3-D finite element analysis of the effects of post location and loading location on stress distribution in root canals of the mandibular 1st molar

Objective The purpose of this study was to evaluate, by using finite element analysis, the influence of post location and occlusal loading location on the stress distribution pattern inside the root canals of the mandibular 1st molar. Material and Methods Three different 3-D models of the mandibular 1st molar were established: no post (NP) – a model of endodontic and prosthodontic treatments; mesiobuccal post (MP) – a model of endodontic and prosthodontic treatments with a post in the mesiobuccal canal; and distal post (DP) – a model of endodontic and prosthodontic treatments with a post in the distal canal. A vertical force of 300 N, perpendicular to the occlusal plane, was applied to one of five 1 mm2 areas on the occlusal surface; mesial marginal ridge, distal marginal ridge, mesiobuccal cusp, distobuccal cusp, and central fossa. Finite element analysis was used to calculate the equivalent von Mises stresses on each root canal. Results The DP model showed similar maximum stress values to the NP model, while the MP model showed markedly greater maximum stress values. The post procedure increased stress concentration inside the canals, although this was significantly affected by the site of the force. Conclusions In the mandibular 1st molar, the distal canal is the better place to insert the post than the mesiobuccal canal. However, if insertion into the mesiobuccal canal is unavoidable, there should be consideration on the occlusal contact, making central fossa and distal marginal ridge the main functioning areas

Striatal neuroinflammation promotes parkinsonism in rats

The specific role of neuroinflammation in the pathogenesis of Parkinson's disease remains to be fully elucidated. By infusing lipopolysaccharide (LPS) into the striatum, we investigated the effect of neuroinflammation on the dopamine nigrostriatal pathway. Here, we report that LPS-induced neuroinflammation in the striatum causes progressive degeneration of the dopamine nigrostriatal system, which is accompanied by motor impairments resembling parkinsonism. Our results indicate that neurodegeneration is associated with defects in the mitochondrial respiratory chain related to extensive S-nitrosylation/nitration of mitochondrial proteins. Mitochondrial injury was prevented by treatment of L-N^6^-(l-iminoethyl)-lysine, an inducible nitric oxide synthase (iNOS) inhibitor, suggesting that iNOS-derived NO is responsible for mitochondrial dysfunction. Furthermore, the nigral dopamine neurons exhibited intracytoplasmic [alpha]-synuclein and ubiquitin accumulation. These results demonstrate that degeneration of nigral dopamine neurons by neuroinflammation is associated with mitochondrial malfunction induced by NO-mediated S-nitrosylation/nitration of mitochondrial proteins

Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior

Recent Vision-Language Pretrained (VLP) models have become the backbone for many downstream tasks, but they are utilized as frozen model without learning. Prompt learning is a method to improve the pre-trained VLP model by adding a learnable context vector to the inputs of the text encoder. In a few-shot learning scenario of the downstream task, MLE training can lead the context vector to over-fit dominant image features in the training data. This overfitting can potentially harm the generalization ability, especially in the presence of a distribution shift between the training and test dataset. This paper presents a Bayesian-based framework of prompt learning, which could alleviate the overfitting issues on few-shot learning application and increase the adaptability of prompts on unseen instances. Specifically, modeling data-dependent prior enhances the adaptability of text features for both seen and unseen image features without the trade-off of performance between them. Based on the Bayesian framework, we utilize the Wasserstein Gradient Flow in the estimation of our target posterior distribution, which enables our prompt to be flexible in capturing the complex modes of image features. We demonstrate the effectiveness of our method on benchmark datasets for several experiments by showing statistically significant improvements on performance compared to existing methods. The code is available at https://github.com/youngjae-cho/APP.Comment: Accepted to AAAI-202

Refraction traveltime tomography using damped monochromatic wavefield

For complicated earth models, wave-equation–based refraction-traveltime tomography is more accurate than ray-based tomography but requires more computational effort. Most of the computational effort in traveltime tomography comes from computing traveltimes and their Fr´echet derivatives, which for ray-based methods can be computed directly. However, in most wave-equation traveltime-tomography algorithms, the steepest descent direction of the objective function is computed by the backprojection algorithm, without computing a Fr ´echet derivative directly. We propose a new wave-based refraction-traveltime– tomography procedure that computes Fr´echet derivatives directly and efficiently. Our method involves solving a damped-wave equation using a frequency-domain, finite-element modeling algorithm at a single frequency and invoking the reciprocity theorem. A damping factor, which is commonly used to suppress wraparound effects in frequency-domain modeling, plays the role of suppressing multievent wavefields. By limiting the wavefield to a single first arrival, we are able to extract the first-arrival traveltime from the phase term without applying a time window. Computing the partial derivative of the damped wave-equation solution using the reciprocity theorem enables us to compute the Fr ´echet derivative of amplitude, as well as that of traveltime, with respect to subsurface parameters. Using the Marmousi-2 model, we demonstrate numerically that refraction traveltime tomography with large-offset data can be used to provide the smooth initial velocity model necessary for prestack depth migration.This work was financially supported by the National Laboratory Project of the Ministry of Science and Technology and the Brain Korea 21 project of the Ministry of Education. We are also grateful to Prof. K. J. Marfurt of the University of Houston and Dr. M. Schoenberger for editing our manuscript