True Count Research Articles

Leveraging Topological Events in Tracking Graphs for Understanding Particle Diffusion

AbstractSingle particle tracking (SPT) of fluorescent molecules provides significant insights into the diffusion and relative motion of tagged proteins and other structures of interest in biology. However, despite the latest advances in high‐resolution microscopy, individual particles are typically not distinguished from clusters of particles. This lack of resolution obscures potential evidence for how merging and splitting of particles affect their diffusion and any implications on the biological environment. The particle tracks are typically decomposed into individual segments at observed merge and split events, and analysis is performed without knowing the true count of particles in the resulting segments. Here, we address the challenges in analyzing particle tracks in the context of cancer biology. In particular, we study the tracks of KRAS protein, which is implicated in nearly 20% of all human cancers, and whose clustering and aggregation have been linked to the signaling pathway leading to uncontrolled cell growth. We present a new analysis approach for particle tracks by representing them as tracking graphs and using topological events – merging and splitting, to disambiguate the tracks. Using this analysis, we infer a lower bound on the count of particles as they cluster and create conditional distributions of diffusion speeds before and after merge and split events. Using thousands of time‐steps of simulated and in‐vitro SPT data, we demonstrate the efficacy of our method, as it offers the biologists a new, detailed look into the relationship between KRAS clustering and diffusion speeds.

Comparison of Various Classification Techniques for Prediction of the Agriculture Production Based on Different Parameters Rainfall, Temperature

The classification techniques based on various machine learning techniques are having use for the Big data analysis. This will be useful in identifying the classification and then finally the prediction which will be useful for the decision managers for having quality decisions. There are various types of supervised and unsupervised learning techniques which are having capabilities in the terms of driving the analysis. This analysis will be useful for having identification of relationship between the various attributes which is required to device the analysis. There are various supervised learning techniques which are useful to drive the analysis. These techniques are SVM, Logistic regression, KNN, Naïve Bayes, Tree, Neural network. The relative comparison of this technique is done in the terms of various parameters for example AUC, CA, F1, Recall and precision. The accuracy in the terms of AUC, CA is highest for the Naïve Bayes. This shows the Naïve Bayes is having higher true positives, true negative ratio. The proposed technique is having higher accuracy of 81% which is far above than all the remaining techniques. The confusion matrix for the Naïve Bayes is having true positive count as 729, true negative at 103. This shows that the true positive and true negative count is far above for this technique compared to the other techniques.

Differentially private and utility-aware publication of trajectory data

Trajectory data is valuable for various applications, especially for intelligent transportation systems, which hunger for plenty of trajectories. However, publishing trajectory data while respecting users’ privacy has been a long-standing challenge. Currently, the prevailing releasing solutions usually merge trajectory locations based on k-means and add unbounded noise with Laplace distribution to the count of trajectory to achieve differential privacy protection. The trajectory merging methods based on k-means have a low efficiency and unbounded noise with Laplace distribution will leak user’ privacy and suffer from serious utility loss. To solve the above two problems, we devise two differentially private and utility-aware publication methods of trajectory data. More specifically, we first propose two trajectory merging schemes based on k-means || clustering. The first one is to use k-means || clustering algorithm to cluster the location area, and all the points in the cluster are replaced by the center of cluster. The other is to utilize Staircase mechanism to perturb the cluster centers in order to improve the level of privacy protection. Afterwards, we propose a bounded Staircase noise generation algorithm to perturb the true count of generalized trajectories. We prove our proposed methods preserve differential privacy theoretically. Experimental comparison show that our proposed publication methods significantly outperform existing approaches in terms of data utility and efficiency.

Bias evaluation and reduction in 3D OP-OSEM reconstruction in dynamic equilibrium PET studies with 11C-labeled for binding potential analysis.

