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Journal articles on the topic 'Targeted sampling'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Zhang, Jun, Yi Isaac Yang, and Frank Noé. "Targeted Adversarial Learning Optimized Sampling." Journal of Physical Chemistry Letters 10, no. 19 (September 16, 2019): 5791–97. http://dx.doi.org/10.1021/acs.jpclett.9b02173.

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Chen, Sue, James A. Cummings, Jerome M. Schmidt, Elizabeth R. Sanabia, and Steven R. Jayne. "Targeted ocean sampling guidance for tropical cyclones." Journal of Geophysical Research: Oceans 122, no. 5 (May 2017): 3505–18. http://dx.doi.org/10.1002/2017jc012727.

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3

Watters, John K., and Patrick Biernacki. "Targeted Sampling: Options for the Study of Hidden Populations." Social Problems 36, no. 4 (October 1989): 416–30. http://dx.doi.org/10.1525/sp.1989.36.4.03a00070.

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4

Watters, John K., and Patrick Biernacki. "Targeted Sampling: Options for the Study of Hidden Populations." Social Problems 36, no. 4 (October 1989): 416–30. http://dx.doi.org/10.2307/800824.

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5

Williams, Michael S., Eric D. Ebel, and Scott J. Wells. "Population inferences from targeted sampling with uncertain epidemiologic information." Preventive Veterinary Medicine 89, no. 1-2 (May 2009): 25–33. http://dx.doi.org/10.1016/j.prevetmed.2008.12.008.

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6

Adamchuk, Viacheslav I., Raphael A. Viscarra Rossel, David B. Marx, and Ashok K. Samal. "Using targeted sampling to process multivariate soil sensing data." Geoderma 163, no. 1-2 (June 2011): 63–73. http://dx.doi.org/10.1016/j.geoderma.2011.04.004.

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7

Borges, Lisa, Alain F. Zuur, Emer Rogan, and Rick Officer. "Optimum sampling levels in discard sampling programs." Canadian Journal of Fisheries and Aquatic Sciences 61, no. 10 (October 1, 2004): 1918–28. http://dx.doi.org/10.1139/f04-138.

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This paper presents optimum sampling levels in discard sampling programs considering cost and precision objectives simultaneously and explores their dependence on both variables. The analysis is based on the Irish discard program: an onboard-observer voluntary sampling scheme aimed at estimating discard rates in trawl fisheries. Multistage analysis was performed to establish the precision levels achieved in the past, and a cost function was determined to estimate the financial cost of the program. Gear, fishing ground, targeted species, and International Council for the Exploration of the Sea (ICES) divisions were the main factors affecting discarding, together with random effects of the three nested groups considered: haul, trip, and vessel. Reductions in the present budget will imply only marginal decreases in precision, although changes in cost variables can have an impact on sampling levels. On the other hand, increasing the target precision by one-half will imply a considerable increase in sampling and associated cost, which will be difficult if not impossible to achieve. Finally, the analysis by fleet components suggests a marked increase in sampling levels, which emphasizes the importance of clearly stated discard sampling objectives.

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8

Carlson, Robert H. "Cervical Cancer: Targeted Sampling Detects Involved Nodes in Unusual Places." Oncology Times 31, no. 16 (August 2009): 25. http://dx.doi.org/10.1097/01.cot.0000360404.44039.b8.

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9

Chen, Fan, Yini Zhang, and Karl Rohe. "Targeted sampling from massive block model graphs with personalized PageRank." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 82, no. 1 (December 31, 2019): 99–126. http://dx.doi.org/10.1111/rssb.12349.

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10

Numao, N., M. Ito, Y. Uchida, S. Yoshida, T. Nakayama, M. Inoue, M. Tatokoro, et al. "211 Optimal number of sampling cores in MRI-targeted biopsy." European Urology Supplements 14, no. 2 (April 2015): e211. http://dx.doi.org/10.1016/s1569-9056(15)60212-3.

