A Multi-Point Geostatistical Seismic Inversion Method Based on Local Probability Updating of Lithofacies

In order to solve the problem that elastic parameter constraints are not taken into account in local lithofacies updating in multi-point geostatistical inversion, a new multi-point geostatistical inversion method with local facies updating under seismic elastic constraints is proposed. The main improvement of the method is that the probability of multi-point facies modeling is combined with the facies probability reflected by the optimal elastic parameters retained from the previous inversion to predict and update the current lithofacies model. Constrained by the current lithofacies model, the elastic parameters were obtained via direct sampling based on the statistical relationship between the lithofacies and the elastic parameters. Forward simulation records were generated via convolution and were compared with the actual seismic records to obtain the optimal lithofacies and elastic parameters. The inversion method adopts the internal and external double cycle iteration mechanism, and the internal cycle updates and inverts the local lithofacies. The outer cycle determines whether the correlation between the entire seismic record and the actual seismic record meets the given conditions, and the cycle iterates until the given conditions are met in order to achieve seismic inversion prediction. The theoretical model of the Stanford Center for Reservoir Forecasting and the practical model of the Xinchang gas field in western China were used to test the new method. The results show that the correlation between the synthetic seismic records and the actual seismic records is the best, and the lithofacies matching degree of the inversion is the highest. The results of the conventional multi-point geostatistical inversion are the next best, and the results of the two-point geostatistical inversion are the worst. The results show that the reservoir parameters obtained using the local probability updating of lithofacies method are closer to the actual reservoir parameters. This method is worth popularizing in practical exploration and development.

Nanoscale organization of Nicastrin, the substrate receptor of the γ-secretase complex, as independent molecular domains

Alterations in the canonical processing of Amyloid Precursor Protein generate proteoforms that contribute to the onset of Alzheimer’s Disease. Modified composition of γ-secretase or mutations in its subunits has been directly linked to altered generation of Amyloid beta. Despite biochemical evidence about the role of γ-secretase in the generation of APP, the molecular origin of how spatial heterogeneity in the generation of proteoforms arises is not well understood. Here, we evaluated the localization of Nicastrin, a γ-secretase subunit, at nanometer sized functional zones of the synapse. With the help of super resolution microscopy, we confirm that Nicastrin is organized into nanodomains of high molecular density within an excitatory synapse. A similar nanoorganization was also observed for APP and the catalytic subunit of γ-secretase, Presenilin 1, that were discretely associated with Nicastrin nanodomains. Though Nicastrin is a functional subunit of γ-secretase, the Nicastrin and Presenilin1 nanodomains were either colocalized or localized independent of each other. The Nicastrin and Presenilin domains highlight a potential independent regulation of these molecules different from their canonical secretase function. The collisions between secretases and substrate molecules decide the probability and rate of product formation for transmembrane proteolysis. Our observations of secretase nanodomains indicate a spatial difference in the confinement of substrate and secretases, affecting the local probability of product formation by increasing their molecular availability, resulting in differential generation of proteoforms even within single synapses.

Improving the Performance of Deep Learning Model-Based Classification by the Analysis of Local Probability

Generally, the performance of deep learning-based classification models is highly related to the captured features of training samples. When a sample is not clear or contains a similar number of features of many objects, we cannot easily classify what it is. Actually, human beings classify objects by not only the features but also some information such as the probability of these objects in an environment. For example, when we know further information such as one object has a higher probability in the environment than the others, we can easily give the answer about what is in the sample. We call this kind of probability as local probability as this is related to the local environment. In this paper, we carried out a new framework that is named L-PDL to improve the performance of deep learning based on the analysis of this kind of local probability. Firstly, our method trains the deep learning model on the training set. Then, we can get the probability of objects on each sample by this trained model. Secondly, we get the posterior local probability of objects on the validation set. Finally, this probability conditionally cooperates with the probability of objects on testing samples. We select three popular deep learning models on three real datasets for the evaluation. The experimental results show that our method can obviously improve the performance on the real datasets, which is better than the state-of-the-art methods.

The evolving temperature field in a 1-m methanol pool fire

Thin filament pyrometry is used to measure the time-varying temperature field in a 1-m methanol pool fire. A digital camera with optical filters and zoom lens recorded the emission intensity of an array of 12-µm silicon–carbide filaments oriented horizontally at various heights across the steadily burning pool fire. A 50-µm-diameter thermocouple measured the temperature at locations corresponding to the filament positions. A correlation was developed between the local probability density functions of the thermocouple time-series measurements corrected for radiation and thermal inertia effects and the camera grayscale pixel intensity of the filaments. A regression analysis yields the local mean temperature and its variance. The time series of the temperature field is transformed into average values during consecutive phases of the fire’s puffing cycle, providing quantitative insight into the complex and dynamic structure of a turbulent fire.

Deterministic and stochastic rules of branching govern dendritic morphogenesis of sensory neurons

Dendrite morphology is necessary for the correct integration of inputs that neurons receive. The branching mechanisms allowing neurons to acquire their type-specific morphology remain unclear. Classically, axon and dendrite patterns were shown to be guided by molecules providing deterministic cues. However, the extent to which deterministic and stochastic mechanisms, based upon purely statistical bias, contribute to the emergence of dendrite shape is largely unknown. We address this issue using the Drosophila class I vpda multi-dendritic neurons. Detailed quantitative analysis of vpda dendrite morphogenesis indicates that the primary branch grows very robustly in a fixed direction while secondary branch numbers and lengths showed fluctuations characteristic of stochastic systems. Live tracking dendrites and computational modeling revealed how neuron shape emerges from few local statistical parameters of branch dynamics. We report key opposing aspects of how tree architecture feedbacks on the local probability of branch shrinkage. Child branches promote stabilization of parent branches while self-repulsion promotes shrinkage. Finally, we show that self-repulsion, mediated by the adhesion molecule Dscam1, indirectly patterns the growth of secondary branches by spatially restricting their direction of stable growth perpendicular to the primary branch. Thus, the stochastic nature of secondary branch dynamics and the existence of geometric feedback emphasizes the importance of self-organization in neuronal dendrite morphogenesis.