Spatial Probability Distribution Research Articles

Mapping distribution of brain metastases: does the primary tumor matter?

PurposePrior reports on the location and sizes of brain metastases almost entirely focus on patients with primary breast and pulmonary cancer. This is the first study comparing multiple other types of cancer that metastasize to the brain.MethodsThis monocentric retrospective study includes 369 untreated patients with 3313 intraaxial brain metastases. Following semi-manual segmentation of metastases on post-contrast T1WI, cumulative spatial probability distribution maps of brain metastases were created for the whole group and for all primary tumors. Furthermore, mixed effects logistic regression model analysis was performed to determine if the primary tumor, patient age, and patient sex influence lesion location.ResultsThe cerebellum as location of brain metastases was proportionally overrepresented. Breast and pulmonary cancer caused higher number of brain metastases to what would normally be expected. Multivariate analyses revealed a significant accumulation of brain metastases from skin cancer in a frontal and from breast and gastrointestinal cancer in a cerebellar location.ConclusionDistribution of brain metastases is very heterogeneous for the distinct primaries, possibly reflecting the diversity of mechanisms involved in brain metastases formation. In daily clinical practice distribution patters may be beneficial to predict the primary cancer site, if unknown.

Predictive modelling of mesophotic habitats in the north‐western Gulf of Mexico

Abstract Effective management of marine resources requires an understanding of the spatial distribution of biologically important communities. The north‐western Gulf of Mexico contains diverse marine ecosystems at a large range of depths and geographic settings. To better understand the distribution of these marine habitats across large geographic areas under consideration for marine sanctuary status, presence‐only predictive modelling was used. Results confirmed that local geographic characteristics can accurately predict the probability of occurrence for marine habitat types, and include a novel technique for assigning a single, most likely habitat in areas where multiple habitats are predicted. The highest resolution bathymetric data (10 m) available for the region was used to develop raster layers that represent characteristics that have been shown to influence species occurrence in other settings. A georeferenced historical photo record collected via remotely operated vehicle was classified according to six commonly found mesophotic habitats across the 18 reefs and banks under consideration for Flower Garden Banks National Marine Sanctuary boundary expansion. Using maximum entropy modelling, the influence of local geographic characteristics on the presence of these habitats was measured and a spatial probability distribution was developed for each habitat type across the study area.

Wildfire ignition probability in Belgium

Abstract. In recent decades, large wildfires have inflicted considerable damage on valuable Natura 2000 regions in Belgium. Despite these events and the general perception that global change will exacerbate wildfire prevalence, this has not been studied yet in the Belgian context. Therefore, the national government initiated the national action plan on wildfires in order to evaluate the wildfire risk, on the one hand, and the materials, procedures, and training of fire services, on the other hand. This study focuses on the spatial distribution of the ignition probability, a component of the wildfire risk framework. In a first stage, we compile a historical wildfire database using (i) newspaper articles between 1994 and 2016 and (ii) a list of wildfire interventions between 2010 and 2013, provided by the government. In a second stage, we use a straightforward method relying on Bayes' rule and a limited number of covariates to calculate the ignition probability. It appears that most wildfire-prone areas in Belgium are located in heathland where military exercises are held. The provinces that have the largest relative areas with a high or very high wildfire risk are Limburg and Antwerp. Our study also revealed that most wildfire ignitions in Belgium are caused by humans (both arson and negligence) and that natural causes such as lightning are rather scarce. Wildfire prevention can be improved by (i) excluding military activity in fire-prone areas during the fire season, (ii) improving collaboration with foreign emergency services, (iii) concentrating the dedicated resources in the areas that display the highest ignition probabilities, (iv) improving fire detection methods, and (v) raising more awareness among the public.

