Age-Specific Adult Rat Brain MRI Templates and Tissue Probability Maps

Age-specific resources in human MRI mitigate processing biases that arise from structural changes across the lifespan. There are fewer age-specific resources for preclinical imaging, and they only represent developmental periods rather than adulthood. Since rats recapitulate many facets of human aging, it was hypothesized that brain volume and each tissue's relative contribution to total brain volume would change with age in the adult rat. Data from a longitudinal study of rats at 3, 5, 11, and 17 months old were used to test this hypothesis. Tissue volume was estimated from high resolution structural images using a priori information from tissue probability maps. However, existing tissue probability maps generated inaccurate gray matter probabilities in subcortical structures, particularly the thalamus. To address this issue, gray matter, white matter, and CSF tissue probability maps were generated by combining anatomical and signal intensity information. The effects of age on volumetric estimations were then assessed with mixed-effects models. Results showed that herein estimation of gray matter volumes better matched histological evidence, as compared to existing resources. All tissue volumes increased with age, and the tissue proportions relative to total brain volume varied across adulthood. Consequently, a set of rat brain templates and tissue probability maps from across the adult lifespan is released to expand the preclinical MRI community's fundamental resources.

GANSim-3D for conditional geomodelling: theory and field application

Geomodelling of subsurface reservoirs is important for water resources, hydrocarbon exploitation, and Carbon Capture and Storage (CCS). Traditional geostatistics-based approaches cannot abstract complex geological patterns and are thus not able to simulate very realistic earth models. We present a Generative Adversarial Networks (GANs)-based 3D reservoir simulation framework, GANSim-3D, which can capture geological patterns and relationships between various conditioning data and earth models and is thus able to directly simulate multiple 3D realistic and conditional earth models of arbitrary sizes from given conditioning data. In GANSim-3D, the generator, designed to only include 3D convolutional layers, takes various 3D conditioning data and 3D random latent cubes (composed of random numbers) as inputs and produces a 3D earth model. Two types of losses, the original GANs loss and condition-based loss, are designed to train the generator progressively from shallow to deep layers to learn the geological patterns and relationships from coarse to fine resolutions. Conditioning data can include 3D sparse well facies data, 3D low-resolution probability maps, and global features like facies proportion, channel width, etc. Once trained on a training dataset where each training sample is a 3D cube of a small fixed size, the generator can be used for geomodelling of 3D reservoirs of large arbitrary sizes by directly extending the sizes of all inputs and the output of the generator proportionally. To illustrate how GANSim-3D is used for field geomodelling and also to verify GANSim-3D, a field karst cave reservoir in Tahe area of China is used as an example. The 3D well facies data and 3D probability map of caves obtained from geophysical interpretation are used as conditioning data. First, we create a training dataset consisting of facies models of 64×64×64 cells with a process-mimicking simulation method to integrate field geological patterns. The training well facies data and the training probability map data are produced from the training facies models. Then, the 3D generator is successfully trained and evaluated in two synthetic cases with various metrics. Next, we apply the pretrained generator for conditional geomodelling of two field cave reservoirs of Tahe area. The first reservoir is 800m×800m×64m and is divided into 64×64×64 cells, while the second is 4200m×3200m×96m and is divided into 336×256×96 cells. We fix the input well facies data and cave probability maps and randomly change the input latent cubes to allow the generator to produce multiple diverse cave reservoir realizations, which prove to be consistent with the geological patterns of real Tahe cave reservoir as well as the input conditioning data. The noise in the input probability map is suppressed by the generator. Once trained, the geomodelling process is quite fast: each realization with 336×256×96 cells takes 0.988 seconds using 1 GPU (V100). This study shows that GANSim-3D is robust for fast 3D conditional geomodelling of field reservoirs of arbitrary sizes.