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11

Christopher, F. Scott, Jacqueline C. Pflieger, Daniel J. Canary, Laura K. Guerrero, and Amy Holtzworth-Munroe. "Targeted Neighborhood Sampling: A New Approach for Recruiting Abusive Couples." Journal of Family Violence 23, no. 2 (September 13, 2007): 89–100. http://dx.doi.org/10.1007/s10896-007-9130-y.

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12

Kral, Alex H., Mohsen Malekinejad, Jason Vaudrey, Alexis N. Martinez, Jennifer Lorvick, Willi McFarland, and H. Fisher Raymond. "Comparing Respondent-Driven Sampling and Targeted Sampling Methods of Recruiting Injection Drug Users in San Francisco." Journal of Urban Health 87, no. 5 (June 26, 2010): 839–50. http://dx.doi.org/10.1007/s11524-010-9486-9.

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13

Edwards, Matthew S., and Martin T. Tinker. "Monitoring Benthic AIgal Communides:A Comparison of Targeted and Coefficient Sampling Methods." ALGAE 24, no. 2 (June 1, 2009): 111–20. http://dx.doi.org/10.4490/algae.2009.24.2.111.

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14

Schroeder, K., J. Chiggiato, A. C. Haza, A. Griffa, T. M. Özgökmen, P. Zanasca, A. Molcard, et al. "Targeted Lagrangian sampling of submesoscale dispersion at a coastal frontal zone." Geophysical Research Letters 39, no. 11 (June 2012): n/a. http://dx.doi.org/10.1029/2012gl051879.

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15

Peterson, James A., Heather Schacht Reisinger, Robert P. Schwartz, Shannon Gwin Mitchell, Sharon M. Kelly, Barry S. Brown, and Michael H. Agar. "Targeted Sampling in Drug Abuse Research: A Review and Case Study." Field Methods 20, no. 2 (March 10, 2008): 155–70. http://dx.doi.org/10.1177/1525822x08314988.

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16

Chong, Luke X., Andrew Turpin, and Allison M. McKendrick. "Targeted spatial sampling using GOANNA improves detection of visual field progression." Ophthalmic and Physiological Optics 35, no. 2 (February 13, 2015): 155–69. http://dx.doi.org/10.1111/opo.12184.

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17

Rice, Christopher J., Christopher A. Taylor, Dusty A. Swedberg, and Robert J. DiStefano. "Targeted microhabitat sampling and its role in conserving endemic freshwater taxa." Freshwater Science 39, no. 2 (June 2020): 334–46. http://dx.doi.org/10.1086/709011.

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18

Marcus, J. "FemCerv – Single Pass Device for Targeted Endo-Cervical – In-Office Sampling." Journal of Minimally Invasive Gynecology 19, no. 6 (November 2012): S76. http://dx.doi.org/10.1016/j.jmig.2012.08.587.

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19

Price, Kristin E., Shannon S. Vandaveer, Craig E. Lunte, and Cynthia K. Larive. "Tissue targeted metabonomics: Metabolic profiling by microdialysis sampling and microcoil NMR." Journal of Pharmaceutical and Biomedical Analysis 38, no. 5 (August 2005): 904–9. http://dx.doi.org/10.1016/j.jpba.2005.02.034.

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20

Skjærvø, Øystein, Trine Grønhaug Halvorsen, and Léon Reubsaet. "All-in-one paper-based sampling chip for targeted protein analysis." Analytica Chimica Acta 1089 (December 2019): 56–65. http://dx.doi.org/10.1016/j.aca.2019.08.043.

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21

Shen, Yuecheng, Yan Liu, Cheng Ma, and Lihong V. Wang. "Sub-Nyquist sampling boosts targeted light transport through opaque scattering media." Optica 4, no. 1 (January 11, 2017): 97. http://dx.doi.org/10.1364/optica.4.000097.

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22

Hartel, Peter G., Karen Rodgers, Gwyneth L. Moody, Sarah N. J. Hemmings, Jared A. Fisher, and Jennifer L. McDonald. "Combining targeted sampling and fluorometry to identify human fecal contamination in a freshwater creek." Journal of Water and Health 6, no. 1 (November 1, 2007): 105–16. http://dx.doi.org/10.2166/wh.2007.004.

