Probability Density Maps Research Articles

LOFAR observations of gravitational wave merger events: O3 results and O4 strategy

ABSTRACT The electromagnetic counterparts to gravitational wave (GW) merger events hold immense scientific value, but are difficult to detect due to the typically large localization errors associated with GW events. The Low-Frequency Array (LOFAR) is an attractive GW follow-up instrument owing to its high sensitivity, large instantaneous field of view, and ability to automatically trigger on events to probe potential prompt emission within minutes. Here, we report on 144-MHz LOFAR radio observations of three GW merger events containing at least one neutron star that were detected during the third GW observing run. Specifically, we probe 9 and 16 per cent of the location probability density maps of S190426c and S200213t, respectively, and place limits at the location of an interesting optical transient (PS19hgw/AT2019wxt) found within the localization map of S191213g. While these GW events are not particularly significant, we use multi-epoch LOFAR data to devise a sensitive wide-field GW follow-up strategy to be used in future GW observing runs. In particular, we improve on our previously published strategy by implementing direction-dependent calibration and mosaicing, resulting in nearly an order of magnitude increase in sensitivity and more uniform coverage. We achieve a uniform 5σ sensitivity of 870 μJy beam−1 across a single instantaneous LOFAR pointing’s 21 deg2 core, and a median sensitivity of 1.1 mJy beam−1 when including the full 89 deg2 hexagonal beam pattern. We also place the deepest transient surface density limits yet on time-scales of the order of month for surveys between 60 and 340 MHz (0.017 deg−2 above 2.0 mJy beam−1 and 0.073 deg−2 above 1.5 mJy beam−1).

Poster graphic design with your Eyes: An approach to automatic textual layout design based on visual perception

The layout of graphic design can betedious, especially for non-professional users who require additional time. Therefore, this paper proposes a graphic design layout method based on visual perception. First, a graphic design dataset is trained based on a fully convolutional network, and a visual saliency prediction model is established to predict the visual saliency of the background image. Second, a probability density map of the background image is obtained using a diffusion equation. Third, based on a generation algorithm for a text anchor box, anchor boxes of different positions, sizes, and lengths are obtained. The experimental results show that, compared with previous work, the text position and size obtained by this method have higher correlations with the baseline.

GIS Data Driven Probability Map Generation for Search and Rescue Using Agents

Predicting the final resting location of a missing person is critical for search and rescue operations with limited resources. To improve the accuracy and speed of these predictions, simulated agents can be created to replicate the behavior of the missing person. In this paper, we introduce an agent-based model, to simulate various psychological profiles, that move over a physical landscape incorporating real-world data in their decision-making without relying on per-location training. The resultant probability density map of the missing person's location was the result of a combination of Monte Carlo simulations and mobility-time-based sampling. General trends in the data were comparable to historical data sets available. This work presents a flexible agent that can be employed by search and rescue that easily extends to various locations.

Data-Augmented Manifold Learning Thermography for Defect Detection and Evaluation of Polymer Composites.

Infrared thermography techniques with thermographic data analysis have been widely applied to non-destructive tests and evaluations of subsurface defects in practical composite materials. However, the performance of these methods is still restricted by limited informative images and difficulties in feature extraction caused by inhomogeneous backgrounds and noise. In this work, a novel generative manifold learning thermography (GMLT) is proposed for defect detection and the evaluation of composites. Specifically, the spectral normalized generative adversarial networks serve as an image augmentation strategy to learn the thermal image distribution, thereby generating virtual images to enrich the dataset. Subsequently, the manifold learning method is employed for the unsupervised dimensionality reduction in all images. Finally, the partial least squares regression is presented to extract the explicit mapping of manifold learning for defect visualization. Moreover, probability density maps and quantitative metrics are proposed to evaluate and explain the obtained defect detection performance. Experimental results on carbon fiber-reinforced polymers demonstrate the superiorities of GMLT, compared with other methods.

