampling’ for the study.

This article introduces a sampling technique especially developed for the field collection of soil and plant samples in a complex forest environment. The technique consists in the definition of a spiders' web georeferenced lattice to be used as a reference frame on the field collection. The web lattice points are equally distributed in polar coordinates, thus providing a finer coverage of the central area, maintaining a good rate up to the boundary. Such a spatial distribution is specially suited to many geostatistical algorithms including the widespread Kriging or Inverse Distance Weighting, which are commonly used in the production of Isoscapes (isotopic abundances maps).

This report provides guidance on how to slow the growth in volume of materials requiring long-term curation and allow for future efficient management of collections of undetermined, little or no research potential.

Made available in DSpace on 2018-12-11T17:25:58Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-06-02 In this paper, we propose the use of the T2 chart with the mixed sampling strategy (MS) to monitor the mean vector of bivariate processes with observations that fit to a first-order vector autoregressive model. With the MS, rational subgroups of size n are taken from the process and the selected units are regrouped to form the mixed samples. The units of the mixed samples are units selected from the last two rational subgroups. The aim of the proposed sampling strategy is to reduce the negative effect of the autocorrelation on the performance of the T2 chart. When the two variables are autocorrelated, the MS always enhances the T2 chart performance, however, the mixed samples are not recommended for bivariate processes with only one autocorrelated variable which is rarely affected by the assignable cause. Production Department São Paulo State University (UNESP) Military Academy of Agulhas Negras Production Department São Paulo State University (UNESP)

Marine natural products are a rich source of novel and biologically active compounds. The number of identified marine natural compounds has grown 20% over the last five years from 2009 to 2013. Several challenges, including sample collection and structure elucidation, have limited the development of this research field. Nonetheless, new approaches, such as sampling strategies for organisms from extreme ocean environments, nanoscale NMR and computational chemistry for structural determination, are now available to overcome the barriers. In this review, we highlight the experimental technology innovations in the field of marine natural products, which in our view will lead to the development of many new drugs in the future.

El trabajo es una propuesta conceptual basada en textos de metodología en ciencias sociales y psicología, y robustecida con análisis empírico de las categorías usadas por investigadores cualitativos en publicaciones realizadas en revistas de alto impacto entre 2013-2015. Se propone una taxonomía de cuatro diseños cualitativos, de carácter flexible, y tres categorías de muestreo, subdivididas en quince estrategias. Permite al investigador decidir a priori cómo organizar la investigación, cómo proceder para acceder a los participantes, cuánto tiempo requiere el estudio y qué resguardos éticos considerar; reduciendo la incertidumbre del trabajo de terreno, sin limitar la flexibilidad de los métodos cualitativos. The work is a conceptual proposal based on methodology texts in social sciences and psychology, and strengthened with empirical analysis of the categories used by qualitative researchers in publications made in high impact journals between 2013-2015. We propose a taxonomy of four qualitative designs, of a flexible nature, and three sampling categories, subdivided into fifteen strategies. It allows the researcher to decide a priori how to organize the research, how to proceed to access the participants, how long the study requires and what ethical safeguards to consider; reducing the uncertainty of field work, without limiting the flexibility of qualitative methods.

Model-based geostatistics and Bayesian approaches are appropriate in the context of Veterinary Epidemiology when point data have been collected by valid study designs. The aim is to predict a continuous infection risk surface. Little work has been done on the use of predictive infection probabilities at farm unit level. In this paper we show how to use predictive infection probability and related uncertainty from a Bayesian kriging model to draw a informative samples from the 8794 geo-referenced sheep farms of the Campania region (southern Italy). Parasitological data come from a first cross-sectional survey carried out to study the spatial distribution of selected helminths in sheep farms. A grid sampling was performed to select the farms for coprological examinations. Faecal samples were collected for 121 sheep farms and the presence of 21 different helminths were investigated using the FLOTAC technique. The 21 responses are very different in terms of geographical distribution and prevalence of infection. The observed prevalence range is from 0.83% to 96.69%. The distributions of the posterior predictive probabilities for all the 21 parasites are very heterogeneous. We show how the results of the Bayesian kriging model can be used to plan a second wave survey. Several alternatives can be chosen depending on the purposes of the second survey: weight by posterior predictive probabilities, their uncertainty or combining both information. The proposed Bayesian kriging model is simple, and the proposed samping strategy represents a useful tool to address targeted infection control treatments and surbveillance campaigns. It is easily extendable to other fields of research.