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Many bacterial source tracking (BST) methods are too expensive for most communities to afford. In an effort to develop an inexpensive method of detecting human sources of fecal contamination in a freshwater creek during baseflow and stormflow conditions, we combined targeted sampling with fluorometry. Targeted sampling is a prelude to BST and finds sources of fecal contamination by continued sampling and resampling over ever-decreasing distances. Fluorometry identifies human fecal contamination in water by detecting fluorescing compounds, optical brighteners, from laundry detergents. Potato Creek, a freshwater creek in Georgia (U.S.A.), had three reaches identified as containing high numbers of fecal bacteria, and these reaches were sampled by targeted sampling and fluorometry. Targeted sampling quickly and inexpensively identified humans, cattle, and dogs as the major sources of fecal contamination in the first, second, and third reaches, respectively. Fluorometric values were consistent with these identifications, but high fluorometric values were sometimes observed in areas with no fecal contamination. One likely cause of these false-positive signals was fluorescence from organic matter. For targeted sampling, the cost of each sample was $6, with a one-time equipment cost of $3,650; for fluorometry, the cost of each sample was negligible, with a one-time equipment cost of $14,250. This was the first study of this relatively inexpensive combination in freshwater during both baseflow and stormflow conditions.

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23

Farris, Calvin A., Christopher H. Baisan, Donald A. Falk, Megan L. Van Horne, Peter Z. Fulé, and Thomas W. Swetnam. "A comparison of targeted and systematic fire-scar sampling for estimating historical fire frequency in south-western ponderosa pine forests." International Journal of Wildland Fire 22, no. 8 (2013): 1021. http://dx.doi.org/10.1071/wf13026.

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Fire history researchers employ various forms of search-based sampling to target specimens that contain visible evidence of well preserved fire scars. Targeted sampling is considered to be the most efficient way to increase the completeness and length of the fire-scar record, but the accuracy of this method for estimating landscape-scale fire frequency parameters compared with probabilistic (i.e. systematic and random) sampling is poorly understood. In this study we compared metrics of temporal and spatial fire occurrence reconstructed independently from targeted and probabilistic fire-scar sampling to identify potential differences in parameter estimation in south-western ponderosa pine forests. Data were analysed for three case studies spanning a broad geographic range of ponderosa pine ecosystems across the US Southwest at multiple spatial scales: Centennial Forest in northern Arizona (100ha); Monument Canyon Research Natural Area (RNA) in central New Mexico (256ha); and Mica Mountain in southern Arizona (2780ha). We found that the percentage of available samples that recorded individual fire years (i.e. fire-scar synchrony) was correlated strongly between targeted and probabilistic datasets at all three study areas (r=0.85, 0.96 and 0.91 respectively). These strong positive correlations resulted predictably in similar estimates of commonly used statistical measures of fire frequency and cumulative area burned, including Mean Fire Return Interval (MFI) and Natural Fire Rotation (NFR). Consistent with theoretical expectations, targeted fire-scar sampling resulted in greater overall sampling efficiency and lower rates of sample attrition. Our findings demonstrate that targeted sampling in these systems can produce accurate estimates of landscape-scale fire frequency parameters relative to intensive probabilistic sampling.

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24

Ebneter, Andreas, Robert J. Casson, John PM Wood, and Glyn Chidlow. "Estimation of axon counts in a rat model of glaucoma: comparison of fixed-pattern sampling with targeted sampling." Clinical & Experimental Ophthalmology 40, no. 6 (February 20, 2012): 626–33. http://dx.doi.org/10.1111/j.1442-9071.2011.02741.x.

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25

Shaffer, Patrick, Omar Valsson, and Michele Parrinello. "Enhanced, targeted sampling of high-dimensional free-energy landscapes using variationally enhanced sampling, with an application to chignolin." Proceedings of the National Academy of Sciences 113, no. 5 (January 19, 2016): 1150–55. http://dx.doi.org/10.1073/pnas.1519712113.

