Number Of Sampling Techniques Research Articles

Sampling techniques and environmental variables influence the distribution of pseudoscorpions in urban forest fragments in the central Amazon

ABSTRACT The growth of urban landscapes has genarally reduced biodiversity worldwide. Invertebrates have explored different environments, and it usually takes different sampling techniques to get a representative sample of the species assemblage in a given location. Some studies have sought to determine the minimum necessary number of sampling techniques, including ecological relationships or costs to guide the sampling protocol. In the Amazon, the effect of soil characteristics on invertebrate distribution is well known. We evaluated if sampling techniques have a complementary effect on the detection of pseudoscorpion assemblages and tested whether environmental variables affect the distribution of pseudoscorpion species. The study sites were two urban forest fragments in the city of Manaus, in the central Amazon. In each fragment, we sampled 20 palm trees using the beating technique, and installed transects with 12 sampling points for collection of soil and litter samples for extraction of arthropods in a Berlese funnel and a Winkler extractor, respectively. We collected 267 individuals of 11 species of pseudoscorpions. Most records were obtained through the Winkler extraction in both fragments. The assemblage from the palm trees was different from that in the edaphic samples. Pseudoscorpion species composition also differed significantly between soil and litter, and was influenced by potassium concentration. The number of species in the fragments and the environmental effect on the distribution of pseudoscorpions was similar to that recorded in environmental protection areas, evidencing that urban forest fragments can serve as an efficient repository of Amazonian pseudoscorpion biodiversity.

Sensitivity and Safety of Electromagnetic Navigation Bronchoscopy for Lung Cancer Diagnosis: Systematic Review and Meta-analysis

Bronchoscopy is a useful tool for the diagnosis of lesions near central airways; however, the diagnostic accuracy of these procedures for peripheral pulmonary lesions (PPLs) is a matter of ongoing debate. In this setting, electromagnetic navigation bronchoscopy (ENB) is a technique used to navigate and obtain samples from these lesions. This systematic review and meta-analysis aims to explore the sensitivity of ENB in patients with PPLs suspected of lung cancer. In patients with peripheral pulmonary lesion suspected of lung cancer, what is the sensitivity and safety of electromagnetic navigation bronchoscopy compared to surgery or longitudinal follow up? A comprehensive search of several databases was performed. Extracted data included sensitivity of ENB for malignancy, adequacy of the tissue sample, and complications. The study quality was assessed using the QUADAS-2 tool, and the combined data were meta-analyzed using a bivariate method model. A summary receiver operatic characteristic curve (sROC) was created. Finally, the quality of evidence was rated using the Grading of Recommendations Assessment, Development and Evaluation approach. Forty studies with a total of 3,342 participants were included in our analysis. ENB reported a pooled sensitivity of 77%(95%CI, 72%-82%; I2= 80.6%) and a specificity of 100%(95%CI, 99%-100%; I2= 0%) for malignancy. The sROC showed an area under the curve of 0.955 (P= .03). ENB achieved a sufficient sample for ancillary tests in 90.9%(95%CI, 84.8%-96.9%; I2= 80.7%). Risk of pneumothorax was 2.0%(95%CI, 1.0-3.0; I2= 45.2%). We found subgroup differences according to the risk of bias and the number of sampling techniques. Meta-regression showed an association between sensitivity and the mean distance of the sensor tip to the center of the nodule, the number of tissue sampling techniques, and the cancer prevalence in the study. ENB is very safe with good sensitivity for diagnosing malignancy in patients with PPLs. The applicability of our findings is limited because most studies were done with the superDimension navigation system and heterogeneity was high. PROSPERO; No.: CRD42019109449; URL: https://www.crd.york.ac.uk/prospero/.

A novel multi-objective genetic algorithm approach to address class imbalance for disease diagnosis

The adverse consequences of class imbalance problem are prevalent while performing classification for disease diagnosis. Such classifiers predict most of the examples of negative class (instances with non-diseased label) correctly but fail to make correct predictions for the positive class examples (instances with diseased label). Since misclassification costs can be very high for a sensitive field like disease diagnosis, addressing the class imbalance issue becomes of utmost important. A number of sampling techniques have been applied for balancing data. However, these techniques reduce the overall accuracy of classifier models. This is due to the fact that there is an issue of trade-off between sensitivity and specificity of such classifiers. This paper first proposes a GA-based undersampling technique with a weighted fitness function to determine the trade-off between sensitivity and specificity followed by a multi-objective genetic algorithm (MOGA) approach to address the class imbalance problem for disease diagnosis. To determine the trade-off between sensitivity and specificity manually is an arduous task. The MOGA approach takes the two extreme training samples from Pareto optimal solutions, one optimally tuned with respect to sensitivity and the other one optimally tuned with respect to specificity on validation data. Two decision tree classification models are built based on these two training sets. The models are named as sensitivity prioritized model (SEPM) and specificity prioritized model (SPPM) respectively. These models are combined to make predictions on the test data. The results obtained through extensive experimentation confirm that the proposed multi-objective scheme makes correct predictions on minority class (SE) without compromising the correct prediction rate on the majority class (SP).