Iterative image reconstruction is widely used in positron emission tomography. However, it is known to contribute to quantitation bias and is particularly pronounced during dynamic studies with 11C-labeled radiotracers where count rates become low towards the end of the acquisition. As the strength of the quantitation bias depends on the counts in the reconstructed frame, it can differ from frame to frame of the acquisition. This is especially relevant in the case of neuro-receptor studies with simultaneous PET/MR when a bolus-infusion protocol is applied to allow the comparison of pre- and post-task effects. Here, count dependent changes in quantitation bias may interfere with task changes. We evaluated the impact of different framing schemes on quantitation bias and its propagation into binding potential (BP) using a phantom decay study with 11C and 3D OP-OSEM. Further, we propose a framing scheme that keeps the true counts per frame constant over the acquisition time as constant framing schemes and conventional increasing framing schemes are unlikely to achieve stable bias values during the acquisition time range. For a constant framing scheme with 5 minutes frames, the BP bias was 7.13±2.01% (10.8% to 3.8%) compared to 5.63±2.85% (7.8% to 4.0%) for conventional increasing framing schemes. Using the proposed constant true counts framing scheme, a stabilization of the BP bias was achieved at 2.56±3.92% (3.5% to 1.7%). The change in BP bias was further studied by evaluating the linear slope during the acquisition time interval. The lowest slope values were observed in the constant true counts framing scheme. The constant true counts framing scheme was effective for BP bias stabilization at relevant activity and time ranges. The mean BP bias under these conditions was 2.56±3.92%, which represents the lower limit for the detection of changes in BP during equilibrium and is especially important in the case of cognitive tasks where the expected changes are low.

Morphological traits determine detectability bias in North American grassland butterflies

AbstractEcological research often includes observing and counting individuals to obtain a population estimate or index that can inform conservation, management, and policy. However, the ability to accurately estimate wildlife populations has always been hindered by bias, and researchers aim to overcome bias by using correction factors that calculate the relationship between observer counts and true counts. Accurate estimates of butterfly populations, for example, are especially necessary to inform management and policy, with over 40 species listed or proposed for listing under the United States Endangered Species Act. Researchers can utilize methods like line‐transect distance sampling (LTDS) to help account for detection bias and calculate more accurate estimates, but species‐specific traits or behaviors may influence survey effectiveness. We used LTDS to detect nearly 35,000 individuals of 33 species across five studies to calculate butterfly species' effective strip widths (ESW)—a type of correction factor—across grasslands in the Great Plains, USA. To better understand how species' traits influence detectability, we modeled the influence of species' morphological, life history, and behavioral traits on ESW. The average ESW was 5.42 m, but varied from 1.84 to 12.6 m. We found that morphology (size and color) impacted the ability of observers to detect butterflies, with larger and brightly colored butterflies detected farther away from observers compared to smaller and dull‐colored butterflies. Additionally, observers were generally better at detecting individuals while they were flying and nectaring compared to resting. Surprisingly, species' life history and ecological traits did not help explain detectability differences. As conservation efforts continue to increase for butterflies, improved estimates of their population size will be necessary to evaluate management strategies and aid conservation decision‐making for future policy. Future surveys need to consider butterfly size and color, adjusting weather protocols when necessary, to minimize and account for bias associated with butterfly species, especially if accurate population estimates are a study goal.

Inferring True COVID19 Infection Rates From Deaths.

The novel coronavirus, SARS-CoV-2, commonly known as COVID19 has become a global pandemic in early 2020. The world has mounted a global social distancing intervention on a scale thought unimaginable prior to this outbreak; however, the economic impact and sustainability limits of this policy create significant challenges for government leaders around the world. Understanding the future spread and growth of COVID19 is further complicated by data quality issues due to high numbers of asymptomatic patients who may transmit the disease yet show no symptoms; lack of testing resources; failure of recovered patients to be counted; delays in reporting hospitalizations and deaths; and the co-morbidity of other life-threatening illnesses. We propose a Monte Carlo method for inferring true case counts from observed deaths using clinical estimates of Infection Fatality Ratios and Time to Death. Findings indicate that current COVID19 confirmed positive counts represent a small fraction of actual cases, and that even relatively effective surveillance regimes fail to identify all infectious individuals. We further demonstrate that the miscount also distorts officials' ability to discern the peak of an epidemic, confounding efforts to assess the efficacy of various interventions.