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The capabilities of molecular simulations have been greatly extended by a number of widely used enhanced sampling methods that facilitate escaping from metastable states and crossing large barriers. Despite these developments there are still many problems which remain out of reach for these methods which has led to a vigorous effort in this area. One of the most important problems that remains unsolved is sampling high-dimensional free-energy landscapes and systems that are not easily described by a small number of collective variables. In this work we demonstrate a new way to compute free-energy landscapes of high dimensionality based on the previously introduced variationally enhanced sampling, and we apply it to the miniprotein chignolin.

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26

Connor, Martin J., Saiful Miah, Rajiv Jayadevan, Christopher C. Khoo, David Eldred-Evans, Taimur Shah, Hashim U. Ahmed, and Leonard Marks. "Value of systematic sampling in an mp-MRI targeted prostate biopsy strategy." Translational Andrology and Urology 9, no. 3 (June 2020): 1501–9. http://dx.doi.org/10.21037/tau.2019.07.16.

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27

Barrow, Lisa N., Alan R. Lemmon, and Emily Moriarty Lemmon. "Targeted Sampling and Target Capture: Assessing Phylogeographic Concordance with Genome-wide Data." Systematic Biology 67, no. 6 (March 21, 2018): 979–96. http://dx.doi.org/10.1093/sysbio/syy021.

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28

Kuntz, Robin L., Peter G. Hartel, Dominique G. Godfrey, Jennifer L. McDonald, Keith W. Gates, and William I. Segars. "Targeted Sampling Protocol as Prelude to Bacterial Source Tracking with Enterococcus faecalis." Journal of Environmental Quality 32, no. 6 (November 2003): 2311–18. http://dx.doi.org/10.2134/jeq2003.2311.

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29

Banis, George E., Luke A. Beardslee, Justin M. Stine, Rajendra Mayavan Sathyam, and Reza Ghodssi. "Gastrointestinal Targeted Sampling and Sensing via Embedded Packaging of Integrated Capsule System." Journal of Microelectromechanical Systems 28, no. 2 (April 2019): 219–25. http://dx.doi.org/10.1109/jmems.2019.2897246.

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30

Manolopoulou, Ioanna, Cliburn Chan, and Mike West. "Selection sampling from large data sets for targeted inference in mixture modeling." Bayesian Analysis 5, no. 3 (September 2010): 429–49. http://dx.doi.org/10.1214/10-ba517.

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31

Giussani, Valentina, Valentina Asnaghi, Andrea Pedroncini, and Mariachiara Chiantore. "Management of harmful benthic dinoflagellates requires targeted sampling methods and alarm thresholds." Harmful Algae 68 (September 2017): 97–104. http://dx.doi.org/10.1016/j.hal.2017.07.010.

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32

Bowser, Jessica, Andrew S. Briggs, Patricia Thompson, Matthew McLean, and Anjanette Bowen. "A Geospatial Approach to Improving Fish Species Detection in Maumee Bay, Lake Erie." Fishes 8, no. 1 (December 21, 2022): 3. http://dx.doi.org/10.3390/fishes8010003.

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Maumee Bay of western Lake Erie is at high risk for invasion by aquatic invasive species due to large urban and suburban populations, commercial shipping traffic, recreational boating, and aquaculture ponds. The U.S. Fish and Wildlife Service’s Early Detection and Monitoring (EDM) program has been monitoring for new invasive species since 2013 and is continually looking to adapt sampling methods to improve efficiency to increase the chance of detecting new aquatic invasive species at low abundances. From 2013–2016, the program used a random sampling design in Maumee Bay with three gear types: boat electrofishing, paired fyke nets, and bottom trawling. Capture data from the initial three years was used to spatially explore fish species richness with the hot spot analysis (Getis-Ord Gi*) in ArcGIS. In 2017, targeted sites in areas with high species richness (hot spots) were added to the randomly sampled sites to determine if the addition of targeted sampling would increase fish species detection rates and detection of rare species. Results suggest that this hybrid sampling design improved sampling efficiency as species not detected or were rare in previous survey years were captured and species were detected at a faster rate (i.e., in less sampling effort), particularly for shallow-water gear types. Through exploring past data and experimenting with targeted sampling, the EDM program will continue to refine and adapt sampling efforts to improve efficiency and provide valuable knowledge for the early detection of aquatic invasive species. The use of geospatial techniques such as hot spot analysis is one approach fisheries researchers and managers can use to incorporate targeted sampling in a non-subjective way to improve species detection.