Background The heterogeneous nature of environmental DNA (eDNA) and its effects on species detection and community composition estimates has been highlighted in several studies in the past decades. Mostly in the context of spatial distribution over large areas, in fewer occasions looking at spatial distribution within a single body of water. Temporal variation of eDNA, similarly, has mostly been studied as seasonality, observing changes over large periods of time, and often only for small groups of organisms such as fish and amphibians. Methods We analyzed and compared small-scale spatial and temporal variation by sampling eDNA from two small, isolated dune lakes for 20 consecutive weeks. Metabarcoding was performed on the samples using generic COI primers. Molecular operational taxonomic unit (MOTUs) were used to assess dissimilarities between spatial and temporal replicates. Results Our results show large differences between samples taken within one lake at one point in time, but also expose the large differences between temporal replicates, even those taken only 1 week apart. Furthermore, between-site dissimilarities showed a linear correlation with time frame, indicating that between-site differences will be inflated when samples are taken over a period of time. We also assessed the effects of PCR replicates and processing strategies on general patterns of dissimilarity between samples. While more inclusive PCR replicate strategies lead to higher richness estimations, dissimilarity patterns between samples did not significantly change. Conclusions We conclude that the dissimilarity of temporal replicates at a 1 week interval is comparable to that of spatial replicate samples. It increases, however, for larger time intervals, which suggests that population turnover effects can be stronger than community heterogeneity. Spatial replicates alone may not be enough for optimal recovery of taxonomic diversity, and cross-comparisons of different locations are susceptible to inflated dissimilarities when performed over larger time intervals. Many of the observed MOTUs could be classified as either phyto- or zooplankton, two groups that have gained traction in recent years as potential novel bio-indicator species. Our results, however, indicate that these groups might be susceptible to large community shifts in relatively short periods of time, highlighting the need to take temporal variations into consideration when assessing their usability as water quality indicators.

Definition of clear criteria for evaluation of the quality of core collections is a prerequisite for selecting high-quality cores. However, a critical examination of the different methods used in literature, for evaluating the quality of core collections, shows that there are no clear guidelines on the choices of quality evaluation criteria and as a result, inappropriate analyses are sometimes made leading to false conclusions being drawn regarding the quality of core collections and the methods to select such core collections. The choice of criteria for evaluating core collections appears to be based mainly on the fact that those criteria have been used in earlier publications rather than on the actual objectives of the core collection. In this study, we provide insight into different criteria used for evaluating core collections. We also discussed different types of core collections and related each type of core collection to their respective evaluation criteria. Two new criteria based on genetic distance are introduced. The consequences of the different evaluation criteria are illustrated using simulated and experimental data. We strongly recommend the use of the distance-based criteria since they not only allow the simultaneous evaluation of all variables describing the accessions, but they also provide intuitive and interpretable criteria, as compared with the univariate criteria generally used for the evaluation of core collections. Our findings will provide genebank curators and researchers with possibilities to make informed choices when creating, comparing and using core collections. Electronic supplementary material The online version of this article (doi:10.1007/s00122-012-1971-y) contains supplementary material, which is available to authorized users.

We consider two stochastic variants of the Share-a-Ride problem: one with stochastic travel times and one with stochastic delivery locations. Both variants are formulated as a two-stage stochastic programming model with recourse. The objective is to maximize the expected profit of serving a set of passengers and parcels using a set of homogeneous vehicles. Our solution methodology integrates an adaptive large neighborhood search heuristic and three sampling strategies for the scenario generation (fixed sample size sampling, sample average approximation, and sequential sampling procedure). A computational study is carried out to compare the proposed approaches. The results show that the convergence rate depends on the source of stochasticity in the problem: stochastic delivery locations converge faster than stochastic travel times according to the numerical test. The sample average approximation and the sequential sampling procedure show a similar performance. The performance of the fixed sample size sampling is better compared to the other two approaches. The results suggest that the stochastic information is valuable in real-life and can dramatically improve the performance of a taxi sharing system, compared to deterministic solutions.

