Computer–Aided Simulation of Mastoidectomy

AbstractObjectiveTo establish a three–dimensional model of the temporal bone using CT scan images for study of temporal bone structures and simulation of mastoidectomy procedures.MethodsCT scan images from 6 individuals (12 temporal bones) were used to reconstruct the Fallopian canal, internal auditory canal, cochlea, semicircular canals, sigmoid sinus, posterior fossa floor and jugular bulb on a computer platform. Their anatomical relations within the temporal bone were restored in the computed model. The same model was used to simulate mastoidectomy procedures.ResultsThe reconstructed computer model provided accurate and clear three–dimensional images of temporal bone structures. Simulation of mastoidectomy using these images provided procedural experiences closely mimicking the real surgical procedure.ConclusionComputer–aided three dimensional reconstruction of temporal bone structures using CT scan images is a useful tool in surgical simulation and can aid surgical procedure planning. Key words three–dimension reconstruction, CT scan, surgery simulatio

Quantum blockade and loop current induced by a single lattice defect in graphene nanoribbons

We investigate theoretically the electronic transport properties in narrow graphene ribbons with an adatom-induced defect. It is found that the lowest conductance step of a metallic graphene nanoribbon may develop a dip even down to zero at certain values of the Fermi energy due to the defect. Accompanying the occurrence of the conductance dip, a loop current develops around the defect. We show how the properties of the conductance dip depend on the parameters of the defect, such as the relative position and severity of the defect as well as the width and edges of the graphene ribbons. In particular, for metallic armchair-edges graphene nanoribbons, whether the conductance dip appears or not, they can be controlled by choosing the position of the single defect.Comment: 6 pages, 6 figure

The conserved aromatic residue W-122 is a determinant of potyviral coat protein stability, replication, and cell-to-cell movement in plants

Coat proteins (CPs) play critical roles in potyvirus cell-to-cell movement. However, the underlying mechanism controlling them remains unclear. Here, we show that substitutions of alanine, glutamic acid, or lysine for the conserved residue tryptophan at position 122 (W-122) in tobacco vein banding mosaic virus (TVBMV) CP abolished virus cell-to-cell movement in Nicotiana benthamiana plants. In agroinfiltrated N. benthamiana leaf patches, both the CP and RNA accumulation levels of three W-122 mutant viruses were significantly reduced compared with those of wild-type TVBMV, and CP accumulated to a low level similar to that of a replication-deficient mutant. The results of polyprotein transient expression experiments indicated that CP instability was responsible for the significantly low CP accumulation levels of the three W-122 mutant viruses. The substitution of W-122 did not affect CP plasmodesmata localization or virus particle formation; however, the substitution significantly reduced the number of virus particles. The wild-type TVBMV CP could complement the reduced replication and abolished cell-to-cell movement of the mutant viruses. When the codon for W-122 was mutated to that for a different aromatic residue, phenylalanine or tyrosine, the resultant mutant viruses moved systemically and accumulated up to 80% of the wild-type TVBMV level. Similar results were obtained for the corresponding amino acids of W-122 in the watermelon mosaic virus and potato virus Y CPs. Therefore, we conclude that the aromatic ring in W-122 in the core domain of the potyviral CP is critical for cell-to-cell movement through the effects on CP stability and viral replication.Peer reviewe

Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China

ObjectiveInsulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the “common soil” of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings.MethodsWe analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models.ResultsThe LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc.ConclusionThe ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings