Accurate Density Estimation Research Articles

Hazy Image Restoration by Bi-Histogram Modification

Visibility restoration techniques are widely used for information recovery of hazy images in many computer vision applications. Estimation of haze density is an essential task of visibility restoration techniques. However, conventional visibility restoration techniques often suffer from either the generation of serious artifacts or the loss of object information in the restored images due to uneven haze density, which usually means that the images contain heavy haze formation within their background regions and little haze formation within their foreground regions. This frequently occurs when the images feature real-world scenes with a deep depth of field. How to effectively and accurately estimate the haze density in the transmission map for these images is the most challenging aspect of the traditional state-of-the-art techniques. In response to this problem, this work proposes a novel visibility restoration approach that is based on Bi-Histogram modification, and which integrates a haze density estimation module and a haze formation removal module for effective and accurate estimation of haze density in the transmission map. As our experimental results demonstrate, the proposed approach achieves superior visibility restoration efficacy in comparison with the other state-of-the-art approaches based on both qualitative and quantitative evaluations. The proposed approach proves effective and accurate in terms of both background and foreground restoration of various hazy scenarios.

Electrical Characterization of Dry and Wet Processed Interface Layer in Ge/High-K Devices

Even through Ge/high-k interface has been extensively studied the high leakage current associated with these gate stacks continues to introduce frequency dispersion and hysteresis in capacitance-voltage (CV) and conductance-voltage (GV) characteristics. These dispersions severely limit the understanding the interface and accurate estimation of interface state density, Dit and equivalent oxide thickness (EOT). Since several parameters like EOT, flatband voltage, bulk doping, surface potential as a function of gate voltage are important and bulk defect and interface defect concentration affect the CV and GV characteristics several corrections to the characteristics are required to obtain accurate values. We have measured the CV and GV characteristics of Ge/ALD 1nm-Al2O3/ALD 3.5nm-ZrO2/ALD TiN MOS capacitors with three different interface treatments by HP4284 LCR meter using different frequencies. The interface treatments are (i) simple chemical oxidation (Chemox); (ii) chemical oxide removal (COR) followed by 1 nm oxide by slot-plane-antenna (SPA) plasma (COR&SPAOx); and (iii) COR followed by vapor O3 treatment (COR&O3). The plots were corrected by three different methods. Firstly, we removed the effect of sheet resistance, RS . The second approach uses data (capacitance and conductance) measured at two different frequencies to solve two unknown variables (capacitance and conductance) (1). The solved CV plot is closer to the corrected CV plot at 1 MHz rather than that at 100 KHz. This allows us to use 1MHz corrected data to obtain the EOT information. Additionally, this approach helps to verify that 1MHz data is robust assuming that defects do not respond to the other frequencies used here. The third approach is to modify the measurement circuits (2) to enhance CV and GV characteristics. Fig. 1 shows the CV characteristics after the corrections by three different approaches for three different samples. COR&SPAOx samples show excellent CV characteristics. The details of all the correction methods and the advantages of these methods to correct CV and GV will be discussed. We will demonstrate that the correction methods are suitable for accurately measure the interface characteristics. The Dit calculated by two method (conductance and capacitance spectroscopy) (Fig. 2) are compared and discrepancy of the results between different samples and different methods will be explained. References Y. Ding and D. Misra, J. Vac. Sci. Technol. B, 33, 021203, 2015.K.J. Yang and C. Hu, IEEE Trans on Electron Dev, 46(7), 1500, 1999.K.S.K Kwa, S. Chattopadhyay, N.D. Jankovic, S.H. Olsen, L.S. Driscoll, and A.G. O'Neill, Semicond. Sci. Technol., 18(2), 82, 2003. Figure 1

Detectability in Audio-Visual Surveys of Tropical Rainforest Birds: The Influence of Species, Weather and Habitat Characteristics.

