Histogram Of Counts Research Articles

A Novel Fit-Flexible Fluorescence Soft Imager: Tri-Sensing of Intensity, Fall-Time, and Life Profile.

Time-resolved fluorescence imaging techniques, like confocal fluorescence lifetime imaging microscopy, are powerful photonic instrumentation tools of modern science with diverse applications, including: biology, medicine, and chemistry. However, complexities of the systems, both at specimen and device levels, cause difficulties in quantifying soft biomarkers. To address the problems, we first aim to understand and model the underlying photophysics of fluorescence decay curves. For this purpose, we provide a set of mathematical functions, called "life models", fittable with the real temporal recordings of histogram of photon counts. For each model, an equivalent electrical circuit, called a "life circuit", is derived for explaining the whole process. In confocal endomicroscopy, the components of excitation laser, specimen, and fluorescence-emission signal as the histogram of photon counts are modelled by a power source, network of resistor-inductor-capacitor circuitry, and multimetre, respectively. We then design a novel pixel-level temporal classification algorithm, called a "fit-flexible approach", where qualities of "intensity", "fall-time", and "life profile" are identified for each point. A model selection mechanism is used at each pixel to flexibly choose the best representative life model based on a proposed Misfit-percent metric. A two-dimensional arrangement of the quantified information detects some kind of structural information. This approach showed a potential of separating microbeads from lung tissue, distinguishing the tri-sensing from conventional methods. We alleviated by 7% the error of the Misfit-percent for recovering the histograms on real samples than the best state-of-the-art competitor. Codes are available online.

Tumor grading model employing geometric analysis of histopathological images with characteristic nuclei dictionary

Histopathological study has been shown to improve diagnosis of various disease classifications effectively as any disease condition is correlated to characteristic set of changes in the tissue structure. This study aims at developing an automated neural network system for grading brain tumors (Glioblastoma Multiforme) from histopathological images within the Whole Slide Images (WSI) of hematoxylin and eosin (H&E) stains with significant accuracy. Hematoxylin channels are extracted from the histopathological image patches using color de-convolution. Cell nuclei are precisely segmented using three level Otsu thresholding. From each segmented image, nuclei boundaries are extracted to extract nucleus level features based on their shape and size. Geometric features including ellipse eccentricities, nucleus perimeter, area, and polygon edge counts are extracted using geometric algorithms to define the nuclei boundaries of the segmented image. These features are collected for a large number of nuclei and the nuclei are clustered using the K-Means algorithm in order to create a dictionary. One of the major contributions involves the creation of dictionary of a fixed number of representative cell nuclei to speed up patch level classification. This optimal dictionary is used for clustering extracted cell nuclei and a fixed length histogram of counts on different types of nuclei is obtained. The proposed system has been tested with a total of 239600 TCGA patches of GBM and 206000 patches of LGG collected from GDC data portal and it showed good diagnosis performance with auto-classification accuracy of 97.2% compared to other state-of-art methods. Our results on segmentation and classification are encouraging, with better attainment with regard to precision and accuracy in contrast with previous models. The auto grading proposed system will act as a potential guide for pathologists to make more accurate decisions.

Creation of multiple NV centers by phthalocyanine ion implantation

A nitrogen vacancy (NV) center in diamond is known as a solid-state spin qubit at room temperature. NV centers coherently coupled by dipole–dipole interactions have a potential to accomplish quantum registers at room temperature. This study reports to develop a phthalocyanine ion implantation technique to fabricate multiple dipole-coupled NV centers. Photon counts and optically detected magnetic resonance spectra show that up to four NV centers were successfully created in a confocal spot. The histogram of photon counts is fitted by a Poisson distribution, and the ratio of multiple NV spots suggests the potential for a five NV centers creation.