Effects on Net Irrigation Water Requirement of Joint Distribution of Precipitation and Reference Evapotranspiration

To establish the uncertain influence that the joint distribution of precipitation and reference evapotranspiration has on net irrigation water requirement, a Copula function–Monte Carlo method (CFMC) was proposed to calculate the probability of irrigation water requirement. Taking the Jingdian Irrigation District in Northwest China as an example, the distribution laws of precipitation and reference evapotranspiration were studied. Furthermore, five typical years under different crop planting structure conditions were selected, and the variation characteristics of net irrigation water requirement in each typical year under the conditions of climate uncertainty were analyzed. The results revealed the optimal distribution functions of precipitation and reference evapotranspiration to be gamma distribution and lognormal distribution. The probability density map of the joint distribution of precipitation and reference evapotranspiration has a “saddle” shape; that is, irrigation water requirement and reference evapotranspiration are usually inversely related. As the probability of the irrigation water requirement increases, the net irrigation water requirement in the irrigation area also increases. The CFMC method can determine the design value of the net irrigation water requirement under a specific probability for typical years under different crop planting structure conditions, which can provide a reference for agricultural water resource allocation in irrigation areas.

Missense Mutations Modify the Conformational Ensemble of the α-Synuclein Monomer Which Exhibits a Two-Phase Characteristic.

α-Synuclein is an intrinsically disordered protein occurring in different conformations and prone to aggregate in β-sheet structures, which are the hallmark of the Parkinson disease. Missense mutations are associated with familial forms of this neuropathy. How these single amino-acid substitutions modify the conformations of wild-type α-synuclein is unclear. Here, using coarse-grained molecular dynamics simulations, we sampled the conformational space of the wild type and mutants (A30P, A53P, and E46K) of α-synuclein monomers for an effective time scale of 29.7 ms. To characterize the structures, we developed an algorithm, CUTABI (CUrvature and Torsion based of Alpha-helix and Beta-sheet Identification), to identify residues in the α-helix and β-sheet from Cα -coordinates. CUTABI was built from the results of the analysis of 14,652 selected protein structures using the Dictionary of Secondary Structure of Proteins (DSSP) algorithm. DSSP results are reproduced with 93% of success for 10 times lower computational cost. A two-dimensional probability density map of α-synuclein as a function of the number of residues in the α-helix and β-sheet is computed for wild-type and mutated proteins from molecular dynamics trajectories. The density of conformational states reveals a two-phase characteristic with a homogeneous phase (state B, β-sheets) and a heterogeneous phase (state HB, mixture of α-helices and β-sheets). The B state represents 40% of the conformations for the wild-type, A30P, and E46K and only 25% for A53T. The density of conformational states of the B state for A53T and A30P mutants differs from the wild-type one. In addition, the mutant A53T has a larger propensity to form helices than the others. These findings indicate that the equilibrium between the different conformations of the α-synuclein monomer is modified by the missense mutations in a subtle way. The α-helix and β-sheet contents are promising order parameters for intrinsically disordered proteins, whereas other structural properties such as average gyration radius, R g , or probability distribution of R g cannot discriminate significantly the conformational ensembles of the wild type and mutants. When separated in states B and HB, the distributions of R g are more significantly different, indicating that global structural parameters alone are insufficient to characterize the conformational ensembles of the α-synuclein monomer.

Estimation of Azimuth and Elevation for Multiple Acoustic Sources Using Tetrahedral Microphone Arrays and Convolutional Neural Networks

A method for multiple acoustic source localization using a tetrahedral microphone array and a convolutional neural network (CNN) is presented. Our method presents a novel approach for the estimation of acoustic source direction of arrival (DoA), both azimuth and elevation, utilizing a non-coplanar microphone array. In our approach, we use the phase component of the short-time Fourier transform (STFT) of the microphone array’s signals as the input feature for the CNN and a DoA probability density map as the training target. Our findings imply that our method outperforms the currently available methods for multiple sound source DoA estimation in both accuracy and speed.