Indices of relative abundance do not control for variation in detectability, which can bias density estimates such that ecological processes are difficult to infer. Distance sampling methods can be used to correct for detectability, but in rainforest, where dense vegetation and diverse assemblages complicate sampling, information is lacking about factors affecting their application. Rare species present an additional challenge, as data may be too sparse to fit detection functions. We present analyses of distance sampling data collected for a diverse tropical rainforest bird assemblage across broad elevational and latitudinal gradients in North Queensland, Australia. Using audio and visual detections, we assessed the influence of various factors on Effective Strip Width (ESW), an intuitively useful parameter, since it can be used to calculate an estimate of density from count data. Body size and species exerted the most important influence on ESW, with larger species detectable over greater distances than smaller species. Secondarily, wet weather and high shrub density decreased ESW for most species. ESW for several species also differed between summer and winter, possibly due to seasonal differences in calling behavior. Distance sampling proved logistically intensive in these environments, but large differences in ESW between species confirmed the need to correct for detection probability to obtain accurate density estimates. Our results suggest an evidence-based approach to controlling for factors influencing detectability, and avenues for further work including modeling detectability as a function of species characteristics such as body size and call characteristics. Such models may be useful in developing a calibration for non-distance sampling data and for estimating detectability of rare species.

STAR FORMATION HISTORY, DUST ATTENUATION, AND EXTRAGALACTIC BACKGROUND LIGHT

At any given epoch, the Extragalactic Background Light (EBL) carries imprints of integrated star formation activities in the universe till that epoch. On the other hand, in order to estimate the EBL, when direct observations are not possible, one requires an accurate estimation of the star formation rate density (SFRD) and the dust attenuation ($A_\nu$) in galaxies. Here, we present a 'progressive fitting method' that determines global average SFRD($z$) and $A_\nu(z)$ for any given extinction curve by using the available multi-wavelength multi-epoch galaxy luminosity function measurements. Using the available observations, we determine the best fitted combinations of SFRD($z$) and $A_\nu(z)$, in a simple fitting form, up to $z\sim8$ for five well known extinction curves. We find, irrespective of the extinction curve used, the $z$ at which the SFRD($z$) peaks is higher than the $z$ above which $A_\nu(z)$ begins to decline. For each case, we compute the EBL from ultra-violet to the far-infrared and optical depth ($\tau_\gamma$) encountered by the high energy $\gamma$-rays due to pair production upon collisions with these EBL photons. We compare these with measurements of the local EBL, $\gamma$-ray horizon and $\tau_\gamma$ measurements using Fermi-LAT. All these and the comparison of independent SFRD($z$) and $A_\nu(z)$ measurements from the literature with our predictions favor the extinction curve similar to that of Large Magellanic Cloud Supershell.

A model for the snow water equivalent derived from stratigraphy observations in northern Sweden

A new parameterization of snow water equivalent (SWE) based on snow depth (HS) has been developed from observations made in northern Sweden. When applying previous SWE parameterization from the Alps on observations from northern Sweden, the SWE values are systematically 20% lower. The new SWE parameterization is derived from a snow layer density regression model using snow layer hardness and snow particle size. The model was evaluated with a detailed field reference dataset, and then applied to the long-term Abisko Scientific Research Station stratigraphic snowpack dataset. The model enables a regional adjustment of snow layer density values for northern Swedish conditions. The snow layer density model provides an accurate estimation of snow bulk density used to derive the SWE parameterization based solely on HS. Snow depth observations are made on a daily basis; by applying our new parameterization, daily values of SWE can be obtained for northern Scandinavian conditions, which can be used, for example, for hydropower production planning and risk assessments.

A generalised random encounter model for estimating animal density with remote sensor data.

Summary Wildlife monitoring technology is advancing rapidly and the use of remote sensors such as camera traps and acoustic detectors is becoming common in both the terrestrial and marine environments. Current methods to estimate abundance or density require individual recognition of animals or knowing the distance of the animal from the sensor, which is often difficult. A method without these requirements, the random encounter model (REM), has been successfully applied to estimate animal densities from count data generated from camera traps. However, count data from acoustic detectors do not fit the assumptions of the REM due to the directionality of animal signals.We developed a generalised REM (gREM), to estimate absolute animal density from count data from both camera traps and acoustic detectors. We derived the gREM for different combinations of sensor detection widths and animal signal widths (a measure of directionality). We tested the accuracy and precision of this model using simulations of different combinations of sensor detection widths and animal signal widths, number of captures and models of animal movement.We find that the gREM produces accurate estimates of absolute animal density for all combinations of sensor detection widths and animal signal widths. However, larger sensor detection and animal signal widths were found to be more precise. While the model is accurate for all capture efforts tested, the precision of the estimate increases with the number of captures. We found no effect of different animal movement models on the accuracy and precision of the gREM.We conclude that the gREM provides an effective method to estimate absolute animal densities from remote sensor count data over a range of sensor and animal signal widths. The gREM is applicable for count data obtained in both marine and terrestrial environments, visually or acoustically (e.g. big cats, sharks, birds, echolocating bats and cetaceans). As sensors such as camera traps and acoustic detectors become more ubiquitous, the gREM will be increasingly useful for monitoring unmarked animal populations across broad spatial, temporal and taxonomic scales.