Novel Meteor Observations Using FM Radio Broadcast Over Gadanki, India

A novel GNU radio-based software-defined radio receiver using USRP (universal serial radio peripheral) front end and YAGI antenna is developed to record meteor echoes in passive mode using FM broadcast. Pilot observations are made for four days in August-September 2020 to record FM signals at 103.2MHz, 104MHz, and 107.5 MHz with a varying sample rate of 8 KHz and 200 KHz. The in-phase and quadrature signals are processed to obtain the amplitude and phase of the received signals for identification of the meteor echoes. A new algorithm is developed to automatically detect the echoes using a normalized amplitude threshold and a minimum temporal separation between two echoes. Overdense meteor echoes with a plateau-like amplitude response are found using a ratio test and remaining echoes are rejected. Thus, the system is able to detect almost all types of meteor echoes reported earlier. Notably, signatures of fragmentation and background wind are also observed in form of different phase patterns. Histogram of the meteor counts shows a peak for shorter durations indicating larger populations of the smaller size of meteoroids. The log-log plot of the meteor duration versus count is found to exhibit a characteristic transition of the slope. An empirical estimate of the height of the echoes is obtained. The echoes show micro-meteoroid populations which may depend upon the transmitting-receiving setup and the geometry. Hence, an inexpensive, low-power, portable, passive and automated meteor detection system is demonstrated which can be highly useful for understanding the meteor phenomena and estimation of atmospheric parameters.

Reprint of “Radiation damage impact on hybrid-pixel detectors data”

This work aimed to quantify the influence of the deposited dose at the hybrid detector ASIC on the resulting image quality. Low (932±4 Gy) and high (6310±24 Gy) dose experiments were performed by irradiating a Medipix3RX single chip detector with the polychromatic beam from the Brazilian Synchrotron X-ray Imaging beamline. It was possible to evaluate subtle effects by using a noise component model based on estimating the quantum, electronic and structural noise contributions. Visible effects were quantified by analyzing the evolution of the histogram of the pixel counts at the irradiated area. The dose threshold for subtle damages was 388±3 Gy deposited in the gate oxide and shallow trench isolation oxide layers, while visible damages were observed for doses higher than 2635±15 Gy. A recovery of the damaged pixels with time was noticed and quantified, reaching the half-life time at 1.84±0.02 h after irradiation. These results encourage periodical maintenance procedures, for example through a new equalization matrix generation, which proved to be a possible tool for recovering the detector performance.

Generalized Born radii computation using linear models and neural networks.

Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of generalized Born (GB) radii, which is accomplished by algorithms based on the electrostatics of inhomogeneous dielectric media. The speed and accuracy of such computations are still an issue especially for their intensive use in classical molecular dynamics. Here, we propose an alternative approach that encodes the physics of the phenomena and the chemical structure of the molecules in model parameters which are learned from examples. GB radii have been computed using (i) a linear model and (ii) a neural network. The input is the element, the histogram of counts of neighbouring atoms, divided by atom element, within 16 Å. Linear models are ca. 8 times faster than the most widely used reference method and the accuracy is higher with correlation coefficient with the inverse of 'perfect' GB radii of 0.94 versus 0.80 of the reference method. Neural networks further improve the accuracy of the predictions with correlation coefficient with 'perfect' GB radii of 0.97 and ca. 20% smaller root mean square error. We provide a C program implementing the computation using the linear model, including the coefficients appropriate for the set of Bondi radii, as Supplementary Material. We also provide a Python implementation of the neural network model with parameter and example files in the Supplementary Material as well. Supplementary data are available at Bioinformatics online.