A Sixth Dynasty Stela from Abydos

The subject of this article is to publish a complete scientific publishing for an old kingdom stela which is related to a high official who was contemporary to the late of the Old Kingdom , Ny-Ppy was the name of the owner of the current stela, who might have begun his career in the Memphite capital before he ended his long career path in Abydos as the true count of the Eighth nome of Upper Egypt where the current stela was discovered by August Mariette. The paper shows in details the complete description of the stela in general and its inscriptions which are inscribed on it as well as the transliteration and the translation of these inscriptions attested on it. The Author gives a complete analysis for the whole titles and epithets mentioned by Ny-Ppy on his current stela which describe his administrative tasks during his long career path. and finally , at the end of the article it suggests a specific dating for the stela according to the Artistic , epigraphical and textual items that attested on the stela and so a particular date for the owner of the stela.

Convolutional neural networks for improving image quality with noisy PET data

GoalPET is a relatively noisy process compared to other imaging modalities, and sparsity of acquisition data leads to noise in the images. Recent work has focused on machine learning techniques to improve PET images, and this study investigates a deep learning approach to improve the quality of reconstructed image volumes through denoising by a 3D convolution neural network. Potential improvements were evaluated within a clinical context by physician performance in a reading task.MethodsA wide range of controlled noise levels was emulated from a set of chest PET data in patients with lung cancer, and a convolutional neural network was trained to denoise the reconstructed images using the full-count reconstructions as the ground truth. The benefits, over conventional Gaussian smoothing, were quantified across all noise levels by observer performance in an image ranking and lesion detection task.ResultsThe CNN-denoised images were generally ranked by the physicians equal to or better than the Gaussian-smoothed images for all count levels, with the largest effects observed in the lowest-count image sets. For the CNN-denoised images, overall lesion contrast recovery was 60% and 90% at the 1 and 20 million count levels, respectively. Notwithstanding the reduced lesion contrast recovery in noisy data, the CNN-denoised images also yielded better lesion detectability in low count levels. For example, at 1 million true counts, the average true positive detection rate was around 40% for the CNN-denoised images and 30% for the smoothed images.ConclusionSignificant improvements were found for CNN-denoising for very noisy images, and to some degree for all noise levels. The technique presented here offered however limited benefit for detection performance for images at the count levels routinely encountered in the clinic.

A new method for calculating butterfly abundance trends for small regional areas

Changes in butterfly populations are routinely monitored using transect counts, for example from the UK Butterfly Monitoring Scheme. However, abundance trends are typically only calculated at national and country level in the UK. A new method is presented that estimates species’ trends for smaller regions or datasets, where there may be limited transects. National approaches rely on larger numbers of transects and estimate flight periods either at site level, at the cost of excluding data, or assume a fixed flight period across transects to maximise data usage. The new approach uses butterfly records from all available sources to estimate a parameterised curve representing the flight period and create a so-called Dummy site. Counts from the Dummy site are included with true transect counts in a generalised additive model to estimate annual flight periods as fixed across sites, from which counts and abundance indices are estimated. Inclusion of the Dummy site produces a better overall fit, with greater influence for species with limited transects. Regional indices were often comparable with those produced from a national analysis, but with more realistic indices for some species. Trends were usually similar in magnitude and sign, but for certain species the new approach estimated more robust trends, benefiting from the inclusion of more data through estimating a common regional flight period. The approach is demonstrated for butterflies in Surrey (UK) but has wider relevance, for example to newly-established or small-scale monitoring schemes which may exploit alternative data sources to inform species’ flight period estimation.

Quantitative accuracy of radiomic features of low-dose 18F-FDG PET imaging.