Enhancing management effectiveness of invasive lionfish using distance sampling and detection probability

Abstract Invasive lionfish Pterois volitans and Pterois miles are one of the greatest threats to coral reef fisheries in the Caribbean. Management currently relies solely on removal, however, there is little understanding of spatial ecology of lionfish to inform management strategies and thus, increase their effectiveness. The use of detection probability and population density estimates from distance sampling as a method to: (a) estimate spatial and depth variations in detectability of lionfish to prioritize removal efforts; (b) reduce costs related to removal in the Caribbean; and (c) provide quantifiable baseline data against which management success can be measured was evaluated. Underwater visual transect surveys were conducted at varying depths at four coral reef sites in the Turks and Caicos Islands using perpendicular distance sampling. Detection functions were fitted to distance data from 299 lionfish sightings across 37 transects to explore variation in detectability among survey sites and depths. Lionfish were detected with a 15.9% (12.2–20.6%) (mean ± 95% CI) probability across all sampling. Detection probability was significantly higher in depths < 15 m, however, lionfish density and mean size increased with depth. Population density on South Caicos reefs was estimated at 1679 (1140.4–2473.1) individuals per km2. Increased detection probability in shallower depths implies caution is needed in assessing management success at depth, and that removal methods and effort should be related specifically to depth and habitat factors. Distance sampling is an effective method for accurate estimation of lionfish population density and detection probability, providing metrics by which to identify priority management areas, and track population changes along with the success of removal efforts. As a result its integration into initial planning and continued monitoring aspects of lionfish management throughout the Caribbean is recommended. Copyright © 2015 John Wiley & Sons, Ltd.

Evaluation of freshwater invertebrate sampling methods in a shallow aridland river (Rio Grande, New Mexico)

Aridland rivers can present several logistical challenges for invertebrate sampling due to shifting substrate and low invertebrate densities. We compared 4 sampling methods for col- lecting both meiofauna and macroinvertebrates in an aridland river (Rio Grande, New Mexico): a water column sampling method, an epibenthic core, a throwtrap, and a stovepipe sampling method. The objective of this study was to find the most efficient combination of sampling meth- ods with which to collect the entire assemblage. Differences were tested among sampling meth- ods in terms of abundance, taxonomic richness, assemblage composition, and invertebrate body size. No single sampling method was able to provide accurate estimates of both abundance and taxonomic richness across the range of body sizes of taxa collected in this study. Invertebrates were concentrated in the epibenthos, and thus using the water column sampling method was not necessary. Samples from the epibenthic core and stovepipe sampling methods contained rela- tively high densities of small-bodied meiofauna, while throwtrap samples included both meio- fauna and macroinvertebrates. Throwtrap and epibenthic core sampling methods together pro- vided the most accurate estimates of taxonomic richness, density, abundance, and invertebrate assemblage composition, and were processed relatively efficiently in the laboratory. Sampling methods that performed well in the Rio Grande are likely to work well in other higher-order (>4) rivers with shifting substrates.

Horizontal target strength of Luciobarbus sp. in ex situ experiments: Testing differences by aspect angle, pulse length and beam position

Horizontal applications of hydroacoustics are used to estimate the density and biomass of fish populations in shallow aquatic ecosystems. In order to calculate fish biomass and density, it is necessary to know the relationship between the biological parameters, such as the length or weight of the studied specimens and the associated target strength (TS). This study presents the results of an ex situ experiment performed on free-swimming specimens of Luciobarbus sp., one of the most diversified and widely distributed genus of the family Cyprinidae. The study was performed using a SIMRAD EK60 split beam echo sounder at 200kHz. TS–length (TS–TL) and TS–weight (TS–W) relationships were derived in the three main orientations: lateral, oblique and head–tail. This study uses a new method in order to calculate the fish aspect angle. This method takes all backscattered fish energy into account and it summarizes the fish movement through regression lines. The TS–TL relationship for the barbel lateral aspect was TSlateral=25.03logTL−99.4 and its results were different from those obtained with other previously published general equations. Thus, the use of this specific equation is recommended in order to obtain accurate estimates of density and biomass in aquatic systems where the barbel is an important component of the fish community. Moreover, the effect of pulse duration on the estimated TS was studied. The results showed no differences between the mean TS obtained by using 0.128ms or 0.256ms of pulse length. Additionally, the influence that the location of the track in the beam has on mean TS was also studied. The results showed that this location does not have a significant influence on fish TS within the first −5dB (one-way) of the beam pattern. This result provides us with a powerful tool to increase the amount of data in TS studies with free-swimming fish where the acquisition process is difficult.