Mapping the architecture of single lithium ion electrode particles in 3D, using electron backscatter diffraction and machine learning segmentation

Accurately quantifying the architecture of lithium ion electrode particles in 3D is critical to understanding sub-particle lithium transport, rate limitations, and degradation mechanisms within lithium ion batteries. Most commercial positive electrode materials consist of polycrystalline particles, where intra-particle grains have a range of morphologies and orientations. Here, focused ion beam slicing in sequence with electron backscatter diffraction is used to accurately quantify intra-particle grain morphologies in 3D. The intra-particle grains are identified using convolution neural network segmentation and distinctly labeled. Efficient morphological characterization of the grain architectures is achieved. Bivariate probability density maps are developed to show correlative relationships between morphological grain descriptors. The implication of morphological features on cell performance, as well as the extension of this dataset to guide artificial generation of realistic particle architectures for 3D multi-physics models, is discussed.

PIXER: an automated particle-selection method based on segmentation using a deep neural network

BackgroundCryo-electron microscopy (cryo-EM) has become a widely used tool for determining the structures of proteins and macromolecular complexes. To acquire the input for single-particle cryo-EM reconstruction, researchers must select hundreds of thousands of particles from micrographs. As the signal-to-noise ratio (SNR) of micrographs is extremely low, the performance of automated particle-selection methods is still unable to meet research requirements. To free researchers from this laborious work and to acquire a large number of high-quality particles, we propose an automated particle-selection method (PIXER) based on the idea of segmentation using a deep neural network.ResultsFirst, to accommodate low-SNR conditions, we convert micrographs into probability density maps using a segmentation network. These probability density maps indicate the likelihood that each pixel of a micrograph is part of a particle instead of just background noise. Particles selected from density maps have a more robust signal than do those directly selected from the original noisy micrographs. Second, at present, there is no segmentation-training dataset for cryo-EM. To enable our plan, we present an automated method to generate a training dataset for segmentation using real-world data. Third, we propose a grid-based, local-maximum method to locate the particles from the probability density maps. We tested our method on simulated and real-world experimental datasets and compared PIXER with the mainstream methods RELION, DeepEM and DeepPicker to demonstrate its performance. The results indicate that, as a fully automated method, PIXER can acquire results as good as the semi-automated methods RELION and DeepEM.ConclusionTo our knowledge, our work is the first to address the particle-selection problem using the segmentation network concept. As a fully automated particle-selection method, PIXER can free researchers from laborious particle-selection work. Based on the results of experiments, PIXER can acquire accurate results under low-SNR conditions within minutes.

Application of High-speed Super-resolution SPEED Microscopy in Live Primary Cilium

The primary cilium is a microtubule-based protrusion on the surface of many eukaryotic cells and contains a unique complement of proteins that function critically in cell motility and signaling. Since cilia are incapable of synthesizing their own protein, nearly 200 unique ciliary proteins need to be trafficked between the cytosol and primary cilia. However, it is still a technical challenge to map three-dimensional (3D) locations of transport pathways for these proteins in live primary cilia due to the limitations of currently existing techniques. To conquer the challenge, recently we have developed and employed a high-speed virtual 3D super-resolution microscopy, termed single-point edge-excitation sub-diffraction (SPEED) microscopy, to determine the 3D spatial location of transport pathways for both cytosolic and membrane proteins in primary cilia of live cells. In this article, we will demonstrate the detailed setup of SPEED microscopy, the preparation of cells expressing fluorescence-protein-labeled ciliary proteins, the real-time single-molecule tracking of individual proteins in live cilium and the achievement of 3D spatial probability density maps of transport routes for ciliary proteins.

Application of High-speed Super-resolution SPEED Microscopy in Live Primary Cilium.

The primary cilium is a microtubule-based protrusion on the surface of many eukaryotic cells and contains a unique complement of proteins that function critically in cell motility and signaling. Since cilia are incapable of synthesizing their own protein, nearly 200 unique ciliary proteins need to be trafficked between the cytosol and primary cilia. However, it is still a technical challenge to map three-dimensional (3D) locations of transport pathways for these proteins in live primary cilia due to the limitations of currently existing techniques. To conquer the challenge, recently we have developed and employed a high-speed virtual 3D super-resolution microscopy, termed single-point edge-excitation sub-diffraction (SPEED) microscopy, to determine the 3D spatial location of transport pathways for both cytosolic and membrane proteins in primary cilia of live cells. In this article, we will demonstrate the detailed setup of SPEED microscopy, the preparation of cells expressing fluorescence-protein-labeled ciliary proteins, the real-time single-molecule tracking of individual proteins in live cilium and the achievement of 3D spatial probability density maps of transport routes for ciliary proteins.