Background18F-FDG PET based radiomics is promising for precision oncology imaging. This work aims to explore quantitative accuracies of radiomic features (RFs) for low-dose 18F-FDG PET imaging.MethodsTwenty lung cancer patients were prospectively enrolled and underwent 18F-FDG PET/CT scans. Low-dose PET situations (true counts: 20×106, 15×106, 10×106, 7.5×106, 5×106, 2×106, 1×106, 0.5×106, 0.25×106) were simulated by randomly discarding counts from the acquired list-mode data. Each PET image was created using the scanner default reconstruction parameters. Each lesion volume of interest (VOI) was obtained via an adaptive contouring method with a threshold of 50% peak standardized uptake value (SUVpeak) in the PET images with full count data and VOIs were copied to the PET images at the reduced count level. Conventional SUV measures, features calculated from first-order statistics (FOS) and texture features (TFs) were calculated. Texture based RF include features calculated from gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), neighboring gray-level dependence matrix (NGLDM) and neighbor gray-tone difference matrix (NGTDM). Bias percentage (BP) at different count levels for each RF was calculated.ResultsFifty-seven lesions with a volume greater than 1.5 cm3 were found (mean volume, 25.7 cm3, volume range, 1.5–245.4 cm3). In comparison with normal total counts, mean SUV (SUVmean) in the lesions, normal lungs and livers, Entropy and sum entropy from GLCM, busyness from NGTDM and run-length non-uniformity from GLRLM were the most robust features, with a BP of 5% at the count level of 1×106 (equivalent to an effective dose of 0.04 mSv) RF including cluster shade from GLCM, long-run low grey-level emphasis, high grey-level run emphasis and short-run low grey-level emphasis from GLRM exhibited the worst performance with 50% of bias with 20×106 counts (equivalent to an effective dose of 0.8 mSv).ConclusionsIn terms of the lesions included in this study, SUVmean, entropy and sum entropy from GLCM, busyness from NGTDM and run-length non-uniformity from GLRLM were the least sensitive features to lowering count.

Supervised learning with cyclegan for low-dose FDG PET image denoising.

PET imaging involves radiotracer injections, raising concerns about the risk of radiation exposure. To minimize the potential risk, one way is to reduce the injected tracer. However, this will lead to poor image quality with conventional image reconstruction and processing. In this paper, we proposed a supervised deep learning model, CycleWGANs, to boost low-dose PET image quality. Validations were performed on a low dose dataset simulated from a real dataset with biopsy-proven primary lung cancer or suspicious radiological abnormalities. Low dose PET images were reconstructed on reduced PET raw data by randomly discarding events in the PET list mode data towards the count level of 1 million. Traditional image denoising methods (Non-Local Mean (NLM) and block-matching 3D(BM3D)) and two recently-published deep learning methods (RED-CNN and 3D-cGAN) were included for comparisons. At the count level of 1 million (true counts), the proposed model can accurately estimate full-dose PET image from low-dose input image, which is superior to the other four methods in terms of the mean and maximum standardized uptake value (SUVmean and SUVmax) bias for lesions and normal tissues. The bias of SUV (SUVmean, SUVmax) for lesions and normal tissues are (-2.06±3.50%,-0.84±6.94%) and (-0.45±5.59%, N/A) in the estimated PET images, respectively. However, the RED-CNN achieved the best score in traditional metrics, such as structure similarity (SSIM), peak signal to noise ratio (PSNR) and normalized root mean square error (NRMSE). Correlation and profile analyses have successfully explained this phenomenon and further suggested that our method could effectively preserve edge and also SUV values than RED-CNN, 3D-cGAN and NLM with a slightly higher noise.

New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys.

Reliable estimates of abundance are critical in effectively managing threatened species, but the feasibility of integrating data from wildlife surveys completed using advanced technologies such as remotely piloted aircraft systems (RPAS) and machine learning into abundance estimation methods such as N‐mixture modeling is largely unknown due to the unique sources of detection errors associated with these technologies.We evaluated two modeling approaches for estimating the abundance of koalas detected automatically in RPAS imagery: (a) a generalized N‐mixture model and (b) a modified Horvitz–Thompson (H‐T) estimator method combining generalized linear models and generalized additive models for overall probability of detection, false detection, and duplicate detection. The final estimates from each model were compared to the true number of koalas present as determined by telemetry‐assisted ground surveys.The modified H‐T estimator approach performed best, with the true count of koalas captured within the 95% confidence intervals around the abundance estimates in all 4 surveys in the testing dataset (n = 138 detected objects), a particularly strong result given the difficulty in attaining accuracy found with previous methods.The results suggested that N‐mixture models in their current form may not be the most appropriate approach to estimating the abundance of wildlife detected in RPAS surveys with automated detection, and accurate estimates could be made with approaches that account for spurious detections.

Photon Counting LIDAR Based on True Random Coding.