Alternative sampling strategies such as dried blood sampling, liquid microsampling and the sampling of oral fluid, hair, meconium, interstitial fluid, sweat, exhaled breath condensate and sputum offer interesting opportunities for many applications in clinical routine. Here, we provide an overview of different applications, with special attention to the pivotal role of LC-MS/MS in facilitating analysis of the collected matrices. Covered clinical fields include newborn screening, endocrinology, therapeutic drug monitoring, phenotyping, toxicology, proteomics and metabolomics. Furthermore, specific advantages, challenges and limitations of each alternative sampling strategy are discussed, along with recent advances and future trends that may contribute to routine implementation of these sampling strategies. Given the development of many recent potentially valuable clinical applications, the possibility of home sampling and the opportunity to obtain information that is hard to procure using traditional sampling, a well-balanced role for alternative sampling strategies can be envisaged in patient healthcare in the (near) future.

Although molluscs feature prominently in the semi-popular and academic literature on marine biodiversity, field surveys largely ignore the small and rare species that form the majority of marine molluscan diversity. As a result of a massive effort to sample the benthic molluscs of a complex tropical coastal environment, 23,238 gastropod specimens representing 259 species of Triphoridae—a family with most adult species ranging from 2 to 10 mm—were obtained from a 45,000 hectares study area off the island of Espiritu Santo, Vanuatu. Most species are represented by fewer than 20 specimens and, despite the intensity of the sampling effort, 13% of the species are unique singletons. Spatial heterogeneity was high: out of 416 sampling events, 187 contained triphorids, and 42% of the species occurred at fewer than 5 stations. Most species were small (68% below 5 mm) or very small (22% below 3 mm). A faunal turnover was documented at around 10 m, and another at around 60 m, at the onset of the ‘‘twilight zone’’ that is particularly difficult to sample. On the order of 70% of the species are probably new to science. When dealing with taxonomically difficult groups a morphospecies segregation approach is operationally appropriate to detect patterns of richness, rarity and spatial turn-over. Very few, if any, conservation surveys have the human and funding resources to carry out baseline surveys of the intensity that generated the results presented here. However, as species numbers are often used to promote the conservation interest of a reef, a bay or a stretch of coast, it is essential to know how the numbers were generated: absolute numbers of species are meaningless unless sampling effort and techniques, area surveyed, and size classes targeted are described. This is very rarely the case, even in the academic literature.

O objetivo deste trabalho foi verificar o efeito do tamanho da coleção original de germoplasma sobre eficácia de diferentes métodos de agrupamento e de amostragem, utilizados na obtenção de coleções nucleares, e comparar esses métodos no estabelecimento de coleções nucleares. Foram simulados sete tamanhos de coleções originais e utilizadas sete estratégias de amostragem para estabelecimento de coleções nucleares, com uso de caracteres morfo-agronômicos. Oito estratégias de obtenção de coleção nuclear foram empregadas, seis com técnicas de agrupamento (Tocher e Tocher sequencial) e duas sem agrupamento (aleatória e Tocher invertido). Nas estratégias de agrupamento, foram empregadas as amostragens constante, logarítmica e proporcional. Determinaram-se a variância explicada, o valor do coeficiente de determinação, o coeficiente de coincidência e o coeficiente de coincidência do desvio-padrão entre a coleção nuclear e a coleção original. As estratégias de amostragem constante e logarítmica geram coleções nucleares representativas da coleção original. O agrupamento de Tocher sequencial é mais eficaz na representação da coleção original pela coleção nuclear do que o agrupamento de Tocher. Entre as estratégias avaliadas, a amostragem pelo método de Tocher com critério de aglomeração inverso foi a mais eficiente na geração de coleções nucleares representativas das coleções originais. The objective of this study was to verify the effect of original germplasm collection size on the effectiveness of different clustering methods and sampling, used to obtain core collections, and to compare these methods in the establishment of core collections. Simulations were made for seven sizes of original collections, and seven sampling strategies were used for the establishment of core collections using morpho-agronomic traits. Eight strategies for obtaining core collections were used, six of them by grouping techniques (Tocher and sequential Tocher), and two without grouping (random and Tocher with inverted criteria grouping). For grouping strategies, constant, logarithmic and proportional sampling were used. The explained variance, the value of the determination coefficient, the coincidence coefficient, and the coincidence standard deviation coefficient between the core and the original collections were determined. The constant and logarithmic sampling strategies generate core collections which are representative of the original collections. The sequential Tocher grouping is more efficient in representing the original collection by the core collection than the Tocher's. Among the evaluated strategies the sampling by Tocher's inversed grouping criterion is the most efficient to generate representative core collections of the original collections.

