Comparison Of Sampling Strategies Research Articles

Diversity of Parasitoid Wasps and Comparison of Sampling Strategies in Rice Fields Using Metabarcoding.

A comprehensive and precise evaluation of Arthropoda diversity in agricultural landscapes can enhance biological pest control strategies. We used Malaise traps and sweep nets to collect insects from three double-cropping paddy fields. DNA was extracted from the ethanol preservative of the Malaise traps and from tissue samples of selected parasitoid wasps. This was followed by amplification using DNA barcoding primers to prepare high-throughput sequencing libraries. We annotated a total of 4956 operational taxonomic units (OTUs), encompassing 174 genera and 32 families of parasitoid wasps. The ethanol filter method efficiently captured a wide range of information. However, the method has low resolution and may result in a reduced estimate of species abundance. Additional insect species were also identified in the parasitoid samples. This suggests that high throughput sequencing from adult parasitoid wasps can also detect host species, enabling a better understanding of host species and providing insights into food webs.

Monitoring insect biodiversity and comparison of sampling strategies using metabarcoding: A case study in the Yanshan Mountains, China

Insects are the richest and most diverse group of animals and yet there remains a lack, not only of systematic research into their distribution across some key regions of the planet, but of standardized sampling strategies for their study. The Yanshan Mountains, being the boundary range between the Inner Mongolian Plateau and the North China Plain, present an indispensable piece of the insect biodiversity puzzle: both requiring systematic study and offering opportunities for the development of standardized methodologies. This is the first use of DNA metabarcoding to survey the insect biodiversity of the Yanshan Mountains. The study focuses on differences of community composition among samples collected via different methods and from different habitat types. In total, 74 bulk samples were collected from five habitat types (scrubland, woodland, wetland, farmland and grassland) using three collection methods (sweep netting, Malaise traps and light traps). After DNA extraction, PCR amplification, sequencing and diversity analysis were performed, a total of 7427 Operational Taxonomic Units (OTUs) at ≥97% sequence similarity level were delimited, of which 7083 OTUs were identified as belonging to Insecta. Orthoptera, Diptera, Coleoptera and Hemiptera were found to be the dominant orders according to community composition analysis. Nonmetric multidimensional scaling (NMDS) analysis based on Bray-Curtis distances revealed highly divergent estimates of insect community composition among samples differentiated by the collection method (R = .524802, p = .001), but nonsignificant difference among samples differentiated according to habitat (R = .051102, p = .078). The study therefore appears to indicate that the concurrent use of varied collection methods is essential to the accurate monitoring of insect biodiversity.

Numerical comparison of sampling strategies for BRDF data manifolds

Abstract The bidirectional reflectance distribution function (BRDF) is one of the fundamental concepts in such diverse fields as multidimensional reflectometry, computer graphics and computer vision. BRDF manifolds form an infinite-dimensional space, but typically the available measurements are very scarce. Therefore, an efficient learning strategy is crucial when performing the measurements. In this paper, we perform simulation studies within a mathematical framework that allows to establish more efficient BRDF sampling and measurement strategies in the sense of statistical design of experiments and generalized proactive learning. Our simulation studies suggest that the default BRDF measurement strategy is suboptimal for a wide class of loss functions.

Soft-tissue imaging with C-arm cone-beam CT using statistical reconstruction

The potential for statistical image reconstruction methods such as penalized-likelihood (PL) to improve C-arm cone-beam CT (CBCT) soft-tissue visualization for intraoperative imaging over conventional filtered backprojection (FBP) is assessed in this work by making a fair comparison in relation to soft-tissue performance. A prototype mobile C-arm was used to scan anthropomorphic head and abdomen phantoms as well as a cadaveric torso at doses substantially lower than typical values in diagnostic CT, and the effects of dose reduction via tube current reduction and sparse sampling were also compared. Matched spatial resolution between PL and FBP was determined by the edge spread function of low-contrast (∼40–80 HU) spheres in the phantoms, which were representative of soft-tissue imaging tasks. PL using the non-quadratic Huber penalty was found to substantially reduce noise relative to FBP, especially at lower spatial resolution where PL provides a contrast-to-noise ratio increase up to 1.4–2.2× over FBP at 50% dose reduction across all objects. Comparison of sampling strategies indicates that soft-tissue imaging benefits from fully sampled acquisitions at dose above ∼1.7 mGy and benefits from 50% sparsity at dose below ∼1.0 mGy. Therefore, an appropriate sampling strategy along with the improved low-contrast visualization offered by statistical reconstruction demonstrates the potential for extending intraoperative C-arm CBCT to applications in soft-tissue interventions in neurosurgery as well as thoracic and abdominal surgeries by overcoming conventional tradeoffs in noise, spatial resolution, and dose.

Comparison of sampling strategies and sparsifying transforms to improve compressed sensing diffusion spectrum imaging

Diffusion Spectrum Imaging enables to reconstruct the ensemble average propagator (EAP) at the expense of having to acquire a large number of measurements. Compressive sensing offers an efficient way to decrease the required number of measurements. The purpose of this work is to perform a thorough experimental comparison of three sampling strategies and six sparsifying transforms to show their impact when applied to accelerate compressive sensing-diffusion spectrum imaging. We propose a novel sampling scheme that assures uniform angular and random radial q-space samples. We also compare and implement six discrete sparse representations of the EAP and thoroughly evaluate them on synthetic and real data using metrics from the full EAP, kurtosis, and orientation distribution function. The discrete wavelet transform with Cohen-Daubechies-Feauveau 9/7 wavelets and uniform angular sampling in combination with random radial sampling showed to be better than other tested techniques to accurately reconstruct the EAP and its features. It is important to jointly optimize the sampling scheme and the sparsifying transform to obtain accelerated compressive sensing-diffusion spectrum imaging. Experiments on synthetic and real human brain data show that one can robustly recover both radial and angular EAP features while undersampling the acquisition to 64 measurements (undersampling factor of 4).

Throughfall in a Puerto Rican lower montane rain forest: A comparison of sampling strategies

During a one-year period, the variability of throughfall and the standard errors of the means associated with different gauge arrangements were studied in a lower montane rain forest in Puerto Rico. The following gauge arrangements were used: (1) 60 fixed gauges, (2) 30 fixed gauges, and (3) 30 roving gauges. Stemflow was measured on 22 trees of four different species. An ANOVA indicated that mean relative throughfall measured by arrangements 1 (77%), 2 (74%), and 3 (73%) were not significantly different at the 0.05 level. However, the variability of the total throughfall estimate was about half as high for roving gauges (23%) as for fixed gauges (48-49%). The variability of stemflow ranged from 36% to 67% within tree species and was 144% for all sampled trees. Total stemflow was estimated at 4.1% of rainfall, of which palms contributed about 66%. Comparative analysis indicated that while fixed and roving gauge arrangements can give similar mean values, least 100 fixed gauges are required to have an error at the 95% confidence level comparable to that obtained by 30 roving gauges.

Comparison of sampling strategies

Horvitz andThompson [1952] considered varying probability sampling method in general and furnished an unbiased estimator of the population total.Rao, Hartley andCochran [1962] proposed a simple procedure of unequal probability sampling with replacement. It leads to an estimator of the population total having smaller variance than is obtained by sampling with replacement. An attempt has been made in the present paper to compare efficiencies ofHorvitz-Thompson's estimator with that due toRao, hartley andCochran. It is demonstrated that the generalized πps sampling strategy consisting of the design with πi, the probability of inclusion of thei-th population unit in the sample proportional to the modified size together withHorvitz-Thompson's estimator is superior toRao, Hartley andCochran's sampling strategy under a general super-population model.