In this paper, a true random coding photon counting LIDAR is described, in which a Gm-APD (Geiger mode avalanche photodiode) acts as the true random sequence signal generator. The true random coding method not only improves the anti-crosstalk capability of the system, but also greatly reduces the 1-bit missed detection caused by the limited Gm-APD count rate. The experiment verifies the feasibility of the true random sequence used in a photon counting LIDAR ranging system, and a simple and intuitive evaluation model of true random sequence autocorrelation is proposed. Finally, the influence of system parameters (mean echo photon number, mean pulse count density, sequence length, mean noise count) on detection probability is explored. In general, this paper proves that the true random code photon counting LIDAR is an effective target detection method, and provides a new idea for the research of an anti-crosstalk LIDAR system.

Creating Predictive Weed Emergence Models Using Repeat Photography and Image Analysis.

Weed emergence models have the potential to be important tools for automating weed control actions; however, producing the necessary data (e.g., seedling counts) is time consuming and tedious. If similar weed emergence models could be created by deriving emergence data from images rather than physical counts, the amount of generated data could be increased to create more robust models. In this research, repeat RGB images taken throughout the emergence period of Raphanus raphanistrum L. and Senna obtusifolia (L.) Irwin and Barneby underwent pixel-based spectral classification. Relative cumulative pixels generated by the weed of interest over time were used to model emergence patterns. The models that were derived from cumulative pixel data were validated with the relative emergence of true seedling counts. The cumulative pixel model for R. raphanistrum and S. obtusifolia accounted for 92% of the variation in relative emergence of true counts. The results demonstrate that a simple image analysis approach based on time-dependent changes in weed cover can be used to generate weed emergence predictive models equivalent to those produced based on seedling counts. This process will help researchers working on weed emergence models, providing a new low-cost and technologically simple tool for data collection.

Fast, Accurate and Provable Triangle Counting in Fully Dynamic Graph Streams

Given a stream of edge additions and deletions, how can we estimate the count of triangles in it? If we can store only a subset of the edges, how can we obtain unbiased estimates with small variances? Counting triangles (i.e., cliques of size three) in a graph is a classical problem with applications in a wide range of research areas, including social network analysis, data mining, and databases. Recently, streaming algorithms for triangle counting have been extensively studied since they can naturally be used for large dynamic graphs. However, existing algorithms cannot handle edge deletions or suffer from low accuracy. Can we handle edge deletions while achieving high accuracy? We propose T hink D, which accurately estimates the counts of global triangles (i.e., all triangles) and local triangles associated with each node in a fully dynamic graph stream with additions and deletions of edges. Compared to its best competitors, T hink D is (a) Accurate: up to 4.3 × more accurate within the same memory budget, (b) Fast: up to 2.2 × faster for the same accuracy requirements, and (c) Theoretically sound: always maintaining estimates with zero bias (i.e., the difference between the true triangle count and the expected value of its estimate) and small variance. As an application, we use T hink D to detect suddenly emerging dense subgraphs, and we show its advantages over state-of-the-art methods.

Haemorrhages detection using geometrical techniques

ABSTRACT Aim With an increasing percentage of retinal pathology because of diabetes, eye screening for diabetic retinopathy (DR) is in demand throughout the world. Haemorrhages (HEs) are one of the common signs for the red lesion detection for early diagnosis of this progressive degenerative disease of the retina. The detection of HEs in early stage prevents further progression of the eye disease and reduces the risk of blindness. Methods: The proposed method is based on morphological segmentation and geometrical feature techniques for HEs extraction. This approach uses preprocessing, removal of other retinal image details, determining connected components analysis and applying a specific shape feature set which results in improving the recognition of HEs. Results: The proposed algorithm demonstrated 95.47% accuracy for a Diaretdb1 database at image level detection. Moreover, the proposed method achieved better performance results for HEs extraction when individual images were analysed and compared with the true HEs count according to the multiple expert evaluations. Conclusion: The results obtained prove the potential use of the proposed algorithm in terms of true HEs count to facilitate the DR grading criteria related to HEs.