In the comparison of treatments in soil science experiments, a well-chosen experimental design is of crucial importance for making appropriate (optimal) statistical inference at low (lowest) costs. In the very common case that several observations from each plot are obtained, one should be aware that measurement units are not equivalent to so-called statistical experimental units. We illustrate the severe consequences of misinterpretations in some very commonly used experimental designs. Appropriate methodology is developed, which entails the aggregation of observations at the plot level based on the tools of spatial statistics, followed by a classical statistical analysis on the plot aggregated data. The model-based approach, in contrast to the design-based approach, appears to be a natural setting for comparison of treatments in soil-based experiments. Consequences for the planning of the experimental design are discussed, and recommendations are given

International audience; The objectives of this study are to evaluate the uncertainty in annual nitrate loads and concentrations (such as annual average and median concentrations) as induced by infrequent sampling and by the algorithms used to compute fluxes. A total of 50 watershed-years of hourly to daily flow and concentration data gathered from nine watersheds (5 to 252 km2) in Brittany, France, were analyzed. Original (high frequency) nitrate concentration and flow data were numerically sampled to simulate common sampling frequencies. Annual fluxes and concentration indicators calculated from the simulated samples were compared to the reference values calculated from the high-frequency data. The uncertainties contributed by several algorithms used to calculate annual fluxes were also quantified. In all cases, uncertainty increased as sampling intervals increased. Results showed that all the tested algorithms that do not use continuous flow data to compute nitrate fluxes introduced considerable uncertainty. The flowƒ]weighted average concentration ratio method was found to perform best across the 50 annual datasets. Analysis of the bias values suggests that the 90th and 95th percentiles and the maximum concentration values tend to be systematically underestimated in the long term, but the load estimates (using the chosen algorithm) and the average and median concentrations were relatively unbiased. Great variability in the precision of the load estimation algorithms was observed, both between watersheds of different sizes and between years for a particular watershed. This has prevented definitive uncertainty predictions for nitrate loads and concentrations in this preliminary work, but suggests that hydrologic factors, such as the watershed hydrological reactivity, could be a key factor in predicting uncertainty levels.

In this paper we consider (hierarchical, Lagrange) reduced basis approximation and a posteriori error estimation for linear functional outputs of affinely parametrized elliptic coercive partial differential equations. The essential ingredients are (primal-dual) Galerkin projection onto a low- dimensional space associated with a smooth ``parametric manifold'' --- dimension reduction; efficient and effective greedy sampling methods for identification of optimal and numerically stable approximations --- rapid convergence; a posteriori error estimation procedures --- rigorous and sharp bounds for the linear-functional outputs of interest; and Offline-Online computational decomposition strategies --- minimum marginal cost for high performance in the real-time/embedded (e.g., parameter-estimation, control) and many-query (e.g., design optimization, multi-model scale) contexts. We present illustrative results for heat conduction and convection- diffusion, inviscid flow, and linear elasticity; outputs include transport rates, added mass, and stress intensity factors.

In this paper the authors describe the limnological approaches, the sampling methodology, and strategy adopted in the study of the Xingu River in the area of influence of future Belo Monte Power Plant. The river ecosystems are characterized by unidirectional current, highly variable in time depending on the climatic situation the drainage pattern an hydrological cycle. Continuous vertical mixing with currents and turbulence, are characteristic of these ecosystems. All these basic mechanisms were taken into consideration in the sampling strategy and field work carried out in the Xingu River Basin, upstream and downstream the future Belo Monte Power Plant Units.