Dispersion and ground deposition of radioactive material according to airflow patterns for enhancing the preparedness to N/R emergencies

The intent of minimizing the impact of the large amount of radioactive material potentially released into the atmosphere in a nuclear event implies preparedness activities. In the early phase and in absence of field observations, countermeasures would largely rely on a previous characterization of the transport and dispersion of radioactive particles and the potential levels of radioactive contamination. This study presents a methodology to estimate the atmospheric transport, dispersion and ground deposition patterns of radioactive particles. The methodology starts identifying the main airflow directions by means of the air mass trajectories calculated by the HYSPLIT model, and, secondly, the dispersion and the ground deposition characteristics associated with each airflow pattern by running the RIMPUFF atmospheric dispersion model. From the basis of these results, different products can be obtained, such as the most probable transport direction, spatial probability distribution of deposition and the geographical probability distribution of deposition above certain predefined threshold. The method is trained on the HYSPLIT trajectories and RIMPUFF simulations during five consecutive years (2012–2016) at the Almaraz Nuclear Power Plant, in Spain. 3644 forward air mass trajectories were calculated (at 00 and 12 UTC, and with duration of 36 h). Eight airflow patterns were identified, and within each pattern, the persistent days, i.e. those days in which trajectories at 00 and 12 UTC grouped into the same airflow pattern, were extracted to simulate the atmospheric dispersion and ground deposition following a hypothetical ISLOCA accident sequence of 35 h. In total, 833 simulations were carried out, in which ground contamination was estimated at cell level on a non-homogeneous geographical grid spacing up to 800 km from Almaraz. The corresponding outcomes show a large variability in the area covered and in deposition values between airflow patterns, which provide comprehensive and oriented information and resources to decision makers to emergency management.

Spatial Correlation between Land Surface Properties and Cloud Characteristics in Indonesia

The change of surface vegetation also links to the evapotranspiration pattern so that the moisture flux might be affected by the atmospheric stratiform or convective clouds, meant to be important in balancing hydrological cycle and the more analysing is necessary to explain this phenomenon. The aim of this paper to analyze the complex phenomenon that link in spatial correlation of cloud response towards land surface change that ensued from cloud microphysical components. Fourteen years from 2003 to 2016 over Indonesia was applied that issued by Moderate-Resolution Imaging Spectroradiometer (MODIS) level-3 (L3) provides both cloud and land surface products. Cloud microphysical features consist of cloud fraction, cloud top pressure, cloud optical thickness, and cloud effective radius, whereas Normalized Difference Vegetation Index (NDVI) was applied to identify the land surface change. The distribution of spatial correlation and probability distribution function are used as the method to determine each cloud microphysical components response to land surface change. Concerning annual result, desirable connections among correlation between NDVI and cloud parameters is rather widely. Probabilistic approach from statistical analysis in the wet season forms palpably pattern (parabolic pattern) rather than a dry season pattern. Correlation values based on spatial analysis between NDVI anomalies and cloud parameter anomalies have a range of values around -0.8 to 0.8. Throughout Indonesia, every correlation between NDVI anomalies and cloud parameter anomalies has a negative correlation. Sumatra, Kalimantan and Papua have a major role to inject negative correlations. This causes this area to be covered with oil palm plantations.

Context-Aware Decision Support for Anti-Submarine Warfare Mission Planning Within a Dynamic Environment

Anti-submarine warfare (ASW) missions are the linchpin of maritime operations involving effective allocation and path planning of scarce assets to search for, detect, classify, track, and prosecute hostile submarines within a dynamic and uncertain mission environment. Motivated by the need to assist ASW commanders to make better decisions within an evolving mission context, we investigate a moving target search problem with multiple searchers and develop a context-driven decision support tool for the ASW mission planning problem. Given the spatial probability distribution of a target submarine, sensor detection probability surfaces from meteorological and oceanographic products, and the risk to the fleet as a function of distance of the target from the fleet, we model and formulate the ASW asset allocation and search path planning problem using a hidden Markov modeling framework. We propose a two phase approach to solve this NP-hard problem. In phase I, we partition the geographic area, satisfying contiguity constraints, into search regions using an evolutionary algorithm (EA) coupled with a Voronoi tessellation approach, and allocate the assets to partitioned search areas using the auction algorithm. In phase II, we construct a dynamic search plan for each asset over the search interval using EA. We evaluate our approach via a hypothetical ASW scenario to monitor an enemy submarine in a geographic region via multiple assets. We compare our results to various search path planning strategies that, using the context-driven decision support tool developed here, revise the search regions at periodic intervals given a fixed total search time.

