Clinical and Immunopathological Features of Moyamoya Disease

Background: Moyamoya disease (MMD) is a cerebrovascular disease characterized by progressive stenosis or occlusion of the terminal portion of internal carotid arteries and the formation of a vascular network at the base of the brain. The pathogenesis of MMD is still unclear. Methodology/Principal Findings: We retrospectively analyzed clinical data for 65 consecutive patients with MMD in our institutions and evaluated the histopathological and immunohistochemical findings of intracranial vessels from 3 patients. The onset age distribution was found to have 1 peak at 40–49 year-old age group, no significant difference was observed in the female-to-male ratio (F/M = 1.2). Intracranial hemorrhage was the predominant disease type (75%). Positive family history was observed in 4.6 % of patients. Histopathological findings were a narrowed lumen due to intimal fibrous thickening without significant inflammatory cell infiltration, and the internal elastic lamina was markedly tortuous and stratified. All 3 autopsy cases showed vacuolar degeneration in the cerebrovascular smooth muscle cells. Immunohistochemical study showed the migration of smooth muscle cells in the thickened intima, and aberrant expression of IgG and S100A4 protein in vascular smooth muscle cells. The Complement C3 immunoreactivity was negative. Conclusion/Significance: This study indicated that aberrant expression of IgG and S100A4 protein in intracranial vascular wall of MMD patients, which suggested that immune-related factors may be involved in the functional and morphologica

Adaptive Prediction of Enhanced Oil Recovery by N2 huff-n-puff in Fractured-Cavity Reservoir Using an FNN-FDS Hybrid Model

N2 huff-n-puff has proven to be a promising technique to further improve oil recovery in naturally fractured-cavity carbonate reservoirs. The effect of enhanced oil recovery (EOR) by N2 huff-n-puff is significantly affected by various dynamic and static factors such as type of reservoir space, reservoir connectivity, water influx, operational parameters, and so on, typically leading to a significant increase in oil production. To reduce the prediction uncertainty of EOR performance by N2 huff-n-puff, an adaptive hybrid model was proposed based on the fundamental principles of fuzzy neural network (FNN) and fractional differential simulation (FDS); a detailed prediction process of the hybrid model was also illustrated. The accuracy of the proposed FNN-FDS hybrid model was validated using production history of N2 huff-n-puff in a typical fractured-cavity carbonate reservoir. The proposed model was also employed to predict the EOR performance by N2 huff-n-puff in a naturally fractured-cavity carbonate reservoir. The methodology can serve as an effective tool to optimize developmental design schemes when using N2 huff-n-puff to tap more remaining oil in similar types of carbonate reservoirs

Constructing a Double Alternant “Rigid-Flexible” Structure for Simultaneously Strengthening and Toughening the Interface of Carbon Fiber/Epoxy Composites

An optimized “rigid-flexible” structure with multistage gradient modulus was constructed on carbon fiber (CF) surface via chemical grafting using “flexible” polyethyleneimine (PEI) and “rigid” polydopamine (PDA) between “rigid” CF and “flexible” epoxy (EP) to elaborate a double alternant “rigid-flexible” structure for simultaneously strengthening and toughening CF/EP composites. PDA and PEI polymers can greatly enhance the roughness and wettability of CF surfaces, further strengthening the mechanical interlocking and chemical interactions between CFs and epoxy. Besides, the “rigid-flexible” structure endows the interface with a gradient transition modulus, which could uniformly transfer internal stress and effectively avoid the stress concentration. Moreover, the double alternant “rigid-flexible” could buffer the external loading, induce more micro cracks and propagation paths and, thereby, consume more energy during the destruction of the composite. The interfacial shear strength, interlaminar shear strength, impact strength increased by 80.2%, 23.5% and 167.2%, and the fracture toughness improved by 227.2%, compared with those of the unmodified CF composite, respectively. This creative strategy and design afford a promising guidance for the preparation and production of advanced CF/EP structural materials with high strength and toughness

Origin and growth mechanisms of strike-slip faults in the central Tarim cratonic basin, NW China

Through fault structure analysis and chronology study, we discuss the origin and growth mechanisms of strike-slip faults in the Tarim Basin. (1) Multiple stages strike-slip faults with inherited growth were developed in the central Tarim cratonic basin. The faults initiation time is constrained at the end of Middle Ordovician of about 460 Ma according to U-Pb dating of the fault cements and seismic interpretation. (2) The formation of the strike-slip faults was controlled by the near N-S direction stress field caused by far-field compression of the closing of the Proto-Tethys Ocean. (3) The faults localization and characteristics were influenced by the pre-existing structures of the NE trending weakening zones in the basement and lithofacies change from south to north. (4) Following the fault initiation under the Andersonian mechanism, the strike-slip fault growth was dominantly fault linkage, associated with fault tip propagation and interaction of non-Andersonian mechanisms. (5) Sequential slip accommodated deformation in the conjugate strike-slip fault interaction zones, strong localization of the main displacement and deformation occurred in the overlap zones in the northern Tarim, while the fault tips, particularly of narrow-deep grabens, and strike-slip segments in thrust zones accumulated more deformation and strain in the Central uplift. In conclusion, non-Andersonian mechanisms, dominantly fault linkage and interaction, resulted in the small displacement but long intraplate strike-slip fault development in the central Tarim Basin. The regional and localized field stress, and pre-existing structures and lithofacies difference had strong impacts on the diversity of the strike-slip faults in the Tarim cratonic basin

Reservoir Characterization and Productivity Forecast Based on Knowledge Interaction Neural Network

The reservoir characterization aims to provide the analysis and quantification of the injection-production relationship, which is the fundamental work for production management. The connectivity between injectors and producers is dominated by geological properties, especially permeability. However, the permeability parameters are very heterogenous in oil reservoirs, and expensive to collect by well logging. The commercial simulators enable to get accurate simulation but require sufficient geological properties and consume excessive computation resources. In contrast, the data-driven models (physical models and machine learning models) are developed on the observed dynamic data, such as the rate and pressure data of the injectors and producers, constructing the connectivity relationship and forecasting the productivity by a series of nonlinear mappings or the control of specific physical principles. While, due to the “black box” feature of machine learning approaches, and the constraints and assumptions of physical models, the data-driven methods often face the challenges of poor interpretability and generalizability and the limited application scopes. To solve these issues, integrating the physical principle of the waterflooding process (material balance equation) with an artificial neural network (ANN), a knowledge interaction neural network (KINN) is proposed. KINN consists of three transparent modules with explicit physical significance, and different modules are joined together via the material balance equation and work cooperatively to approximate the waterflooding process. In addition, a gate function is proposed to distinguish the dominant flowing channels from weak connecting ones by their sparsity, and thus the inter-well connectivity can be indicated directly by the model parameters. Combining the strong nonlinear mapping ability with the guidance of physical knowledge, the interpretability of KINN is fully enhanced, and the prediction accuracy on the well productivity is improved. The effectiveness of KINN is proved by comparing its performance with the canonical ANN, on the inter-well connectivity analysis and productivity forecast tasks of three synthetic reservoir experiments. Meanwhile, the robustness of KINN is revealed by the sensitivity analysis on measurement noises and wells shut-in cases