Computer System for Perception of Lung Cancer

Detection of cancer in its previous stages to increases survival rate of patient. CAD system is efficient because it take minimum time to detect weather the patient has cancer or not. It is very difficult for detection of lung cancer its earlier stage as it takes many tests. There are many of the CAD system which is designed for earlier detection of tumors. Many CAD systems have been designed in past for early detection of lung tumor. For segmentation purpose Thresholding is used and detection of area in which suspected tumor part growing algorithms is used. There are various factor is calculated using GLCM. Multilayer feed forward BPNN approaches for classify the feature set. Performance is calculated in form of mean square error (MSE) using BPNN. The CAD (computer aided diagnosis) model gives 90% true count. For implementation purpose MATLAB is used.

Dendrochronological procedures improve the precision and accuracy of tree and stand age estimates in the western Canadian boreal forest

Tree and stand age are critical information needed for understanding forest ecosystems. Tree age estimates are usually derived from ring counts, but these are not dendrochronological estimates unless procedures like ring measurement and cross-dating are used. Stand age is usually estimated from the mean ring count on a sample of trees. However, the oldest trees may better represent time since stand replacing disturbance. Using a forest inventory dataset from Manitoba, Canada, standard ring counts were found to underestimate true ring counts obtained using dendrochronological procedures for all black spruce (Picea mariana) trees, and older jack pine (Pinus banksiana) and trembling aspen (Populus tremuloides) trees. Data from whole stand reconstruction plots for these same species demonstrated that mean ring counts from a sample of trees underestimate the time since last stand replacing disturbance, particularly for black spruce stands where ring counts at breast height were highly variable among individuals. The evidence in this study, and from similar existing studies, suggests that tree age, stand age, and time since last stand replacing disturbance are more likely underestimated than overestimated in the boreal forest of western Canada. Dendrochronological procedures can improve both the precision and accuracy of tree age estimates, but some aspects of age uncertainty may be irreducible, especially in older stands. Age uncertainty should be more widely acknowledged and where possible incorporated into forest science models.

Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks

Lowering either the administered activity or scan time is desirable in PET imaging as it decreases the patient’s radiation burden or improves patient comfort and reduces motion artifacts. But reducing these parameters lowers overall photon counts and increases noise, adversely impacting image contrast and quantification. To address this low count statistics problem, we propose a cycle-consistent generative adversarial network (Cycle GAN) model to estimate diagnostic quality PET images using low count data.Cycle GAN learns a transformation to synthesize diagnostic PET images using low count data that would be indistinguishable from our standard clinical protocol. The algorithm also learns an inverse transformation such that cycle low count PET data (inverse of synthetic estimate) generated from synthetic full count PET is close to the true low count PET. We introduced residual blocks into the generator to catch the differences between low count and full count PET in the training dataset and better handle noise.The average mean error and normalized mean square error in whole body were −0.14% ± 1.43% and 0.52% ± 0.19% with Cycle GAN model, compared to 5.59% ± 2.11% and 3.51% ± 4.14% on the original low count PET images. Normalized cross-correlation is improved from 0.970 to 0.996, and peak signal-to-noise ratio is increased from 39.4 dB to 46.0 dB with proposed method.We developed a deep learning-based approach to accurately estimate diagnostic quality PET datasets from one eighth of photon counts, and has great potential to improve low count PET image quality to the level of diagnostic PET used in clinical settings.

INCIDENCE OF ICE CREAM CONTAMINATION WITH COLIFORMS AND FECAL COLIFORMS IN ASSIUT CITY

A total of 90 random samples of raw cow''s milk used in small scale ice cream , small scale ice cream and large scale plain ice cream(30 samples each) collected fromAssiut city, Egypt , in the period between from April and September 2019. Each sample was put in ice tank with thermometer to maintain temp. at 4C. The samples were dispatched to the laboratory with a minimum of delay where they were examined for coliforms, fecal coliforms and E.coli true faecal type.The results show that the mean counts of total coliforms in raw cow''s milk, small scale and large scale plain ice cream were 2.4x103, 2.8x102 and 1.56x102 cfu/ml or g. , respectively. While the mean counts of fecal coliforms were 2.1x103 , 1.9x102 and 1.32 x102 cfu/ml or g. , respectively and the mean counts of E.coli true faecal type count cfu/ml or g. were 2.5x102 , 3.3x102 and 1.12x102 cfu/mlor g., respectively.The heath hazards of coliforms, fecal coliforms and E.coli true faecal type, methods of contamination and control werediscussed.