Application of Fractal and Gray-Level Co-Occurrence Matrix Indices to Assess the Forest Dynamics in the Curvature Carpathians—Romania

The mountain ecosystems face significant damage from deforestation and environmental forest changes. We investigated the evolution of tree types of cover areas, deforested areas and total deforested areas from Curvature Carpathians using Gray-Level Co-occurrence Matrix and fractal analysis. The forest dynamics mapping was one of the main objectives of this study and it was carried out using multiple fractal and GLCM indices. We approached the analysis of satellite forest images by calculation of four fractal indices such as Pyramid dimension, Cube Counting Dimension, Fractal Fragmentation-Compaction Index and Tug-of-War lacunarity. We also calculated fractal dimension because it is an index of complexity comparing how the detail in a pattern changes with the scale at which it is measured. Fractal dimension is useful for estimation of irregularity or roughness of fractal and natural objects that do not conform to Euclidian geometry. While the fractal dimension quantifies how much space is occupied, the Tug-of-War lacunarity complements fractal dimension with its ability to quantify how space is occupied. Analysis was further supplemented by the Gray-Level Co-occurrence Matrix analysis because it quantifies spatial probability distributions of gray level values between pixel pairs within an image. The calculated Gray-Level Co-occurrence Matrix features included Angular Second Moment, Contrast, Correlation, Inverse Difference Moment and Entropy. Such comprehensive analysis has the advantage of combining fractal analysis that extracts quantitative information about the morphological complexity of the image with the spatial distribution of the gray pixel intensities as calculated by the co-occurrence features provided by Gray-Level Co-occurrence Matrix. Evolution of deforested areas, expansion of agricultural land and the increased demand for quality timber have affected the forests ecosystems and, the regional sustainable development of local communities.

Real-time use of artificial intelligence in the evaluation of cancer in Barrett’s oesophagus

Based on previous work by our group with manual annotation of visible Barrett oesophagus (BE) cancer images, a real-time deep learning artificial intelligence (AI) system was developed. While an expert...

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Landslides are one type of serious geological hazard which cause immense losses of local life and property. Landslide susceptibility prediction (LSP) can be used to determine the spatial probability of landslide occurrence in a certain area. It is important to implement LSP for landslide hazard prevention and reduction. This study developed a particle-swarm-optimized multilayer perceptron (PSO-MLP) model for LSP implementation to overcome the drawbacks of the conventional gradient descent algorithm and to determine the optimal structural parameters of MLP. Shicheng County in Jiangxi Province of China was used as the study area. In total, 369 landslides, randomly selected non-landslides, and 14 landslide-related predisposing factors were used to train and test the present PSO-MLP model and three other comparative models (an MLP-only model with the gradient descent algorithm, a back-propagation neural network (BPNN), and an information value (IV) model). The results showed that the PSO-MLP model had the most accurate prediction performance (area under the receiver operating characteristic curve (AUC) of 0.822 and frequency ratio (FR) accuracy of 0.856) compared with the MLP-only (0.791 and 0.829), BPNN (0.800 and 0.840), and IV (0.788 and 0.824) models. It can be concluded that the proposed PSO-MLP model addresses the drawbacks of the MLP-only model well and performs better than conventional artificial neural networks (ANNs) and statistical models. The spatial probability distribution law of landslide occurrence in Shicheng County was well revealed by the landslide susceptibility map produced using the PSO-MLP model. Furthermore, the present PSO-MLP model may have higher prediction and classification performances in some other fields compared with conventional ANNs and statistical models.