Heterogeneous Spatial Distribution of Transcriptional Activity in Budding Yeast Nuclei

Recent microscopic and simulation studies have shown that the genome structure fluctuates dynamically in the nuclei of budding yeast Saccharomyces cerevisiae. This genome-wide movement should lead to the fluctuations of individual genes in their territorial regions. This raises an intriguing question of whether the resulting distribution of genes is correlated to their transcriptional activity. An effective method for examining this correlation is to analyze how the spatial distribution of genes and their transcriptional activity are modified by mutation. In this study, we analyzed the modification observed in a budding yeast mutant in which genes necessary for anchoring telomeres to the nuclear envelope, yku70 and esc1, are silenced. Taddei et al. reported that 60 genes are clearly misregulated by this mutation, with 28 and 32 genes downregulated and upregulated, respectively. We calculated the probability density maps of the misregulated genes using a model of dynamical movement of the yeast genome in both wild-type (WT) and yku70 esc1 mutant and showed that the density of downregulated genes is larger near the nucleolus, whereas the density of upregulated genes is larger at the opposite side of the nucleus. By comparing these genes with those highly (top 200 of transcriptome) and lowly (bottom 200) expressed, we showed that the simulated distribution of 28 downregulated (12 out of 32 upregulated) genes has a distinctly larger overlap with the distribution of lowly (highly) expressed genes in the mutant than in the WT. The remaining 20 upregulated genes are localized near the nuclear envelope both in the WT and in the mutant. These results showed that the transcriptional level of genes is affected by their spatial distribution, thus highlighting the importance of the structural regulation in the yeast genome.

Sensitivity of Protein Glass Transition to the Choice of Water Model.

The sensitivity of protein glass transition temperature and dynamics of Trp-cage miniprotein to the choice of water models have been investigated by performing molecular dynamics simulations at different temperatures between 130 and 300 K using mTIP3P, TIP4P, and TIP4P-Ew water models with the CHARMM22 force field. Analysis of the structure of the protein and the ordering of the hydration shell water molecules revealed correlated dynamic crossovers of the protein and its hydration water. It is demonstrated that the parametrization of different water models is robust enough to probe the onset temperatures for the two dynamic crossovers of the peptide: the first one around 150 K which is attributed to active methyl group rotations and the second one around 200-230 K specifically pertaining to glass transition of the protein. The behavior of the protein has been found to be qualitatively similar to the three water models employed, while the structural ordering and number of hydration shell waters vary significantly among the 3-site and 4-site water models. The changes in the probability density map of methyl hydrogens of the protein with temperature also show sensitivity to the choice of water model.

Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms.

Predicting ligand binding sites (LBSs) on protein structures, which are obtained either from experimental or computational methods, is a useful first step in functional annotation or structure-based drug design for the protein structures. In this work, the structure-based machine learning algorithm ISMBLab-LIG was developed to predict LBSs on protein surfaces with input attributes derived from the three-dimensional probability density maps of interacting atoms, which were reconstructed on the query protein surfaces and were relatively insensitive to local conformational variations of the tentative ligand binding sites. The prediction accuracy of the ISMBLab-LIG predictors is comparable to that of the best LBS predictors benchmarked on several well-established testing datasets. More importantly, the ISMBLab-LIG algorithm has substantial tolerance to the prediction uncertainties of computationally derived protein structure models. As such, the method is particularly useful for predicting LBSs not only on experimental protein structures without known LBS templates in the database but also on computationally predicted model protein structures with structural uncertainties in the tentative ligand binding sites.