Non-local crime density estimation incorporating housing information

Given a discrete sample of event locations, we wish to produce a probability density that models the relative probability of events occurring in a spatial domain. Standard density estimation techniques do not incorporate priors informed by spatial data. Such methods can result in assigning significant positive probability to locations where events cannot realistically occur. In particular, when modelling residential burglaries, standard density estimation can predict residential burglaries occurring where there are no residences. Incorporating the spatial data can inform the valid region for the density. When modelling very few events, additional priors can help to correctly fill in the gaps. Learning and enforcing correlation between spatial data and event data can yield better estimates from fewer events. We propose a non-local version of maximum penalized likelihood estimation based on the H1 Sobolev seminorm regularizer that computes non-local weights from spatial data to obtain more spatially accurate density estimates. We evaluate this method in application to a residential burglary dataset from San Fernando Valley with the non-local weights informed by housing data or a satellite image.

A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning

MRI significantly improves the accuracy and reliability of target delineation in radiation therapy for certain tumors due to its superior soft tissue contrast compared to CT. A treatment planning process with MRI as the sole imaging modality will eliminate systematic CT/MRI co-registration errors, reduce cost and radiation exposure, and simplify clinical workflow. However, MRI lacks the key electron density information necessary for accurate dose calculation and generating reference images for patient setup. The purpose of this work is to develop a unifying method to derive electron density from standard T1-weighted MRI. We propose to combine both intensity and geometry information into a unifying probabilistic Bayesian framework for electron density mapping. For each voxel, we compute two conditional probability density functions (PDFs) of electron density given its: (1) T1-weighted MRI intensity, and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of the atlas and test patient. The two conditional PDFs containing intensity and geometry information are combined into a unifying posterior PDF, whose mean value corresponds to the optimal electron density value under the mean-square error criterion. We evaluated the algorithm’s accuracy of electron density mapping and its ability to detect bone in the head for eight patients, using an additional patient as the atlas or template. Mean absolute HU error between the estimated and true CT, as well as receiver operating characteristics for bone detection (HU > 200) were calculated. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water). The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 126, compared with 139 for deformable registration (p = 2 × 10−4), 283 for the intensity approach (p = 2 × 10−6) and 282 without density correction (p = 5 × 10−6). For 90% sensitivity in bone detection, the proposed method achieved a specificity of 86%, compared with 80, 11 and 10% using deformable registration, intensity and without density correction, respectively. Notably, the Bayesian approach was more robust against anatomical differences between patients, with a specificity of 62% in the worst case (patient), compared to 30% specificity in registration-based approach. In conclusion, the proposed unifying Bayesian method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous anatomy.

A generalized random encounter model for estimating animal density with remote sensor data

Acoustic detectors are commonly being used to monitor wildlife. Current estimators of abundance or density require recognition of individuals or the distance of the animal from the sensor, which is often difficult. The random encounter model (REM) has been successfully applied to count data without these requirements. However, count data from acoustic detectors do not fit the assumptions of the REM due to the directionality of animal signals. We developed a generalized REM (gREM), to estimate animal density from count data, derived for different combinations of sensor detection widths and animal signal widths. We tested the accuracy and precision of this model using simulations for different combinations of sensor detection and animal signal widths, number of captures, and animal movement models. The gREM produces accurate estimates of absolute animal density. However, larger sensor detection and animal signal widths, and larger number of captures give more precise estimates. Different animal movement models had no effect on the gREM. We conclude that the gREM provides an effective method to estimate animal densities in both marine and terrestrial environments. As acoustic detectors become more ubiquitous, the gREM will be increasingly useful for monitoring animal populations across broad spatial, temporal, and taxonomic scales.