In this paper the authors describe the limnological approaches, the sampling methodology, and strategy adopted in the study of the Xingu River in the area of influence of future Belo Monte Power Plant. The river ecosystems are characterized by unidirectional current, highly variable in time depending on the climatic situation the drainage pattern an hydrological cycle. Continuous vertical mixing with currents and turbulence, are characteristic of these ecosystems. All these basic mechanisms were taken into consideration in the sampling strategy and field work carried out in the Xingu River Basin, upstream and downstream the future Belo Monte Power Plant Units. Resumo Neste trabalho os autores descrevem as abordagens limnológicas e a metodologia e estratégia de amostragem adotadas no estudo do Rio Xingu, na área de influência da futura Usina da Belo Monte (UHE Belo Monte). O ecossistema de rios é caracterizado por velocidade unidirecional de correntes, muitas flutuações de fluxos dependendo do clima do padrão de drenagem e do ciclo hidrológico. Mistura vertical permanente e turbulências ocorrem constantemente devido a correntes predominantes. Todos estes mecanismos gerais e específicos característicos do Rio Xingu, foram levados em consideração no plano de estudos limnológicos e amostragens desenvolvidas na área de influência de UHE Belo Monte a montante e a jusante do futuro empreendimento.

Background Depending on tree and site characteristics crown biomass accounts for a significant portion of the total aboveground biomass in the tree. Crown biomass estimation is useful for different purposes including evaluating the economic feasibility of crown utilization for energy production or forest products, fuel load assessments and fire management strategies, and wildfire modeling. However, crown biomass is difficult to predict because of the variability within and among species and sites. Thus the allometric equations used for predicting crown biomass should be based on data collected with precise and unbiased sampling strategies. In this study, we evaluate the performance different sampling strategies to estimate crown biomass and to evaluate the effect of sample size in estimating crown biomass. Methods Using data collected from 20 destructively sampled trees, we evaluated 11 different sampling strategies using six evaluation statistics: bias, relative bias, root mean square error (RMSE), relative RMSE, amount of biomass sampled, and relative biomass sampled. We also evaluated the performance of the selected sampling strategies when different numbers of branches (3, 6, 9, and 12) are selected from each tree. Tree specific log linear model with branch diameter and branch length as covariates was used to obtain individual branch biomass. Results Compared to all other methods stratified sampling with probability proportional to size estimation technique produced better results when three or six branches per tree were sampled. However, the systematic sampling with ratio estimation technique was the best when at least nine branches per tree were sampled. Under the stratified sampling strategy, selecting unequal number of branches per stratum produced approximately similar results to simple random sampling, but it further decreased RMSE when information on branch diameter is used in the design and estimation phases. Conclusions Use of auxiliary information in design or estimation phase reduces the RMSE produced by a sampling strategy. However, this is attained by having to sample larger amount of biomass. Based on our finding we would recommend sampling nine branches per tree to be reasonably efficient and limit the amount of fieldwork.

Sampling stygofauna is both time consuming and labour intensive. The challenge is to get samples from as many bores as possible within a limited time. The essential assumption for this is that faunal communities inside bores are comparable and representative of the communities outside. To compare relative abundance, taxonomic richness and community composition of the fauna inside groundwater bores to the fauna of the surrounding aquifer, 20 monitoring bores in Palatinate, southwestern Germany, were sampled twice in 1 month. Initially, a sample of 4 l of water was collected from the bottom of each bore. A further sample of 51 l was collected from the groundwater surrounding the bore using a pneumatic piston pump with double packer sampler. Water chemistry inside and outside the bore was similar, but the relative amounts of sediments within the bores were higher compared to those from outside. Relative abundances of fauna inside the bores were higher than in the aquifer, but taxonomic composition was similar with the exception of the proportions of nematodes and amphipods, which were higher inside. As a result, the proportions of cyclopoids were lower inside. Higher nematode proportions are explained partially by the nearly complete extraction of bore sediment. A “habitat heterogeneity effect” states that in heterogeneous aquifers with few suitable habitats, faunal distribution is supposed to be extremely patchy. Thus, detritus accumulates in bores, attracting animals and providing “habitat islands” in the groundwater. This effect could explain the higher amphipode proportions inside the bores, which were generally more frequently populated than the surrounding groundwater. As a consequence, fauna is thought to be nearly absent from groundwater, where suitable habitats are lacking. In those sparsely populated aquifers, samples representative of the aquifer taxonomic richness and composition can only be collected by removing large volumes of water, or by sampling the bottom of bores. These findings also suggest that the use of unbaited colonisation chambers or traps in the groundwater, which are comparable with bores, would seem to be a promising approach.