SU‐F‐J‐113: Multi‐Atlas Based Automatic Organ Segmentation for Lung Radiotherapy Planning

Purpose:Normal organ segmentation is one time‐consuming and labor‐intensive step for lung radiotherapy treatment planning. The aim of this study is to evaluate the performance of a multi‐atlas based segmentation approach for automatic organs at risk (OAR) delineation.Methods:Fifteen Lung stereotactic body radiation therapy patients were randomly selected. Planning CT images and OAR contours of the heart ‐ HT, aorta ‐ AO, vena cava ‐ VC, pulmonary trunk ‐ PT, and esophagus – ES were exported and used as reference and atlas sets. For automatic organ delineation for a given target CT, 1) all atlas sets were deformably warped to the target CT, 2) the deformed sets were accumulated and normalized to produce organ probability density (OPD) maps, and 3) the OPD maps were converted to contours via image thresholding. Optimal threshold for each organ was empirically determined by comparing the auto‐segmented contours against their respective reference contours. The delineated results were evaluated by measuring contour similarity metrics: DICE, mean distance (MD), and true detection rate (TD), where DICE=(intersection volume/sum of two volumes) and TD = {1.0 ‐ (false positive + false negative)/2.0}. Diffeomorphic Demons algorithm was employed for CT‐CT deformable image registrations.Results:Optimal thresholds were determined to be 0.53 for HT, 0.38 for AO, 0.28 for PT, 0.43 for VC, and 0.31 for ES. The mean similarity metrics (DICE[%], MD[mm], TD[%]) were (88, 3.2, 89) for HT, (79, 3.2, 82) for AO, (75, 2.7, 77) for PT, (68, 3.4, 73) for VC, and (51,2.7, 60) for ES.Conclusion:The investigated multi‐atlas based approach produced reliable segmentations for the organs with large and relatively clear boundaries (HT and AO). However, the detection of small and narrow organs with diffused boundaries (ES) were challenging. Sophisticated atlas selection and multi‐atlas fusion algorithms may further improve the quality of segmentations.

Optical flow profiling method for visualization and evaluation of flow disturbances in agricultural pneumatic conveyance systems

Pneumatic conveying is widely used for transporting granular materials and agricultural products. Traditional flow visualization methods are used extensively in experimental fluid dynamics but have not been commonly used with agricultural products as the flow seeding particles. A flow visualization method was developed to aid in understanding physical design changes made to agricultural pneumatic conveying systems. This optical flow profiling method is demonstrated by providing qualitative flow images and quantitative values to describe the behaviour of the particle flow, both upstream and downstream of a 25mm spherical obstruction. The sphere was attached to the bottom of an acrylic conveying line that was conveying wheat particles [equivalent diameter: 3.66mm] at an air speed of 20m/s and a mass flow rate of approximately 5kg/min. Probability density maps of particle occurrence were developed to describe the chance of a wheat particle being present in a particular location of the conveying line. The data contained in these maps were used to determine the centroid of the distribution and to plot the change in the cross-sections over the test area of the pneumatic conveying system.

Investigation of electron localization in harmonic emission from asymmetric molecular ion**Project supported by the National Natural Science Foundation of China (Grant No. 11404204), the Key Project of Chinese Ministry of Education (Grant No. 211025), the Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20111404120004), the Natural Science Foundation for Young Scientists of Shanxi Province, China

We theoretically investigate the electron localization around two nuclei in harmonic emission from asymmetric molecular ion. The results show that the ionization process of electron localized around one nucleus competes with its transfer process to the other nucleus. By increasing the initial vibrational level, more electrons localized around the nucleus D+ tend to transfer to the nucleus He2+ so that the ionizations of electrons localized around the nucleus He2+ increase. In this case, the difference in harmonic efficiency between HeH2+ and HeD2+ decreases while the difference in harmonic spectral structure increases. The evident minimum can be observed in the harmonic spectrum of HeH2+ compared with that in the spectral structure of HeD2+, which is due to the strong interference of multiple recombination channels originating from two nuclei. Time-dependent nuclear probability density, electron-nuclear probability density, double-well model, and time-frequency maps are presented to explain the underlying mechanisms.