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33

Pinto, James O., Andrew J. Monaghan, Luca Delle Monache, Emilie Vanvyve, and Daran L. Rife. "Regional Assessment of Sampling Techniques for More Efficient Dynamical Climate Downscaling." Journal of Climate 27, no. 4 (February 10, 2014): 1524–38. http://dx.doi.org/10.1175/jcli-d-13-00291.1.

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Abstract Dynamical downscaling is a computationally expensive method whereby finescale details of the atmosphere may be portrayed by running a limited area numerical weather prediction model (often called a regional climate model) nested within a coarse-resolution global reanalysis or global climate model output. The goal of this study is to assess using sampling techniques to dynamically downscale a small subset of days to approximate the statistical properties of the entire period of interest. Two sampling techniques are explored: one where days are randomly selected and another where representative days are chosen (or targeted) based on a set of selection criteria. The relative merit of using random sampling versus targeted random sampling is demonstrated using daily mean 2-m air temperature (T2M). The first two moments of dynamically downscaled T2M can be approximated within 0.3 K using just 5% of the population of available days during a 20-yr period. Targeted random sampling can reduce the mean absolute error of these estimates by as much as 30% locally. Estimation of the more extreme values of T2M is more uncertain and requires a larger sample size. The potential reduction in computational cost afforded by these sampling techniques could greatly benefit applications requiring high-resolution dynamically downscaled depictions of regional climate, including situations in which an ensemble of regional climate simulations is required to properly characterize uncertainty in the model physics assumptions, scenarios, and so on.

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34

Smith, Katherine L., and Michael L. Jones. "Watershed-level sampling effort requirements for determining riverine fish species composition." Canadian Journal of Fisheries and Aquatic Sciences 62, no. 7 (July 1, 2005): 1580–88. http://dx.doi.org/10.1139/f05-098.

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Accurate assessments of watershed-level species composition are necessary for comparative ecological studies, ecosystem health assessments, monitoring, and aquatic conservation prioritization. Several studies have addressed sampling effort requirements for characterizing fish species composition at a section of stream, but none have examined watershed-level requirements. In the spring and summer of 2002, we extensively sampled nine Great Lakes watersheds to assess sampling-effort requirements. Sampling requirements increased with the targeted percentage of estimated species richness. Sampling 15119 randomly selected reaches of stream, stratified by stream order, was on average sufficient to detect 80%100% of estimated species richness. Watershed size (km2) and estimated species richness each showed a weak, negative correlation with sampling-effort requirements in our study streams, with Pearson's correlation coefficients of 5.06 and 0.590, respectively. Because of among-watershed variability in sampling effort requirements, field crews should plot species accumulation curves onsite to determine adequate inventory completion. Based on the difficulty of detecting the last 10% of species, random sampling should be conducted in conjunction with targeted sampling of rare species.

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35

Aberson, Sim D. "Targeted Observations to Improve Operational Tropical Cyclone Track Forecast Guidance." Monthly Weather Review 131, no. 8 (August 1, 2003): 1613–28. http://dx.doi.org/10.1175//2550.1.