48 fine needle aspiration biopsy (FNAB) samples from 25 breast cancer cases, originally used for cytodiagnosis were subjected to DNA cytometry. There were air dried smears stained with the MGG method, and samples stained with HE or PAP stain after 50% ethanol fixation and cytocentrifugation. Different sampling strategies were applied. Four methods were tested: method 1: cell groups measured, method 2: all cells measured, method 3: free cells measured, and method 4: atypical free cells measured. Method 4 showed most often DNA aneuploid histogram patterns, sampling method 1 had the highest number of DNA diploid histogram patterns. Diagnostic approaches may benefit from a sampling method detecting the hiding aneuploid cell population. Grading of neoplasm could potentially benefit from other approaches.

International audience; The implementation of the WFD (Water Framework Directive), requests European Union member states to achieve good qualitative and quantitative status of all water bodies in 2015. The monitoring of organic micropollutants such as pesticides is an essential step to assess the chemical and biological state of streams, to understand the reasons of degradation and to implement sound mitigation solutions in the watershed. In particular, the water sampling, which can be performed according to several strategies, has to be closely adapted to the experimental goals. In this study, we present and compare 3 different active sampling strategies: grab sampling, time-related and flow-dependent automatic samplings. In addition, the last two can be fractionated (i.e. several samples collected, and each one contained in a single bottle) or averaged (i.e. several samples mixed in the same bottle). Time-related samples allow the assessment of average exposure concentrations of organic micropollutants, whereas flow-dependent samples lead to average flux concentrations. The 3 sampling strategies were applied and compared during the monitoring of the pesticide contamination of a river located in a French vineyard watershed (the Morcille River, located 60 km north of Lyon, in the Beaujolais region). Data were collected between 2007 and 2011, during different seasons and for a range of hydrological events. The Morcille watershed is characterized by contrasted hydrological events with a very short-time response due to its small size (5 km2), steep slopes (20 to 28%) and highly permeable sandy soils. These features make it particularly difficult to monitor water quality, due to fast variations of pesticide concentrations depending on rain events. This comparative study is performed in 2 steps. At first, we compare the timestamps of each sample composing the weekly-averaged samples and those of the grab samples with hydrological data. This allows us to evaluate the efficiency of these 2 sampling strategies in the integration of flow variations and therefore pesticide concentration variations during the sampling campaign. In a second step, we use the fractionated samples data during flood events to calculate the concentrations of virtual averaged samples of the events. Different time or flow steps were used for the calculation, to assess their impact on the pesticide averaged-concentrations or fluxes. These analyses highlight the benefits and drawbacks of each sampling strategy. They show that the sampling strategy should be carefully chosen and designed depending on the final aim of the study and on the watershed characteristics (in particular its hydrological dynamics). This study may help to design future monitoring on water quality.

The resolution of rapid evolutionary radiations or “bushes” in the tree of life has been one of the most difficult and interesting problems in phylogenetics. The avian order Galliformes appears to have undergone several rapid radiations that have limited the resolution of prior studies and obscured the position of taxa important both agriculturally and as model systems (chicken, turkey, Japanese quail). Here we present analyses of a multi-locus data matrix comprising over 15,000 sites, primarily from nuclear introns but also including three mitochondrial regions, from 46 galliform taxa with all gene regions sampled for all taxa. The increased sampling of unlinked nuclear genes provided strong bootstrap support for all but a small number of relationships. Coalescent-based methods to combine individual gene trees and analyses of datasets that are independent of published data indicated that this well-supported topology is likely to reflect the galliform species tree. The inclusion or exclusion of mitochondrial data had a limited impact upon analyses upon analyses using either concatenated data or multispecies coalescent methods. Some of the key phylogenetic findings include support for a second major clade within the core phasianids that includes the chicken and Japanese quail and clarification of the phylogenetic relationships of turkey. Jackknifed datasets suggested that there is an advantage to sampling many independent regions across the genome rather than obtaining long sequences for a small number of loci, possibly reflecting the differences among gene trees that differ due to incomplete lineage sorting. Despite the novel insights we obtained using this increased sampling of gene regions, some nodes remain unresolved, likely due to periods of rapid diversification. Resolving these remaining groups will likely require sequencing a very large number of gene regions, but our analyses now appear to support a robust backbone for this order.