Mercury's magnetopause and bow shock from MESSENGER Magnetometer observations

AbstractWe have established the average shape and location of Mercury's magnetopause and bow shock from orbital observations by the MESSENGER Magnetometer. We fit empirical models to midpoints of boundary crossings and probability density maps of the magnetopause and bow shock positions. The magnetopause was fit by a surface for which the position R from the planetary dipole varies as [1 + cos(θ)]−α, where θ is the angle between R and the dipole‐Sun line, the subsolar standoff distance Rss is 1.45 RM (where RM is Mercury's radius), and the flaring parameter α = 0.5. The average magnetopause shape and location were determined under a mean solar wind ram pressure PRam of 14.3 nPa. The best fit bow shock shape established under an average Alfvén Mach number (MA) of 6.6 is described by a hyperboloid having Rss = 1.96 RM and an eccentricity of 1.02. These boundaries move as PRam and MA vary, but their shapes remain unchanged. The magnetopause Rss varies from 1.55 to 1.35 RM for PRam in the range of 8.8–21.6 nPa. The bow shock Rss varies from 2.29 to 1.89 RM for MA in the range of 4.12–11.8. The boundaries are well approximated by figures of revolution. Additional quantifiable effects of the interplanetary magnetic field are masked by the large dynamic variability of these boundaries. The magnetotail surface is nearly cylindrical, with a radius of ~2.7 RM at a distance of 3 RM downstream of Mercury. By comparison, Earth's magnetotail flaring continues until a downstream distance of ~10 Rss.

A Stochastic Forecast of California Earthquakes Based on Fault Slip and Smoothed Seismicity

We present a testable earthquake source model for intermedi- ate- to long-term forecasts. The model is based on fundamental observations: the frequency-magnitude distribution, slip rates on major faults, long-term strain rates, and source parameter values of instrumentally recorded and historic earthquakes. The basic building blocks of the model are two pairs of probability density maps. The first pair consists of smoothed seismicity and weighted focal mechanisms based on observed earthquakes. The second pair corresponds to mapped faults and their slip rates and consists of smoothed moment-rate and weighted focal mechanisms based on fault geometry. We construct from the model a stochastic event set, that is to say, a large set of simulated earthquakes that are relevant for seismic hazard calculations and earthquake forecast development. Their complete descriptions are determined in the following order: magnitude, epicenter, moment tensor, length, displacement, and down-dip width. Our approach assures by construction that the simulated magnitudes are consistent with the observed frequency-magnitude distribution. We employ a magnitude-dependent weighting procedure that tends to place the largest simulated earthquakes near major faults with consistent focal mechanisms. Nevertheless, our model allows for surprises such as large off-fault earthquakes, events that comply with the observation that several recent destructive earthquakes occurred on previously unknown fault structures. We apply our model to California to illustrate its features.

Ophthalmological disorders in rural areas of Crete: a geospatial analysis

Estimation of the prevalence of vision impairment in a population can be performed using epidemiological research. The purpose of this study was to measure, using spatial statistics, the prevalence of ophthalmologic disease identified at visits of the Mobile Ophthalmologic Unit (MOU) of the Vardinogiannion Eye Institute of Crete (VEIC) to villages in Crete. The study also aimed to estimate increased 'risk' of ophthalmological disease according to demographic and location factors and, thus, use the present findings as a basis for planning future services. Estimation of risks for cataract, glaucoma, and refractive errors were assessed by multiple logistic regression models in order to measure the effect of demographic (age, sex) and location (province, distance from nearest ophthalmologist) parameters. Spatial analysis was applied in order to produce a density and probability density map of ophthalmologic disorders using kriging interpolation methods. Newly diagnosed cataracts and refractive errors were found more frequently in locations greater than 70 km from the nearest ophthalmologist (respectively, OR = 6.0 [95% CI = 1.637-9.482]; OR = 27.4 [20.038-39.028] <em>p</em>-value = 0.004). Those aged >60 years had higher risk for all eye abnormalities: cataracts (OR = 0.7; 95% CI = 0.238-0.938), glaucoma (OR = 1.6; 95% CI = 1.227-2.037), and refractive errors (OR = 0.5; 95% CI = 0.183-0.829). The present study supports the use of local policies and preventive measures in rural areas of Crete in order to improve rural health standards. Some insights concerning the effectiveness of future visits of MOUs are provided, guided by spatial analysis.