Abstract 4948: PET/CT clinical protocol design for the novel, first in class 68Ga labeled guanylyl cyclase C targeted peptide MLN6907 ([68Ga]MLN6907)

Abstract Acquisition and interpretation guidelines for clinical PET/CT imaging in oncology have been designed for whole-body 18F-FDG imaging and may not be optimized for assessment of other PET imaging tracers. Here we describe a methodology of PET/CT study design for the novel first in class 68Ga-labeled Guanylyl cyclase C (GCC) targeted peptide, [68Ga]MLN6907, based on a combination of in vitro, ex vivo, and in vivo preclinical imaging studies and model-based estimation of tumor parameters from simulated clinical PET data. GCC, a protein expressed in GI malignancies, is being targeted by the antibody drug conjugate MLN0264. GCC is also expressed on the healthy apical surface of the intestinal epithelium, which should be inaccessible to intravascular treatment. [68Ga]MLN6907 binds GCC with high affinity and is being developed as an imaging biomarker in an effort to help identify patients likely to respond to GCC-targeted therapy. In a series of experiments, the peptide affinity, internalization rate, and clearance were determined in patient-derived CRC xenografts with varied tumor microenvironmental phenotype. In addition to supporting the clinical development of the imaging agent, this data was used in combination with simulated clinical list-mode PET data to evaluate tumor parameter estimability under several clinically viable acquisition and reconstruction conditions. Specifically, liver CRC metastases of varying tumor diameter, antigen density, and vascularity were simulated in combination with PET imaging acquisition duration and reconstruction with and without partial-volume correction. Tumor, liver, and background time-activity curves (TACs) were generated from the reconstructed data and analyzed using a distributed tumor model to estimate the known tumor antigen density and vascularity. Analysis of the simulation studies revealed: 1) Partial volume correction is required for accurate antigen density and vascularity estimation; 2) Parameter estimation was most accurate within a tumor size range of 1-5 cm; 3) Parameter estimation was robust for all tested TAC reconstruction durations (e.g., 2, 3, 5, and 10 min); 4) Parameter estimation was optimal for common clinical acquisition times of 30-90 minutes; 5) Antigen density estimation was less accurate in poorly vascularized tumors. For the translation of a novel clinical biomarker, well controlled preclinical studies are critical; and, in this case, the findings combined with the distributed tumor model simulations directly guided the clinical image protocol. This rational and data-driven approach has the ability to not only improve the estimation of tumor properties in human subjects but also to guide the design of first-in-human oncology clinical imaging protocols with novel biomarkers. Citation Format: Jacob Y. Hesterman, Kelly D. Orcutt, Ozlem Yardibi, Jerome T. Mettetal, Shu-Wen Teng, Donna Cvet, Jack Hoppin, Thea Kalebic, Daniel P. Bradley. PET/CT clinical protocol design for the novel, first in class 68Ga labeled guanylyl cyclase C targeted peptide MLN6907 ([68Ga]MLN6907). [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4948. doi:10.1158/1538-7445.AM2014-4948

A Unifying Method to Derive Electron Density From MRI for Radiation Therapy Treatment Planning

MRI has a number of advantages over CT as a primary modality for patient simulation and treatment planning in radiation therapy. Due to its superior soft tissue contrast, MRI significantly improves the accuracy and reliability of target delineation. An MRI based simulation will eliminate systematic image co-registration errors, reduce cost and simplify clinical workflow, with the additional benefit of zero ionizing radiation. However, one key bottleneck problem still remains, which is the lack of electron density information in MRI. The purpose of this work is to develop a reliable method to derive electron density from MRI. We adopt a probabilistic Bayesian approach for electron density mapping based on T1-weighted MRI. The probabilistic approach allows us to explicitly recognize the various sources of uncertainties (such as deformable registration and density mapping) and incorporate them into the analysis. We utilize a head and neck patient with co-registered CT/MRI as the atlas. For each voxel, we compute two conditional probabilities or likelihood functions: (1) electron density given its MR image intensity (T1 value), and (2) electron density given its geometry or position in a reference anatomy, obtained by deformable image registration between the MRI of the test patient and atlas. The two sources of information (image intensity and spatial location) are combined into a unifying posterior probability density function, using the Bayesian formalism. The mean value of the posterior or its mode (value that corresponds to the maximum probability) gives the electron density. To evaluate the proposed method, we calculated the mean absolute HU error between the estimated and true CT, as well as area under the ROC curve for bone detection (HU > 200). The performance was compared with a global intensity approach based on T1 value and no density correction (set whole head to water). The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 117, compared with 232 for the intensity approach and 273 without density correction. For bone detection, the proposed method had an accuracy of 87%, with a sensitivity of 90% and specificity of 90% (AUC = 94%). In comparison, the AUC for bone detection is only 70% and 50% using the intensity approach and without density correction, respectively. The proposed unifying method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous regions. This paves the way for accurate dose calculation and generating reference images for patient setup in MRI-based treatment planning.