Locomotion scores are used for lameness detection in dairy cows. In research, locomotion scores with 5 levels are used most often. Analysis of scores, however, is done after transformation of the original 5-level scale into a 4-, 3-, or 2-level scale to improve reliability and agreement. The objective of this study was to evaluate different ways of merging levels to optimize resolution, reliability, and agreement of locomotion scores for dairy cows. Locomotion scoring was done by using a 5-level scale and 10 experienced raters in 2 different scoring sessions from videos from 58 cows. Intra- and interrater reliability and agreement were calculated as weighted kappa coefficient (κw) and percentage of agreement (PA), respectively. Overall intra- and interrater reliability and agreement and specific intra- and interrater agreement were determined for the 5-level scale and after transformation into 4-, 3-, and 2-level scales by merging different combinations of adjacent levels. Intrarater reliability (κw) ranged from 0.63 to 0.86, whereas intrarater agreement (PA) ranged from 60.3 to 82.8% for the 5-level scale. Interrater κw = 0.28 to 0.84 and interrater PA = 22.6 to 81.8% for the 5-level scale. The specific intrarater agreement was 76.4% for locomotion level 1, 68.5% for level 2, 65% for level 3, 77.2% for level 4, and 80% for level 5. Specific interrater agreement was 64.7% for locomotion level 1, 57.5% for level 2, 50.8% for level 3, 60% for level 4, and 45.2% for level 5. Specific intra- and interrater agreement suggested that levels 2 and 3 were more difficult to score consistently compared with other levels in the 5-level scale. The acceptance threshold for overall intra- and interrater reliability (κw and κ ≥0.6) and agreement (PA ≥75%) and specific intra- and interrater agreement (≥75% for all levels within locomotion score) was exceeded only for the 2-level scale when the 5 levels were merged as (12)(345) or (123)(45). In conclusion, when locomotion scoring is performed by experienced raters without further training together, the lowest specific intra- and interrater agreement was obtained in levels 2 and 3 of the 5-level scale. Acceptance thresholds for overall intra- and interrater reliability and agreement and specific intra- and interrater agreement were exceeded only in the 2-level scale.

To reduce the amount of data transfer in networked systems measurements are usually taken only when an event occurs rather than periodically in time. However, a fundamental assessment on the response of estimation algorithms receiving event sampled measurements is not available. This research presents such an analysis when new measurements are sampled at well-designed events and sent to a Luenberger observer. Conditions are then derived under which the estimation error is bounded, followed by an assessment of two event sampling strategies when the estimator encounters two different types of disturbances: an impulse and a step disturbance. The sampling strategies are compared via four performance measures, such as estimation-error and communication resources. The result is a clear insight of the estimation response in an event-based setup.

To reduce the amount of data transfer in networked systems measurements are usually taken only when an event occurs rather than periodically in time. However, a fundamental assessment on the response of estimation algorithms receiving event sampled measurements is not available. This research presents such an analysis when new measurements are sampled at well-designed events and sent to a Luenberger observer. Conditions are then derived under which the estimation error is bounded, followed by an assessment of two event sampling strategies when the estimator encounters two different types of disturbances: an impulse and a step disturbance. The sampling strategies are compared via four performance measures, such as estimation-error and communication resources. The result is a clear insight of the estimation response in an event-based setup.

Most of the devices for acquisition and display of medical images use rectangular lattices even though there are other sampling strategies that can be more efficient in terms of resolution. This paper proposes an approach for ultrasound image enhancement that uses a hexagonal sampling scheme to display and process the images. The images were resampled on an interlaced grid. Interlaced sampling uses square pixels shifted half a pixel on alternate rows. Two types of hexagonal filters were designed and tested on ultrasound images: a statistical adaptive filter and an anisotropic diffusion filter. Results show improvements in signal-to-noise ratio and more natural representation of curved structures. © 2014 SPIE and IS&T (DOI: 10.1117/1.JEI.23.4.043022)