On time series classification with dictionary-based classifiers

A family of algorithms for time series classification (TSC) involve running a sliding window across each series, discretising the window to form a word, forming a histogram of word counts over the dictionary, then constructing a classifier on the histograms. A recent evaluation of two of this type of algorithm, Bag of Patterns (BOP) and Bag of Symbolic Fourier Approximation Symbols (BOSS) found a significant difference in accuracy between these seemingly similar algorithms. We investigate this phenomenon by deconstructing the classifiers and measuring the relative importance of the four key components between BOP and BOSS. We find that whilst ensembling is a key component for both algorithms, the effect of the other components is mixed and more complex. We conclude that BOSS represents the state of the art for dictionary-based TSC. Both BOP and BOSS can be classed as bag of words approaches. These are particularly popular in Computer Vision for tasks such as image classification. We adapt three techniques used in Computer Vision for TSC: Scale Invariant Feature Transform; Spatial Pyramids; and Histogram Intersection. We find that using Spatial Pyramids in conjunction with BOSS (SP) produces a significantly more accurate classifier. SP is significantly more accurate than standard benchmarks and the original BOSS algorithm. It is not significantly worse than the best shapelet-based or deep learning approaches, and is only outperformed by an ensemble that includes BOSS as a constituent module.

Efficiently Inferring the Demographic History of Many Populations With Allele Count Data

ABSTRACTThe sample frequency spectrum (SFS), or histogram of allele counts, is an important summary statistic in evolutionary biology, and is often used to infer the history of population size changes, migrations, and other demographic events affecting a set of populations. The expected multipopulation SFS under a given demographic model can be efficiently computed when the populations in the model are related by a tree, scaling to hundreds of populations. Admixture, back-migration, and introgression are common natural processes that violate the assumption of a tree-like population history, however, and until now the expected SFS could be computed for only a handful of populations when the demographic history is not a tree. In this article, we present a new method for efficiently computing the expected SFS and linear functionals of it, for demographies described by general directed acyclic graphs. This method can scale to more populations than previously possible for complex demographic histories including admixture. We apply our method to an 8-population SFS to estimate the timing and strength of a proposed “basal Eurasian” admixture event in human history. We implement and release our method in a new open-source software package momi2.

Radiation damage impact on hybrid-pixel detectors data

This work aimed to quantify the influence of the deposited dose at the hybrid detector ASIC on the resulting image quality. Low (932±4 Gy) and high (6310±24 Gy) dose experiments were performed by irradiating a Medipix3RX single chip detector with the polychromatic beam from the Brazilian Synchrotron X-ray Imaging beamline. It was possible to evaluate subtle effects by using a noise component model based on estimating the quantum, electronic and structural noise contributions. Visible effects were quantified by analyzing the evolution of the histogram of the pixel counts at the irradiated area. The dose threshold for subtle damages was 388±3 Gy deposited in the gate oxide and shallow trench isolation oxide layers, while visible damages were observed for doses higher than 2635±15 Gy. A recovery of the damaged pixels with time was noticed and quantified, reaching the half-life time at 1.84±0.02 h after irradiation. These results encourage periodical maintenance procedures, for example through a new equalization matrix generation, which proved to be a possible tool for recovering the detector performance.

Exploring cell tower data dumps for supervised learning-based point-of-interest prediction (industrial paper)

Exploring massive mobile data for location-based services becomes one of the key challenges in mobile data mining. In this paper, we investigate a problem of finding a correlation between the collective behavior of mobile users and the distribution of points of interest (POIs) in a city. Specifically, we use large-scale cell tower data dumps collected from cell towers and POIs extracted from a popular social network service, Weibo. Our objective is to make use of the data from these two different types of sources to build a model for predicting the POI densities of different regions in the covered area. An application domain that may benefit from our research is a business recommendation application, where a prediction result can be used as a recommendation for opening a new store/branch. The crux of our contribution is the method of representing the collective behavior of mobile users as a histogram of connection counts over a period of time in each region. This representation ultimately enables us to apply a supervised learning algorithm to our problem in order to train a POI prediction model using the POI data set as the ground truth. We studied 12 state-of-the-art classification and regression algorithms; experimental results demonstrate the feasibility and effectiveness of the proposed method.

Dark counts of superconducting nanowire single-photon detector under illumination.

