Effect of enhanced masticatory force on OPG, RANKL and MGF in alveolar bone of ovariectomized rats

Menopause induces oral bone loss, leading to various oral diseases. Mastication importantly affects bone metabolism in the jawbone. Objective: To analyze the effect of enhanced masticatory force on osteoprotegerin (OPG), receptor activator of nuclear factor kappa B ligand (RANKL), and mechano–growth factor (MGF) in alveolar bone of ovariectomized rats and to study the mechanics mechanism of the alveolar bone of ovariectomized rats response to enhanced masticatory force. Methodology: Thirty Sprague Dawley rats were randomly divided into three groups: sham–operation group (fat around the removed ovary + normal hard diet), model group (ovariectomy + normal hard diet), and experimental group (ovariectomy + high hard diet). It was a 2–month experiment. Enzyme–linked immunosorbent assay (ELISA) detected serum estradiol (E2), osteocalcin (BGP) and alkaline phosphatase (ALP) in rats. Bone histomorphometric indices in the third molar region of maxilla were detected by micro-CT; protein expressions of OPG, RANKL, and MGF in the third molar region of maxilla was detected by Western blot; and gene expression of OPG, RANKL, and MGF in the third molar region of maxilla was detected by Quantitative Real–Time PCR. Results: Comparing with model group, serum E2 in experimental group increased but not significantly, serum BGP and serum ALP in experimental group decreased but not significantly, OPG in experimental group in alveolar bone increased significantly, RANKL in experimental group in alveolar bone decreased significantly, RANKL/OPG ratio in experimental group decreased significantly, MGF in experimental group in alveolar bone increased significantly, bone volume to total volume fraction increased significantly in experimental group, trabecular thickness increased significantly in experimental group, and trabecular separation decreased significantly in experimental group. Conclusion: Enhanced masticatory force affected the expression of OPG, RANKL, and MGF in alveolar bone of ovariectomized rats, improved the quality of jaw bone of ovariectomized rats, and delayed oral bone loss by ovariectomy

Distilling Knowledge from Self-Supervised Teacher by Embedding Graph Alignment

Recent advances have indicated the strengths of self-supervised pre-training for improving representation learning on downstream tasks. Existing works often utilize self-supervised pre-trained models by fine-tuning on downstream tasks. However, fine-tuning does not generalize to the case when one needs to build a customized model architecture different from the self-supervised model. In this work, we formulate a new knowledge distillation framework to transfer the knowledge from self-supervised pre-trained models to any other student network by a novel approach named Embedding Graph Alignment. Specifically, inspired by the spirit of instance discrimination in self-supervised learning, we model the instance-instance relations by a graph formulation in the feature embedding space and distill the self-supervised teacher knowledge to a student network by aligning the teacher graph and the student graph. Our distillation scheme can be flexibly applied to transfer the self-supervised knowledge to enhance representation learning on various student networks. We demonstrate that our model outperforms multiple representative knowledge distillation methods on three benchmark datasets, including CIFAR100, STL10, and TinyImageNet. Code is here: https://github.com/yccm/EGA.Comment: British Machine Vision Conference (BMVC 2022

Counterfactual Fairness for Predictions using Generative Adversarial Networks

Fairness in predictions is of direct importance in practice due to legal, ethical, and societal reasons. It is often achieved through counterfactual fairness, which ensures that the prediction for an individual is the same as that in a counterfactual world under a different sensitive attribute. However, achieving counterfactual fairness is challenging as counterfactuals are unobservable. In this paper, we develop a novel deep neural network called Generative Counterfactual Fairness Network (GCFN) for making predictions under counterfactual fairness. Specifically, we leverage a tailored generative adversarial network to directly learn the counterfactual distribution of the descendants of the sensitive attribute, which we then use to enforce fair predictions through a novel counterfactual mediator regularization. If the counterfactual distribution is learned sufficiently well, our method is mathematically guaranteed to ensure the notion of counterfactual fairness. Thereby, our GCFN addresses key shortcomings of existing baselines that are based on inferring latent variables, yet which (a) are potentially correlated with the sensitive attributes and thus lead to bias, and (b) have weak capability in constructing latent representations and thus low prediction performance. Across various experiments, our method achieves state-of-the-art performance. Using a real-world case study from recidivism prediction, we further demonstrate that our method makes meaningful predictions in practice

The Impact of COVID-19 Pandemic on Undergraduate Students’ Interest in the STEM Field

The deadly consequences of COVID-19 have been well documented, as have the social, emotional, and cognitive effects. These sequelae extend to the educational system. Much less investigated have been the potential positive outcomes of the pandemic. Given that STEM education relies heavily on hands-on laboratory experiences, STEM students may have been especially impacted by pandemic-imposed remote instruction. We surveyed 392 students at one liberal arts college querying why they continue studying in STEM or leave the STEM disciplines. Because the literature indicates that people of color and those from lower socioeconomic groups were more negatively affected by COVID-19, we hypothesized that students from traditionally marginalized groups in STEM would report greater adverse educational consequences of the pandemic as well; however, this was not borne out by the findings. Across demographic groups, students reported negative impacts of COVID-19, although in a few areas we found that more traditionally “privileged” groups complained of more negative outcomes than traditionally marginalized students did. What was most novel and dramatic in our results were the positive outcomes of the “lockdown” reported by students. These beneficial results were in the areas of enhanced resilience, improved social relationships, greater opportunities, academic improvement, and better mental health. Our paper concludes with recommendations for addressing the negative outcomes of COVID-19 and remote instruction, and for taking advantage of the unexpected positive effects

Reactions and clustering of water with silica surface

The interaction between silicasurface and water is an important topic in geophysics and materials science, yet little is known about the reaction process. In this study we use first-principles molecular dynamics to simulate the hydrolysis process of silicasurface using large cluster models. We find that a single water molecule is stable near the surface but can easily dissociate at three-coordinated silicon atom defect sites in the presence of other water molecules. These extra molecules provide a mechanism for hydrogen transfer from the original water molecule, hence catalyzing the reaction. The two-coordinated silicon atom is inert to the water molecule, and water clusters up to pentamer could be stably adsorbed at this site at room temperature.Peer reviewe

Magnetic properties of vacancies in graphene and single-walled carbon nanotubes

Spin-polarized density functional theory has been used to study the properties of vacancies in a graphene sheet and in single-walled carbon nanotubes (SWNTs). For graphene, we find that the vacancies are magnetic and the symmetry of the sheet is broken by the distortion of an atom next to the vacancy site. We also studied vacancies in four armchair SWNTs from (3,3) to (6,6) and six zigzag SWNTs from (5,0) to (10,0). Our calculations demonstrate that vacancies can change the electronic structure of SWNTs, converting some metallic nanotubes to semiconductors and vice versa. Metallic nanotubes with vacancies exhibit ferro- or ferrimagnetism, whereas some semiconducting nanotubes with vacancies show an antiferromagnetic order. The magnetic properties depend on chiralities of the tubes, the configuration of the vacancy and the concentration of the vacancies.Peer reviewe