A seismic facies classification method based on the convolutional neural network and the probabilistic framework for seismic attributes and spatial classification

In the early stage of oil and gas exploration, due to the lack of available drilling data, the automatic seismic facies classification technology mainly relies on the unsupervised clustering method combined with the seismic multiattribute. However, the clustering results are unstable and have no clear geologic significance. The supervised classification method based on manual interpretation can provide corresponding geologic significance, but there are still some problems such as the discrete classification results and low accuracy. To solve these problems, inspired by hyperspectral and spatial probability distribution technology, we have developed a classification framework called the probabilistic framework for seismic attributes and spatial classification (PFSSC). It can improve the continuity of the classification results by combining the classification probability and the spatial partial probability of the classifier output. In addition, the convolutional neural network (CNN) is a typical classification algorithm in deep learning. By convolution and pooling, we could use it to extract features of complex nonlinear objects for classification. Taking advantage of the combination of PFSSC and CNN, we could effectively solve the existing problems mentioned above in seismic facies classification. It is worth mentioning that we select seismic the multiattribute by maximal information coefficient (MIC) before the seismic facies classification. Finally, using the CNN-PFSSC and MIC methods, we can obtain high accuracy in the test set, reasonable continuity within the same seismic facies, clear boundaries between different seismic facies, and seismic facies classification results consistent with sedimentological laws.

Spatial analysis of occurrence probability of dusty days in west and southwest of Iran

In Iran, one of the environmental issues is dust storms, especially in the west and the southwest. These storms are important factors in soil erosion, economic damage to industry, agriculture, transportation sectors, and human life. Therefore, the recognition of the frequency, occurrence probability, and the return period of these storms can be instrumental in reducing damages. The purpose of this study was the spatial analysis of the occurrence probability of dusty days in the west and southwest of Iran in April, May, June, and July, using the Markov chain. The daily dust data was used in 14 synoptic stations during 24 years (1990–2013). In this study, to detect dust storms, a horizontal visibility factor of ≤ 1000 m was used for all meteorological codes. At first, the days were divided into two groups of normal days and dusty days and then the frequency of matrices, probability of transmission, and the stable matrix were calculated. Finally, the spatial distribution of the occurrence probability and the return period of the dust within 2 to 5 days were depicted. The results showed that the average occurrence probability of 2-day dusts was 15% in April and May, 22% in June, and 24% in July. Also, the occurrence probability of 3-day dusts decreased to 4%, 8%, and 9%, respectively. The return period of 1-day dust in all stations of the area and all months was 1.25 day on average; however, due to the increase in the duration of the dust period, its return period increased exponentially. Spatial distribution of stable matrix also revealed that the occurrence probability of dust in the western and southeastern parts of the studied area was more than those of the others.

3D Tracking-Free Approach for Obtaining 3D Super-Resolution Information in Rotationally Symmetric Biostructures.

Currently, it is highly desirable but still challenging to obtain high-resolution (<50 nm) three-dimensional (3D) super-resolution information on structures in fixed specimens as well as for dynamic processes in live cells. Here we introduce a simple approach, without using 3D super-resolution microscopy or real-time 3D particle tracking, to estimate 3D sub-diffraction-limited structural or dynamic information in rotationally symmetric biostructures. This is a postlocalization analysis that transforms 2D super-resolution images or 2D single-molecule localization distributions into their corresponding 3D spatial probability distributions on the basis of prior known structural knowledge. This analysis is ideal in cases where the ultrastructure of a cellular structure is known but the substructural localization of a particular (usually mobile) protein is not. The method has been successfully applied to achieve 3D structural and functional sub-diffraction-limited information for 25-300 nm subcellular organelles that meet the rotational symmetry requirement, such as nuclear pore complex, primary cilium, and microtubule. In this Article, we will provide comprehensive analyses of this method by using experimental data and computational simulations. Finally, open source code of the 2D to 3D transformation algorithm (MATLAB) and simulations (Python) have also been developed.