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Abstract Since 1997, the Tropical Prediction Center and the Hurricane Research Division have conducted operational synoptic surveillance missions with a Gulfstream IV-SP jet aircraft to improve numerical forecast guidance. Due to limited aircraft resources, optimal observing strategies for these missions must be developed. In the current study, the most rapidly growing modes are represented by areas of large forecast spread in the NCEP bred-vector ensemble forecasting system. The sampling strategy requires sampling of the entire target region with regularly spaced dropwindsonde observations. Three dynamical models were employed in testing the targeting and sampling strategies. With the assimilation into the numerical guidance of all the observations gathered during the surveillance missions, only the 12-h Geophysical Fluid Dynamics Laboratory Hurricane Model forecast showed statistically significant improvement. Assimilation of only the subset of data from the subjectively found fully sampled target regions produced a statistically significant reduction of the track forecast errors of up to 25% within the critical first 2 days of the forecast. This is comparable with the cumulative business-as-usual improvement expected over 18 yr.

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36

Puech, Philippe, Adil Ouzzane, Vianney Gaillard, Nacim Betrouni, Benoit Renard, Arnauld Villers, and Laurent Lemaitre. "Multiparametric MRI-Targeted TRUS Prostate Biopsies Using Visual Registration." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/819360.

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Prebiopsy multiparametric prostate MRI (mp-MRI), followed by transrectal ultrasound-guided (TRUS-G) target biopsies (TB) of the prostate is a key combination for the diagnosis of clinically significant prostate cancers (CSPCa), to avoid prostate cancer (PCa) overtreatment. Several techniques are available for guiding TB to the suspicious mp-MRI targets, but the simplest, cheapest, and easiest to learn is “cognitive,” with visual registration of MRI and TRUS data. This review details the successive steps of the method (target detection, mp-MRI reporting, intermodality fusion, TRUS guidance to target, sampling simulation, sampling, TRUS session reporting, and quality insurance), how to optimize each, and the global indications of mp-MRI-targeted biopsies. We discuss the diagnostic yield of visually-registered TB in comparison with conventional biopsy, and TB performed using other registration methods.

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37

Blickenstorfer, Sarah, Heinzpeter Schwermer, Monika Engels, Martin Reist, Marcus G. Doherr, and Daniela C. Hadorn. "Using scenario tree modelling for targeted herd sampling to substantiate freedom from disease." BMC Veterinary Research 7, no. 1 (2011): 49. http://dx.doi.org/10.1186/1746-6148-7-49.

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38

Shields, Michael D., and V. S. Sundar. "Targeted random sampling: a new approach for efficient reliability estimation for complex systems." International Journal of Reliability and Safety 9, no. 2/3 (2015): 174. http://dx.doi.org/10.1504/ijrs.2015.072718.

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39

Chen, Lu, Lina Gruzinskyte, Steffen Lynge Jørgensen, Anja Boisen, and Sarvesh Kumar Srivastava. "An Ingestible Self-Polymerizing System for Targeted Sampling of Gut Microbiota and Biomarkers." ACS Nano 14, no. 9 (August 24, 2020): 12072–81. http://dx.doi.org/10.1021/acsnano.0c05426.

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40

Lee, Jeeyeon, Jin Hyang Jung, Wan Wook Kim, Ryu Kyung Lee, Hye Jung Kim, Won Hwa Kim, Ji-Young Park, et al. "5-year oncological outcomes of targeted axillary sampling in pT1-2N1 breast cancer." Asian Journal of Surgery 42, no. 6 (June 2019): 681–87. http://dx.doi.org/10.1016/j.asjsur.2018.10.004.

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41

McDonald, Jennifer L., Peter G. Hartel, Lisa C. Gentit, Carolyn N. Belcher, Keith W. Gates, Karen Rodgers, Jared A. Fisher, Katy A. Smith, and Karen A. Payne. "Identifying Sources of Fecal Contamination Inexpensively with Targeted Sampling and Bacterial Source Tracking." Journal of Environmental Quality 35, no. 3 (May 2006): 889–97. http://dx.doi.org/10.2134/jeq2005.0328.

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42

Grottke, Michael, Alberto Avritzer, Daniel S. Menasché, Leandro P. de Aguiar, and Eitan Altman. "On the Efficiency of Sampling and Countermeasures to Critical-Infrastructure-Targeted Malware Campaigns." ACM SIGMETRICS Performance Evaluation Review 43, no. 4 (February 25, 2016): 33–42. http://dx.doi.org/10.1145/2897356.2897361.