An abnormal increase in the SDE was observed for superconducting nanowire single-photon detectors (SNSPDs) when the bias current (I(b)) was close to the switching current (I(sw)). By introducing the time-correlated single-photon counting technique, we investigated the temporal histogram of the detection counts of an SNSPD under illumination. The temporal information helps us to distinguish photon counts from dark counts in the time domain. In this manner, the dark count rate (DCR) under illumination and the accurate SDE can be determined. The DCR under moderate illumination may be significantly larger than the conventional DCR measured without illumination under a high I(b), which causes the abnormal increase in the SDE. The increased DCR may be explained by the suppression of I(sw) under illumination.

일반화된 허프변환의 임계값 선택을 위한 확률적 접근방식

When the Hough transform is applied to identify an instance of a given model, the output is typically a histogram of votes cast by a set of image features into a parameter space. The next step is to threshold the histogram of counts to hypothesize a given match. The question is What is a reasonable choice of the threshold? In a standard implementation of the Hough transform, the threshold is selected heuristically, e.g., some fraction of the highest cell count. Setting the threshold too low can give rise to a false alarm of a given shape(Type I error). On the other hand, setting the threshold too high can result in mis-detection of a given shape(Type II error). In this paper, we derive two conditional probability functions of cell counts in the accumulator array of the generalized Hough transform(GHough), that can be used to select a scientific threshold at the peak detection stage of the Ghough.

Prolyl‐isomerase Pin1 controls normal and cancer stem cells of the breast

Mammary epithelial stem cells are fundamental to maintain tissue integrity. Cancer stem cells (CSCs) are implicated in both treatment resistance and disease relapse, and the molecular bases of their malignant properties are still poorly understood. Here we show that both normal stem cells and CSCs of the breast are controlled by the prolyl-isomerase Pin1. Mechanistically, following interaction with Pin1, Notch1 and Notch4, key regulators of cell fate, escape from proteasomal degradation by their major ubiquitin-ligase Fbxw7α. Functionally, we show that Fbxw7α acts as an essential negative regulator of breast CSCs' expansion by restraining Notch activity, but the establishment of a Notch/Pin1 active circuitry opposes this effect, thus promoting breast CSCs self-renewal, tumor growth and metastasis in vivo. In human breast cancers, despite Fbxw7α expression, high levels of Pin1 sustain Notch signaling, which correlates with poor prognosis. Suppression of Pin1 holds promise in reverting aggressive phenotypes, through CSC exhaustion as well as recovered drug sensitivity carrying relevant implications for therapy of breast cancers.

Modelling fat mass as a function of weekly physical activity profiles measured by Actigraph accelerometers

We show results on the Avon longitudinal study of parents and children (ALSPAC) using a new approach for modelling the relationship between health outcomes and physical activity assessed by accelerometers. The key feature of the model is that it uses the histogram of physical activity counts as a predictor function, rather than scalar summary measures such as average daily moderate to vigorous physical activity (MVPA). Three models are fitted: (1a) A regression of fat mass at age 12 (N = 4164) onto the histogram of accelerometer counts at age 12; (1b) A regression of fat mass at age 14 (N = 2403) onto the histogram of accelerometer counts at age 12 and (1c) a regression of fat mass at age 14 (N = 2413) onto the accelerometer counts at age 14. All three models significantly improve on models including MVPA instead of the histogram and improve the goodness of fit of models (2a), (2b) and (2c) from R2 = 0.267, 0.248 and 0.230 to R2 = 0.292, 0.263 and 0.258 for models (1a), (1b) and (1c) respectively. The proportion of time spent in sedentary and very light activity (corresponding to slow walking and similar activities) has a positive contribution towards fat mass and time spent in moderate to vigorous activity has a negative contribution towards fat mass.

Thrombocytopenia in Iraqi Pregnant Women.