Spatial assessment of probable recharge areas – Investigating the hydrogeological controls of an active deep-seated gravitational slope deformation

Continuous and slow-moving deep-seated landslides entail challenges for the effective planning of mitigation strategies aiming at the reduction of landslide movements. Given that the activity of most of these landslides is governed by pore pressure variations within the shear zone, profound knowledge about their hydrogeological control is required. In this context, the present study presents a new approach for the spatial assessment of probable recharge areas to better understand a slope's hydrogeological system. The highly automated geo-statistical approach allows deriving recharge probability maps of groundwater based on stable isotope monitoring and a digital elevation model (DEM). By monitoring stable isotopes in both, groundwater and precipitation, mean elevations of recharge areas can be determined and further constrained in space with the help of the DEM. The approach was applied to the Vögelsberg landslide, an active slab of a deep-seated gravitational slope deformation (DSGSD) in the Watten valley (Tyrol, Austria). Resulting recharge probability maps indicate that shallow groundwater emerging at springs on the landslide recharges between 1100–1500 m a.s.l.. In contrast, groundwater encountered in wells in up to 49 m below the landslide’s surface indicates a mean recharge elevation of up to 2200 m a.s.l. matching the highest parts of the catchment. Further inferred proxies, including flow path length, estimated recharge area sizes, and mean transit times of groundwater validated against field measurements of electrical conductivity, water temperature, and discharge resulted in a profound understanding of the hydrogeological driver of the landslide. It is shown that the new approach can provide valuable insights into the spatial pattern of probable recharge areas where mitigation measures aiming at reducing groundwater recharge could be most effective.

Extracting multi-way chromatin contacts from Hi-C data

There is a growing realization that multi-way chromatin contacts formed in chromosome structures are fundamental units of gene regulation. However, due to the paucity and complexity of such contacts, it is challenging to detect and identify them using experiments. Based on an assumption that chromosome structures can be mapped onto a network of Gaussian polymer, here we derive analytic expressions for n-body contact probabilities (n > 2) among chromatin loci based on pairwise genomic contact frequencies available in Hi-C, and show that multi-way contact probability maps can in principle be extracted from Hi-C. The three-body (triplet) contact probabilities, calculated from our theory, are in good correlation with those from measurements including Tri-C, MC-4C and SPRITE. Maps of multi-way chromatin contacts calculated from our analytic expressions can not only complement experimental measurements, but also can offer better understanding of the related issues, such as cell-line dependent assemblies of multiple genes and enhancers to chromatin hubs, competition between long-range and short-range multi-way contacts, and condensates of multiple CTCF anchors.

Volcanic hazard assessment for tephra fallout in Martinique

Mount Pelée (Martinique) is one of the most active volcanoes in the Lesser Antilles arc with more than 34 magmatic events in the last 24,000 years, including the deadliest eruption of the 20th century. The current volcanic hazard map used in the civil security plan puts the emphasis on the volcanic hazard close to the volcano. This map is however based on an incomplete eruptive history and does not take into account the variability of the expected source conditions (mass eruption rate, total erupted mass, and grain-size distribution) or the wind effect on ash dispersal. We propose here to refine the volcanic hazard map for tephra fallout by using the 2-D model of ash dispersal HAZMAP. We first simulate the maximum expected eruptive scenario at Mount Pelée (i.e., the P3 eruption) using a seasonal wind profile. Building upon the good agreement with field data, we compute probability maps based on this maximum expected scenario, which show that tephra fallout hazard could threaten not only areas close to the volcano but also the southern part of Martinique. We then use a comprehensive approach based on 16 eruptive scenarios that include new field constraints obtained in the recent years on the past Plinian eruptions of Mount Pelée volcano. Each eruptive scenario considers different values of total erupted mass and mass eruption rate, and is characterized by a given probability of occurrence estimated from the refined eruptive history of the volcano. The 1979-2019 meteorological ERA-5 database is used to further take into account the daily variability of winds. These new probability maps show that the area of probable total destruction is wider when considering the 16 scenarios compared to the maximum expected scenario. The southern part of Martinique, although less threatened than when considering the maximum expected scenario, would still be impacted both by tephra fallout and by its high dependence on the water and electrical network carried from the northern part of the island. Finally, we show that key infrastructures in Martinique (such as the international airport) have a non-negligible probability of being impacted by a future Plinian eruption of the Mount Pelée. These results provide strong arguments for and will support significant and timely reconceiving of the emergency procedures as the local authorities have now placed Mount Pelée volcano on alert level yellow (vigilance) based on increased seismicity and tremor-type signals.