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43

Allen, Sean T., Katherine H. A. Footer, Noya Galai, Ju Nyeong Park, Bradley Silberzahn, and Susan G. Sherman. "Implementing Targeted Sampling: Lessons Learned from Recruiting Female Sex Workers in Baltimore, MD." Journal of Urban Health 96, no. 3 (July 31, 2018): 442–51. http://dx.doi.org/10.1007/s11524-018-0292-0.

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44

Odonye DD, Amuta EU, Ogbonna IO, Odonye EP, Abisabo A, and Tsaku MI. "Sero-diagnostic studies on the occurrence and prevalence of bovine spongiform encephalopathies in Nasarawa state, Nigeria." GSC Biological and Pharmaceutical Sciences 14, no. 3 (March 30, 2021): 157–65. http://dx.doi.org/10.30574/gscbps.2021.14.3.0075.

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The study was conducted to determine the occurrence and the prevalence of bovine spongiform encephalopathy among cattle herds in Nasarawa State, Nigeria using targeted sampling approach. The procedure of sampling adapted was a “double” targeted method for survey. First, a BSE risk group was targeted ‘emergency or casualty slaughter’ and within this Group, animals with signs indicative for BSE were sub-targeted. Two sampling locations were chosen; Akwanga central abattoir and Lafia central abattoir, both located in Nasarawa North and Nasarawa south senatorial district. Immediately the suspected animals were slaughtered, the caudal brain stem was harvested and examined for the disease specific form of the prion protein, PrPSc, using Western Blot technique after proteinase K digestion. A total of 2000 cattle of local breeds, aged ≥ 24 months and above were clinically examined. A total of 147 (7.4 %) of the cattle sampled were clinically suspicious for BSE. No sample was positive for BSE. Fluorescent antibody test for rabies and H&E staining on samples were carried out to observe for differential diagnosis. These showed no pathological lesions indicative for neurological disease. While our findings do not exclude the presence of BSE in Nasarawa State, we demonstrate that targeted sampling of ruminants for neuroinfectious disease is possible in developing countries, pointing to the possibility of implementing such a monitoring scheme in Nigeria to prevent economic losses in ruminant livestock as BSE caveats from endemic countries have shown.

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45

Connor, M. J., D. Eldred-Evans, F. Hosking-Jervis, E. Bass, D. Reddy, M. Bertoncelli Tanaka, H. Bhola-Stewart, et al. "Which men should undergo non-targeted systematic sampling in an mpMRI-targeted pathway – an analysis from 1,719 pre-biopsy mpMRI cases?" European Urology Open Science 19 (July 2020): e191. http://dx.doi.org/10.1016/s2666-1683(20)32674-4.

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46

Zhang, Xianming, Matthew Robson, Karl Jobst, Miren Pena-Abaurrea, Alina Muscalu, Sri Chaudhuri, Chris Marvin, Ian D. Brindle, Eric J. Reiner, and Paul Helm. "Halogenated organic contaminants of concern in urban-influenced waters of Lake Ontario, Canada: Passive sampling with targeted and non-targeted screening." Environmental Pollution 264 (September 2020): 114733. http://dx.doi.org/10.1016/j.envpol.2020.114733.

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47

Miller, Timothy J., John R. Skalski, and James N. Ianelli. "Optimizing a stratified sampling design when faced with multiple objectives." ICES Journal of Marine Science 64, no. 1 (November 13, 2006): 97–109. http://dx.doi.org/10.1093/icesjms/fsl013.