Reliability-Based Optimization Using Evolutionary Algorithms

Uncertainties in design variables and problem parameters are often inevitable and must be considered in an optimization task if reliable optimal solutions are sought. Besides a number of sampling techniques, there exist several mathematical approximations of a solution's reliability. These techniques are coupled in various ways with optimization in the classical reliability-based optimization field. This paper demonstrates how classical reliability-based concepts can be borrowed and modified and, with integrated single and multiobjective evolutionary algorithms, used to enhance their scope in handling uncertainties involved among decision variables and problem parameters. Three different optimization tasks are discussed in which classical reliability-based optimization procedures usually have difficulties, namely (1) reliability-based optimization problems having multiple local optima, (2) finding and revealing reliable solutions for different reliability indices simultaneously by means of a bi-criterion optimization approach, and (3) multiobjective optimization with uncertainty and specified system or component reliability values. Each of these optimization tasks is illustrated by solving a number of test problems and a well-studied automobile design problem. Results are also compared with a classical reliability-based methodology.

Systemic coagulation parameters in mice after treatment with vascular targeting agents

BackgroundVascular targeting of malignant tumors has become a clinically validated new treatment approach with clear patient benefit. However clinical studies have also revealed that some types of vascular targeting agents (VTAs) are prone to coagulation system side effects. It is therefore essential to predetermine coagulation parameters in preclinical studies. As of to date, this has rarely been done, predominantly due to technical issues.The goal of this study was to establish and apply a standardized process, whereby systemic coagulation activation can be routinely measured in mice.ResultsWe have evaluated a number of sampling techniques and coagulation tests regarding their suitability for this purpose. We were able to adapt two assays measuring soluble fibrin, a marker for a prethrombotic status. Thus, soluble fibrin could be measured for the first time in mice. All assays were validated in a positive control model for systemic coagulation activation, i.e. lipopolysaccharide-induced endotoxemia.Based on our results, we selected a panel of coagulation tests, which are both feasable and informative for preclinical testing of VTAs: soluble fibrin, thrombin-antithrombin complexes, free antithrombin III, white blood cell counts and platelet counts. The effect of tumor transplants on coagulation parameters was evaluated using this panel. We then applied this set of assays in treatment studies with a VTA developed in our laboratory to investigate a potential systemic coagulation activation.ConclusionWe have established a standardized panel of assays that can be used to test murine blood samples for coagulation activation in preclinical studies. All tests are feasible to perform in any research laboratory without specialized equipment. In addition, this is the first report to measure soluble fibrin, an early marker of systemic coagulation activation, in mice. The panel was applied on tumor bearing mice and mice treated with a VTA. We suggest its general application for coagulation activation analyses in mice.

Modelling air quality in street canyons: a review

High pollution levels have been often observed in urban street canyons due to the increased traffic emissions and reduced natural ventilation. Microscale dispersion models with different levels of complexity may be used to assess urban air quality and support decision-making for pollution control strategies and traffic planning. Mathematical models calculate pollutant concentrations by solving either analytically a simplified set of parametric equations or numerically a set of differential equations that describe in detail wind flow and pollutant dispersion. Street canyon models, which might also include simplified photochemistry and particle deposition–resuspension algorithms, are often nested within larger-scale urban dispersion codes. Reduced-scale physical models in wind tunnels may also be used for investigating atmospheric processes within urban canyons and validating mathematical models. A range of monitoring techniques is used to measure pollutant concentrations in urban streets. Point measurement methods (continuous monitoring, passive and active pre-concentration sampling, grab sampling) are available for gaseous pollutants. A number of sampling techniques (mainly based on filtration and impaction) can be used to obtain mass concentration, size distribution and chemical composition of particles. A combination of different sampling/monitoring techniques is often adopted in experimental studies. Relatively simple mathematical models have usually been used in association with field measurements to obtain and interpret time series of pollutant concentrations at a limited number of receptor locations in street canyons. On the other hand, advanced numerical codes have often been applied in combination with wind tunnel and/or field data to simulate small-scale dispersion within the urban canopy.

Sampling program for grape mealybugs improves pest management

The results of a mealybug study in Central Valley vineyards, designed to develop sampling guidelines, reveal that mealybug distribution on vines varies greatly through the season and that mealybugs usually prefer concealed locations, such as under bark. This combination makes sampling difficult. A number of sampling techniques were compared. Three- or 5-minute timed counts were most efficient because samplers could follow the mealybugs' movement over the season. Midseason counts were much better predictors of damage at harvest than early season counts. This research confirms past control guidelines and opens new control options. Grape bunches touching vine trunks or spurs will have higher damage. Removing these bunches or using barriers between bunches and mealybug oviposition sites can also reduce damage.

Environmental noise assessment: Single family residential spatial analysis and methodology error analysis

A number of sampling techniques have recently been developed to assist municipalities in assessing and evaluating the total environment noise climate within the community and to identify the intrusive noise sources. One methodology was developed by Wyle Research, El Segundo, California for the U.S. Environmental Protection Agency and published in July 1976. The University of Texas at Dallas, Graduate Program in Environmental Sciences, has modified the Wyle methodology for use in conducting an environmental noise assessment for the City of Dallas, Texas. This publication reports on the modifications made to the Wyle method and the results of a spatial survey conducted in a spatially homogenous single family residential land use area. The purpose of the spatial analysis was to determine the number of monitoring sites required. Prior to using the modified methodology, an error analysis was conducted for the purpose of determining the accuracy of the methodology. Nine primary monitoring sites were selected for this analysis. Using commercially available digital monitoring equipment each site was monitored for four or more continuous 24‐h periods and the day‐night average sound levels, Ldn's, measured were compared to the 24‐h Ldn's computed using selected 30‐min periods obtained on weekdays, weeknights, weekend days, and weekend nights. From these analyses, the required number of sites was determined and the accuracy of the methodology was validated.