Extracting multi-way chromatin contacts from Hi-C data

There is a growing realization that multi-way chromatin contacts formed in chromosome structures are fundamental units of gene regulation. However, due to the paucity and complexity of such contacts, it is challenging to detect and identify them using experiments. Based on an assumption that chromosome structures can be mapped onto a network of Gaussian polymer, here we derive analytic expressions for n-body contact probabilities (n > 2) among chromatin loci based on pairwise genomic contact frequencies available in Hi-C, and show that multi-way contact probability maps can in principle be extracted from Hi-C. The three-body (triplet) contact probabilities, calculated from our theory, are in good correlation with those from measurements including Tri-C, MC-4C and SPRITE. Maps of multi-way chromatin contacts calculated from our analytic expressions can not only complement experimental measurements, but also can offer better understanding of the related issues, such as cell-line dependent assemblies of multiple genes and enhancers to chromatin hubs, competition between long-range and short-range multi-way contacts, and condensates of multiple CTCF anchors.

NIMG-42. MP-MRI-BASED TUMOR PROBABILITY MAPS TRAINED USING AUTOPSY TISSUE SAMPLES AS GROUND TRUTH NON-INVASIVELY PREDICT INFILTRATIVE TUMOR BEYOND THE CONTRAST ENHANCING REGION

Infiltrative glioma beyond contrast enhancement on MRI is often difficult to identify with conventional imaging. In this study, we use large-format autopsy samples aligned to multi-parametric MRI to test the hypothesis that radio-pathomic machine learning models are able to accurately identify areas of infiltrative tumor beyond the contrast enhancing region. At autopsy, 140 tissue samples from 62 brain cancer patients were collected from brain slices sectioned to align with the patients’ last clinical MRI prior to death. Cell, extra-cellular fluid (ECF), and cytoplasm densities were computed from digitized, hematoxylin and eosin-stained samples, and a subset of 20 slides from 9 patients were annotated for tumor presence by a pathologist-trained technician. In-house custom software was used to align the tissue samples to the patients’ last clinical imaging, which included pre- and post-contrast T1, FLAIR, and ADC images. Bagging random forest models were then trained to predict cellularity, ECF, and cytoplasm density using 5-by-5 voxel tiles from each MRI as input. A 2/3-1/3 train-test split was used to validate model generalizability. A naïve Bayes classifier was trained to predict tumor class using cellularity, ECF, and cytoplasm segmentations within the annotation data set, again using a 2/3-1/3 train-test split to validate performance. The random forest models each accurately predicted cellularity, ECF, and cytoplasm density within the test data set, with root-mean-squared error values for each falling within one standard deviation of the ground truth. The histology-based tumor prediction model accurately predicted tumor, with a test set ROC AUC of 0.86. When using whole brain cellularity, ECF, and cytoplasm predictions from the random forest models as inputs for the naïve Bayes classifier, tumor probability maps identified regions of infiltrative tumor beyond contrast enhancement. Our results suggest that radio-pathomic maps of tumor probability accurately identify regions of infiltrative tumor beyond currently accepted MRI signatures.

Exploring Geostatistical Modeling and VisualizationTechniques of Uncertainties for Categorical Spatial Data

This article presents and analyzes the indicator geostatistical modeling and some visualization techniques of uncertainty models for categorical spatial attributes. A set of sample points of some categorical attribute is used as input information. The indicator approach requires a transformation of sample points on fields of indicator samples according to the classes of interest. Experimental and theoretical semivariograms of the indicator fields are defined representing the spatial variation of the indicator information. The indicator fields, along with their semivariograms, are used to determine the uncertainty model, the conditioned probability distribution function, of the attribute at any location inside the geographic region delimited by the samples. The probability functions are considered for producing prediction and probability maps based on the maximum class probability criterion. These maps can be visualized using different techniques. In this work, it is considered individual visualization of the predicted and probability maps and a combination of them. The predicted maps can also be visualized with or without constraints related to the uncertainty probabilities. The combined visualizations are based on three-dimensional (3D) planar projection and on the Red-Green-Blue to Intensity-Hue-Saturation (RGB-IHS) fusion transformation techniques. The methodology of this article is illustrated by a case study with real data, a sample set of soil textures observed in an experimental farm located in the region of São Carlos city in São Paulo State, Brazil. The resulting maps of this case study are presented and the advantages and the drawbacks of the visualization options are analyzed and discussed.