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Abstract Miller, T. J., Skalski, J. R., and Ianelli, J. N. 2007. Optimizing a stratifield sampling design when faced with multiple objectives – ICES Journal of Marine Science, 64, 97–109. For many stratified sampling designs, the data collected are used by multiple parties with different estimation objectives. Quantitative methods to determine allocation of sampling effort to different strata to satisfy the often disparate estimation objectives are lacking. Analytical results for the sampling fractions and sample sizes for primary units within each stratum of a stratified (multi-stage) sampling design that are optimal with respect to a weighted sum of relative variances for the estimation objectives are presented. Further, an approach for assessing gains or losses for each estimation objective by changing allocation of sample sizes to each stratum is provided. As an illustration, the analytical results are applied to determine optimal observer sampling fractions (coverage rates) for the North Pacific Groundfish Observer Programme (NPGOP), for which the multiple objectives are assumed to be bycatch (seabird, marine mammal, and non-targeted fish species) and total catch, and catch-at-length and -age of targeted fish species. Simultaneously optimizing a criterion that defines the strata of the NPGOP sampling design is also considered. When observer coverage rates are allowed to be gear-specific for the NPGOP design, the optimized objective function is between 10% and 28% less than the value corresponding to current sampling for annual data (2000–2003) and 12% less when optimized over all years combined.

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Christodoulou, Panagiotis, Lysandros Pantelidis, and Elias Gravanis. "The Effect of Targeted Field Investigation on the Reliability of Axially Loaded Piles: A Random Field Approach." Geosciences 10, no. 5 (April 29, 2020): 160. http://dx.doi.org/10.3390/geosciences10050160.

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This work deals with the effect of targeted field investigation on the reliability of axially loaded piles, aiming at an optimal serviceability and ultimate limit state design. This is done in a Random Finite Element Method (RFEM) framework properly considering sampling in the analysis; the RFEM method combines finite element analysis with the random field theory. In this respect, the freely available program called RPILE1D has been modified by the authors as to consider sampling of both soil and pile properties. In each RFEM realization, failure is considered to have occurred when the calculated shaft resistance of pile considering spatially uniform properties (average of sampled values from the soil and pile random fields) is greater than the respective “actual” one considering spatially random properties for both soil and pile. The necessary numerical demonstration of the proposed methodology is done by considering two sampling strategies: a) sampling from a single point and b) sampling from a domain, both along the pile, whilst the various parameters governing the statistical uncertainty of the problem are examined; 5383 different cases were considered in total. As shown, a targeted field investigation may minimize or even eliminate the statistical error inserted in the design. The error is quantified by the difference in the probability of failure comparing different sampling scenarios. Another main finding is that the optimal horizontal sampling location occurs where the pile is going to be constructed. In addition, it is shown that the benefit of a targeted field investigation is much greater than the benefit gained using characteristic soil property values.

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Christodoulou, Panagiotis, Lysandros Pantelidis, and Elias Gravanis. "The Effect of Targeted Field Investigation on the Reliability of Earth-Retaining Structures in Active State." Applied Sciences 9, no. 22 (November 18, 2019): 4953. http://dx.doi.org/10.3390/app9224953.

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This paper introduces the concept of targeted field investigation on the reliability of earth-retaining structures in an active state, which is implemented in a random finite element method (RFEM) framework. The open source RFEM software REARTH2D was used and modified suitably in order to accommodate the purposes of the present research. Soil properties are modeled as random fields, and measurements are modeled by sampling from different points of the field domain. Failure is considered to have occurred when the “actual” resultant earth pressure force on the retaining wall (calculated using the friction angle random field) is greater than the respective “predicted” force (calculated using an homogenous friction angle field characterized by the mean of the values sampled from the respective random field). Two sampling strategies are investigated, namely, sampling from a single point and sampling from a domain, through an extensive parametric analysis. As shown, the statistical uncertainty related to soil properties may be significant and can only be minimized by performing targeted field investigation. Among the main findings is that the optimal sampling location in the active state is immediately adjacent to the wall face. In addition, it is advisable that the entire wall height be considered in sampling. Finally, it was observed that the benefit from a targeted field investigation is much greater as compared to the benefit gained using characteristic values in a Load and Resistance Factor Design framework.

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Ellison, Aaron M., and Elizabeth J. Farnsworth. "Targeted Sampling Increases Knowledge and Improves Estimates of Ant Species Richness in Rhode Island." Northeastern Naturalist 21, no. 1 (March 2014): NENHC—13—NENHC—24. http://dx.doi.org/10.1656/045.021.0118.

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