Monitoring a population of translocatedGrandCayman blue iguanas: assessing the accuracy and precision of distance sampling and repeated counts

AbstractIn wildlife translocations, it is important to identify reliable monitoring methods that can provide accurate population estimates for post‐release management. We compared density and abundance estimates of distance sampling and repeated counts with complete counts (census data) to determine accuracy (percentage relative bias) and precision (coefficient of variation) for monitoring populations of blue iguanas restored to protected areas onGrandCayman. We conducted incomplete counts (survey data) of blue iguanas at 12 transects of unequal length (range = 322.1 to 431.7 m) visited 36 times per year, yielding 405 detections inMarch 2010 and 443 detections inMarch 2013. Distance sampling and repeated counts provided accurate (range of percentage relative bias = −9.52% to 0.00%) and precise (range of coefficient of variation = 0.10 to 0.15) estimates of iguana density and abundance, and yielded an estimate of the proportion of individuals that were available for detection in the study area during the surveys (,se = 0.03). The use of distance sampling and repeated count methods, in carefully designed surveys with representative coverage and adequate replication, can lead to more reliable monitoring of extant wild and translocated rock iguana populations on otherCaribbean islands, where individual marking may not be practical, and the proportion of individuals that is available for detection is unknown.

Dm-KDE: dynamical kernel density estimation by sequences of KDE estimators with fixed number of components over data streams

In many data stream mining applications, traditional density estimation methods such as kernel density estimation, reduced set density estimation can not be applied to the density estimation of data streams because of their high computational burden, processing time and intensive memory allocation requirement. In order to reduce the time and space complexity, a novel density estimation method Dm-KDE over data streams based on the proposed algorithm m-KDE which can be used to design a KDE estimator with the fixed number of kernel components for a dataset is proposed. In this method, Dm-KDE sequence entries are created by algorithm m-KDE instead of all kernels obtained from other density estimation methods. In order to further reduce the storage space, Dm-KDE sequence entries can be merged by calculating their KL divergences. Finally, the probability density functions over arbitrary time or entire time can be estimated through the obtained estimation model. In contrast to the state-of-the-art algorithm SOMKE, the distinctive advantage of the proposed algorithm Dm-KDE exists in that it can achieve the same accuracy with much less fixed number of kernel components such that it is suitable for the scenarios where higher on-line computation about the kernel density estimation over data streams is required.We compare Dm-KDE with SOMKE and M-kernel in terms of density estimation accuracy and running time for various stationary datasets. We also apply Dm-KDE to evolving data streams. Experimental results illustrate the effectiveness of the proposed method.

An Optimization Approach to the Two-Circle Method of Estimating Ground-Dwelling Arthropod Densities

Information on ground-dwelling arthropod densities is important for efficient management in agro-ecosystems. A method of using paired pitfall traps with different inter-trap distances, called the two-circle method (TCM), was proposed recently for accurate and efficient estimation of arthropod densities. Using the numbers of individuals caught in paired traps and the inter-trap distances between the paired traps as input, the TCM can simultaneously estimate the effective trapping radius and the population density by fitting a nonlinear model. However, the previous fitting procedure (using the nonlinear least squares approach) provides the estimates and standard errors of only these two variables, and often suffers from its hypersensitivity to the initial values assigned in the nonlinear regression. To estimate the confidence intervals of these estimates and to assess the effects of the number of replications per distance class and the number of distance classes on the accuracy of density estimates, we provide a new procedure for fitting the model by using the optimization function. Evaluation based on simulated and field data suggests that the TCM could provide a reliable estimate of density by using at least 15 paired traps per distance class and at least 4 distance classes.