Background: Thrombocytopenia is the second most common hematological abnormality in pregnancy following anemia. However, its incidence causes and outcomes received scarce attention, particularly in developing countries like Iraq.Objective: This study was conducted to determine the incidence, causes and consequences of thrombocytopenia on both the mother and fetus.Material and Methods: Platelet counts were performed on 600 pregnant women in their third trimester and on 200 age matched non pregnant healthy women, as well as 150 of the newborns of the former groups.Results: The overall incidence of thrombocytopenia in pregnancy was 8.6%, with the gestational thrombocytopenia implicated in 76.9%,. Hypertensive disorders (pregnancy induced hypertension, preeclampsia) were the second most common causes of thrombocytopenia in the studied group. Other diagnoses such as HELLP syndrome and Disseminated Intravascular Coagulation (DIC) were infrequent. The 2.5th percentile significantly differed in pregnant women (117x109/L) than the control group (162 x109/L) with the histogram of platelets counts shifted to the left during pregnancy. Among pregnant women with mild thrombocytopenia (platelets count 100-149x109/L), 94% were gestational in origin with a decreasing proportion of the latter with increased severity of the thrombocytopenia. Except for one lady with DIC, all other pregnant ladies completed their pregnancy with no reported complications and none of the followed up neonates had thrombocytopenia.Conclusion: Similar to worldwide studies gestational thrombocytopenia is the commonest cause for thrombocytopenia in pregnancy in Iraqi ladies and for healthy pregnant women, platelets count > 117 x 109 /L late in pregnancy does not require further investigations and could be considered as a safe threshold.

Poster — Wed Eve—40: Comparison of CBCT CT Numbers to CT‐Simulator CT Numbers using Mutual Information and Joint Probability Distributions

The Varian On‐Board Imaging (OBI) system is an imaging tool used by the London Regional Cancer Program for image‐guided radiation therapy. The system consists of a kilovoltage source (120 kVp), emitting a conical X‐ray beam, and a flat panel amorphous silicon detector. Both source and detector are mounted on the linear accelerator gantry, rotating 360° about the patient creating 3‐dimensional volumetric computed tomography images. Unfortunately, cone‐beam computed tomography (CBCT) images are susceptible to many forms of artifacts resulting primarily from: increased amounts of scatter due to CBCT geometry, spectral X‐ray attenuation, and intra‐scanning organ motion. In order to assess the extent of CBCT image artifacts, we propose a new technique that compares CT Numbers in corresponding tissue regions obtained by the CBCT and Phillips Brilliance CT‐simulator systems. Our approach registers CT images from two imaging modalities using a mutual information (MI) algorithm. Once aligned, quantitative CT Number analysis is performed on the CT images using a 2‐dimensional histogram (or joint probability distribution) of pixel counts for geographically‐matched pairs of CT Numbers. Phantom studies have shown similarity between CBCT and CT‐simulation CT Numbers when the imaged medium is head‐like in diameter (20 cm) and homogeneous (Catphan 504 phantom). Greater discrepancies were observed (400 HU) when CT images of larger phantoms (30 cm diameter, Gammex RMI Tomo phantom) and increased heterogeneity were compared. Presently, quantitative CBCT CT Number inaccuracy precludes the use of CBCT images for whole body treatment planning, and adaptive dose computations.

Universal and Adapted Vocabularies for Generic Visual Categorization

Generic Visual Categorization (GVC) is the pattern classification problem which consists in assigning labels to an image based on its semantic content. This is a challenging task as one has to deal with inherent object/scene variations as well as changes in viewpoint, lighting and occlusion. Several state-of-the-art GVC systems use a vocabulary of visual terms to characterize images with a histogram of visual word counts. We propose a novel practical approach to GVC based on a universal vocabulary, which describes the content of all the considered classes of images, and class vocabularies obtained through the adaptation of the universal vocabulary using class-specific data. The main novelty is that an image is characterized by a set of histograms - one per class - where each histogram describes whether the image content is best modeled by the universal vocabulary or the corresponding class vocabulary. This framework is applied to two types of local image features: low-level descriptors such as the popular SIFT and high-level histograms of word co-occurrences in a spatial neighborhood. It is shown experimentally on two challenging datasets (an in-house database of 19 categories and the PASCAL VOC 2006 dataset) that the proposed approach exhibits state-of-the-art performance at a modest computational cost.