Analysis of temporal and spatial distributions between earthquakes in the region of California through Non-Extensive Statistical Mechanics and its limits of validity

In this work we study the influence of considering partial sets of earthquakes data has on the temporal and spatial probability distributions of earthquakes, using data from the California region between 2003 and 2016, with different thresholds for magnitude and depth of hypocenters. For this we have considered sequences of earthquakes where we have “jump”, or pulled, a given fixed number of actually sequential earthquakes. Through the concepts of Non-Extensive Statistical Mechanics for the time interval between earthquakes, we have found that the increase of the jump between earthquakes forming the sequence, affects the non-extensive characteristic of the system in the temporal probability distribution, denoted by a change in the value of the entropic index q, for relatively small jump sizes. However, for the distance between earthquakes, we observe that the increase of jumps lets untouched the non-extensive characteristic of the system, keeping the entropic index values q approximately constant. This analysis allows not just to show the robustness of the Non-Extensive Statistical Mechanics treatments for the study of earthquakes, but also their limits of applicability with respect to jumps or data loss. At the same time, it is a very useful test for memory effects and long-range interactions.

Time domain study on the tunneling dynamics in photoionization microscopy

The time-dependent wave-packet theory is used to study the tunneling dynamics during photoionization microscopy in a time domain. The results show both the ionized electron current in the time domain and the radial distribution in the spatial domain originate from two contributions: the quantum tunneling ionization of quasi-bound states and the classical above-barrier ionization of continuous states. The two types of ionization exhibit different temporal characters, resulting in a spatial probability distribution which depends on the detection time. For atoms in parallel electric and magnetic fields, the interference narrowing effect is confirmed from two aspects: the linewidth evolution and the electron current distribution. Tunneling ionization reaches its maximum at the point of levels anti-crossing. This makes it possible to observe node structures in the photoionization microscopy of an atom or molecule with strong mixing states.

Effects of changing development patterns and ignition locations within Central Texas.

Researchers have a limited understanding of the interactions between development patterns and Ignition Probability. This study explores the variation in Ignition Probability as a result of differing development patterns. Based on LANDFIRE datasets, development changes were mapped for two sets of years (2001 and 2012) and the relationship between development and Ignition Probability was assessed. The study area covered the two adjacent counties, Bastrop and Travis located in Texas, USA. These two counties have a high potential for wildfire, and due to expanding development have high vulnerability. Expanding lateral development was organized into one of five categories: infill, radial, isolated, clustered, and linear. The Maximum Entropy algorithm predicted the spatial distribution of ignition probabilities based on several physical and land use characteristics coupled with historic ignition locations. Variation in Ignition Probability was assessed for each category of development using two-way ANOVA’s and post hoc analysis. Ignition Probability maps indicated a fair sensitivity (Area Under the Curve: 0.77–0.78), suggesting that the spatial configuration of development patterns influenced wildfire ignition. Analyses found that isolated, clustered, and linear outlying development patterns had higher Ignition Probability than infill and radial development, and that fire probabilities fell along a development gradient. This trend between the development gradient and ignition probabilities should be used to direct land use planning to reduce fire risk.

3D Microscopy-Independent Approach for Obtaining 3D Super-Resolution Information in Rotationally Symmetric Bio-Structures

Currently, it is highly desirable but still challenging to obtain high-resolution (<50 nm) three-dimensional (3D) super-resolution information of structures in fixed specimens as well as for dynamic processes in live cells. Here we introduce a simple approach, without using 3D super-resolution microscopy or real-time 3D particle tracking, to estimate 3D sub-diffraction-limited structural or dynamic information in rotationally symmetric bio-structures. This is a post-localization analysis that transforms 2D super-resolution images or 2D single-molecule localization distributions into their corresponding 3D spatial probability distributions based on prior known structural knowledge. This analysis is ideal in cases where the ultrastructure of a cellular structure is known but the sub-structural localization of a particular (usually mobile) protein is not. The method has been successfully applied to achieve 3D structural and functional sub-diffraction-limited information for 25-300 nm subcellular organelles that meet the rotational symmetry requirement, such as nuclear pore complex, primary cilium and microtubule. Herein, we will provide comprehensive analyses of this method by using experimental data and computational simulations.