TH‐A‐BRF‐01: A Probabilistic Bayesian Approach to Derive Electron Density From MRI for Radiation Therapy Treatment Planning

Purpose:MRI significantly improves the accuracy and reliability of target delineation for patient simulation and treatment planning in radiation therapy, due to its superior soft tissue contrast as compared to CT. An MRI based simulation will reduce cost and simplify clinical workflow with zero ionizing radiation. However, MRI lacks the key electron density information. The purpose of this work is to develop a reliable method to derive electron density from MRI.Methods:We adopt a probabilistic Bayesian approach for electron density mapping based on T1‐weighted head MRI. For each voxel, we compute conditional probability of electron densities given its: (1) T1 intensity and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of test patient and atlas. Intensity and geometry information are combined into a unifying posterior probability density function whose mean gives the electron density. Mean absolute HU error between the estimated and true CT, as well as ROC's for bone detection (HU>200) were calculated for 8 patients. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water).Results:The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 132, compared with 139 for deformable registration (p=10−3), 371 for the intensity approach (p=10−5) and 282 without density correction (p=2×10−4). For 90% sensitivity in bone detection, the proposed method had a specificity of 85% and that for deformable registration, intensity and without density correction are 80%, 24% and 10% respectively.Conclusion:The proposed unifying method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous regions. This paves the way for accurate dose calculation and generating reference images for patient setup in MRI‐based treatment planning.

Animal density and track counts: understanding the nature of observations based on animal movements.

Counting animals to estimate their population sizes is often essential for their management and conservation. Since practitioners frequently rely on indirect observations of animals, it is important to better understand the relationship between such indirect indices and animal abundance. The Formozov-Malyshev-Pereleshin (FMP) formula provides a theoretical foundation for understanding the relationship between animal track counts and the true density of species. Although this analytical method potentially has universal applicability wherever animals are readily detectable by their tracks, it has long been unique to Russia and remains widely underappreciated. In this paper, we provide a test of the FMP formula by isolating the influence of animal travel path tortuosity (i.e., convolutedness) on track counts. We employed simulations using virtual and empirical data, in addition to a field test comparing FMP estimates with independent estimates from line transect distance sampling. We verify that track counts (total intersections between animals and transects) are determined entirely by density and daily movement distances. Hence, the FMP estimator is theoretically robust against potential biases from specific shapes or patterns of animal movement paths if transects are randomly situated with respect to those movements (i.e., the transects do not influence animals’ movements). However, detectability (the detection probability of individual animals) is not determined simply by daily travel distance but also by tortuosity, so ensuring that all intersections with transects are counted regardless of the number of individual animals that made them becomes critical for an accurate density estimate. Additionally, although tortuosity has no bearing on mean track encounter rates, it does affect encounter rate variance and therefore estimate precision. We discuss how these fundamental principles made explicit by the FMP formula have widespread implications for methods of assessing animal abundance that rely on indirect observations.

Spatial and temporal analysis of wheat bulb fly (Delia coarctata, Fallén) oviposition: consequences for pest population monitoring

The soil dwelling larvae of the wheat bulb fly (Delia coarctata, Fallen) can cause damage to a number of cereal crops. Monitoring of the oviposition of this pest is important to allow forecasts of larval attack after the overwintering period, allowing suitable management practices to be implemented. However, little is known about the population dynamics of wheat bulb fly, both spatially and through time. Analysis of the spatial depen- dence of oviposition was conducted, allowing the con- struction of detailed contour or scale-sized dot maps of oviposition in the sampled fields. The population estimate obtained by the sample for estimate technique, used to test spatial dependence, was compared to the traditional wheat bulb fly sample for decision-making methodology. Further to this, the peak period of egg population density was established for Scotland. Spatial dependence was observed in 50 % of fields assessed, while field monitoring of egg population densities using the traditional single-line tran- sect pattern were found to vary greatly from the mean density collected from a more intensive 50 by 50 m sam- pling grid. These findings suggest that the single-line transect sampling pattern is not robust enough when esti- mating the patchy egg distribution observed in the sampled fields. The peak period of egg population density, observed over three successive seasons, was found to be in late August, with population estimates taken prior to, or after this period showing a marked reduction in the observed maximum population. The results highlight the difficulty of obtaining accurate population density estimates for aggre- gated pest species and indicate that monitoring pro- grammes, that have been implemented without thorough investigation into pest population dynamics, can lead to the execution of incorrect management strategies.