The Challenge of Predicting Demand for Emergency Department Services

The objective was to develop methodology for predicting demand for emergency department (ED) services by characterizing ED arrivals. One year of ED arrival data from an academic ED were merged with local climate data. ED arrival patterns were described; Poisson regression was selected to represent the count of hourly ED arrivals as a function of temporal, climatic, and patient factors. The authors evaluated the appropriateness of prediction models by whether the data met key Poisson assumptions, including variance proportional to the mean, positive skewness, and absence of autocorrelation among hours. Model accuracy was assessed by comparing predicted and observed histograms of arrival counts and by how frequently the observed hourly count fell within the 50 and 90% prediction intervals. Hourly ED arrivals were obtained for 8,760 study hours. Separate models were fit for high- versus low-acuity patients because of significant arrival pattern differences. The variance was approximately equal to the mean in the high- and low-acuity models. There was no residual autocorrelation (r = 0) present after controlling for temporal, climatic, and patient factors that influenced the arrival rate. The observed hourly count fell within the 50 and 90% prediction intervals 50 and 90% of the time, respectively. The observed histogram of arrival counts was nearly identical to the histogram predicted by a Poisson process. At this facility, demand for ED services was well approximated by a Poisson regression model. The expected arrival rate is characterized by a small number of factors and does not depend on recent numbers of arrivals.

Bayesian Analysis of Lidar Signals with Multiple Returns

Time-Correlated Single Photon Counting and Burst Illumination Laser data can be used for range profiling and target classification. In general, the problem is to analyse the response from a histogram of either photon counts or integrated intensities to assess the number, positions and amplitudes of the reflected returns from object surfaces. The goal of our work is a complete characterisation of the 3D surfaces viewed by the laser imaging system. The authors present a unified theory of pixel processing that is applicable to both approaches based on a Bayesian framework which allows for careful and thorough treatment of all types of uncertainties associated with the data. We use reversible jump Markov chain Monte Carlo (RJMCMC) techniques to evaluate the posterior distribution of the parameters and to explore spaces with different dimensionality. Further, we use a delayed rejection step to allow the generated Markov chain to mix better through the use of different proposal distributions. The approach is demonstrated on simulated and real data, showing that the return parameters can be estimated to a high degree of accuracy. We also show some practical examples from both near and far range depth imaging.

Phylogenetic conservation of chromosome numbers in Actinopterygiian fishes

The genomes of ray-finned fishes (Actinopterygii) are well known for their evolutionary dynamism as reflected by drastic alterations in DNA content often via regional and whole-genome duplications, differential patterns of gene silencing or loss, shifts in the insertion-to-deletion ratios of genomic segments, and major re-patternings of chromosomes via non-homologous recombination. In sharp contrast, chromosome numbers in somatic karyotypes have been highly conserved over vast evolutionary timescales - a histogram of available counts is strongly leptokurtic with more than 50% of surveyed species displaying either 48 or 50 chromosomes. Here we employ comparative phylogenetic analyses to examine the evolutionary history of alterations in fish chromosome numbers. The most parsimonious ancestral state for major actinopterygiian clades is 48 chromosomes. When interpreted in a phylogenetic context, chromosome numbers evidence many recent instances of polyploidization in various lineages but there is no clear indication of a singular polyploidization event that has been hypothesized to have immediately preceded the teleost radiation. After factoring out evident polyploidizations, a correlation between chromosome numbers and genome sizes across the Actinopterygii is marginally statistically significant (p = 0.012) but exceedingly weak (R (2) = 0.0096). Overall, our phylogenetic analysis indicates a mosaic evolutionary pattern in which the forces that govern labile features of fish genomes must operate largely independently of those that operate to conserve chromosome numbers.