A hardware-accelerated particle filter for the geolocation of demersal fishes

Geolocation is increasingly employed to reconstruct the movements of demersal fishes using data retrieved from electronic archival tags. However, geolocation methods commonly suffer from limitations such as low horizontal resolution of locations, flawed land boundary treatment, and extensive computation time. We addressed these issues using a state-space approach based on the particle filter (PF), and developed a geolocation package with graphics processing unit (GPU) acceleration. Our method focused on application to demersal fish and utilizes comparison of the tag-recorded depth and temperature to the same variables from an unstructured grid regional oceanographic model. A rigorous boundary treatment scheme was implemented to handle regions with complex coastline geometry. Validation exercises using stationary mooring tags and double-electronic-tagged (archival and acoustic tags) Atlantic cod in the Gulf of Maine resulted in <10 km median errors of the estimated tracks. Sensitivity analyses suggest that using 200,000 particles was adequate to stabilize the location track estimation. Acceleration of the particle filter using GPUs resulted in faster processing than the single threaded CPU (central processing unit) implementation, enabling rapid geolocations using consumer grade computer hardware. The geolocation output of each tagged fish includes the most probable track and the associated spatial probability distribution. The resulting PF geolocation package enables high resolution and accelerated geolocation analyses to be performed on affordable consumer-grade computer hardware, resolving the time intensiveness problem of the PF that may have prevented its adoptions in marine animal geolocation. Expanded application of geolocation will yield more reliable migration information to support management. Geolocation results from archival tagging will contribute to our understanding of the spatial ecology of marine species.

Multivariate Bioclimatic Indices Modelling: A Coregionalised Approach

A methodological approach for modelling the spatial multivariate distribution of multiple bioclimatic indices is presented. The value of the indices is modelled by means of a Bayesian conditional coregionalised linear model. Elicitation of prior distributions and approximation of posterior distributions of the parameters in the proposed model are also discussed. A posterior predictive distribution and a spatial bioclimatic probability distribution for each bioclimatic index are obtained. This allows researchers to obtain the probability of each location belonging to different bioclimates. The presented methodology is applied in a practical setting showing that the spatial bioclimatic probability distributions are more realistic than the ones obtained in the univariate setting, while providing an interesting tool in the context of climate change.

Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables

As most of the forest fires in South Korea are related to human activity, socio-economic factors are critical in estimating their probability. To estimate and analyze how human activity is influencing forest fire probability, this study considered not only environmental factors such as precipitation, elevation, topographic wetness index, and forest type, but also socio-economic factors such as population density and distance from urban area. The machine learning Maximum Entropy (Maxent) and Random Forest models were used to predict and analyze the spatial distribution of forest fire probability in South Korea. The model performance was evaluated using the receiver operating characteristic (ROC) curve method, and models’ outputs were compared based on the area under the ROC curve (AUC). In addition, a multi-temporal analysis was conducted to determine the relationships between forest fire probability and socio-economic or environmental changes from the 1980s to the 2000s. The analysis revealed that the spatial distribution was concentrated in or around cities, and the probability had a strong correlation with variables related to human activity and accessibility over the decades. The AUC values for validation were higher in the Random Forest result compared to the Maxent result throughout the decades. Our findings can be useful for developing preventive measures for forest fire risk reduction considering socio-economic development and environmental conditions.

Probabilistic modelling of wildfire occurrence based on logistic regression, Niassa Reserve, Mozambique

Fires are one of the main factors for disturbances in Niassa Reserve-Mozambique, with economic and environmental impacts. There are cyclical records of fire occurrences across the reserve. However, studies on the main causative factors and identification of more susceptible locations are very limited. In this perspective, this study had as objectives: (1) determine the main significant factors for wildfire occurrences; (2) Map the probability of wildfire occurrences, using logistic regression. Independent variables included vegetation index (NDVI), climatic, topographic and socioeconomic data. The analysis period was from 2001 to 2015 and comprised the months with more occurrences of wildfire (May to December). According to the results, the main factors that determine the occurrence of fires were: NDVI, temperature, elevation, followed by precipitation, slope, relative humidity and human settlements. The spatial distribution of probability of fire occurrence reveals that zones with high and very high risk are located at the west and central west zones (areas with higher accumulation of dry biomass); medium risk zones are located in the centre of the reserve, while in central east and east zones the probability of fire occurrence is low and very low risk. Results showed that the expectation of wildfire ignition using logistic regression presented good precision (area under the curve 74%).