On the origin of interface states at oxide/III-nitride heterojunction interfaces

The energy spectrum of interface state density, D-it(E), was determined at oxide/III-N heterojunction interfaces in the entire band gap, using two complementary photo-electric methods: (i) photo-assisted capacitance-voltage technique for the states distributed near the midgap and the conduction band (CB) and (ii) light intensity dependent photo-capacitance method for the states close to the valence band (VB). In addition, the Auger electron spectroscopy profiling was applied for the characterization of chemical composition of the interface region with the emphasis on carbon impurities, which can be responsible for the interface state creation. The studies were performed for the AlGaN/GaN metal-insulator-semiconductor heterostructures (MISH) with Al2O3 and SiO2 dielectric films and AlxGa1-x layers with x varying from 0.15 to 0.4 as well as for an Al2O3/InAlN/GaN MISH structure. For all structures, it was found that: (i) D-it(E) is an U-shaped continuum increasing from the midgap towards the CB and VB edges and (ii) interface states near the VB exhibit donor-like character. Furthermore, D-it(E) for SiO2/AlxGa1-x/GaN structures increased with rising x. It was also revealed that carbon impurities are not present in the oxide/III-N interface region, which indicates that probably the interface states are not related to carbon, as previously reported. Finally, it was proven that the obtained D-it(E) spectrum can be well fitted using a formula predicted by the disorder induced gap state model. This is an indication that the interface states at oxide/III-N interfaces can originate from the structural disorder of the interfacial region. Furthermore, at the oxide/barrier interface we revealed the presence of the positive fixed charge (Q(F)) which is not related to D-it(E) and which almost compensates the negative polarization charge (Q(pol)(-))

Federating heterogeneous datasets to enhance data sharing and experiment reproducibility

Recent studies have demonstrated the difficulties to replicate scientific findings and/or experiments published in past.1 The effects seen in the replicated experiments were smaller than previously reported. Some of the explanations for these findings include the complexity of the experimental design and the pressure on researches to report positive findings. The International Committee of Medical Journal Editors (ICMJE) suggests that every study considered for publication must submit a plan to share the de-identified patient data no later than 6 months after publication. There is a growing demand to enhance the management of clinical data, facilitate data sharing across institutions and also to keep track of the data from previous experiments. The ultimate goal is to assure the reproducibility of experiments in the future. This paper describes Shiny-tooth, a web based application created to improve clinical data acquisition during the clinical trial; data federation of such data as well as morphological data derived from medical images; Currently, this application is being used to store clinical data from an osteoarthritis (OA) study. This work is submitted to the SPIE Biomedical Applications in Molecular, Structural, and Functional Imaging conference

SVA: Shape variation analyzer

Temporo-mandibular osteo arthritis (TMJ OA) is characterized by progressive cartilage degradation and subchondral bone remodeling. The causes of this pathology remain unclear. Current research efforts are concentrated in finding new biomarkers that will help us understand disease progression and ultimately improve the treatment of the disease. In this work, we present Shape Variation Analyzer (SVA), the goal is to develop a noninvasive technique to provide information about shape changes in TMJ OA. SVA uses neural networks to classify morphological variations of 3D models of the mandibular condyle. The shape features used for training include normal vectors, curvature and distances to average models of the condyles. The selected features are purely geometric and are shown to favor the classification task into 6 groups generated by consensus between two clinician experts. With this new approach, we were able to accurately classify 3D models of condyles. In this paper, we present the methods used and the results obtained with this new tool

A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis

This study presents a web-system repository: Data Storage for Computation and Integration (DSCI) for Osteoarthritis of the temporomandibular joint (TMJ OA). This environment aims to maintain and allow contributions to the database from multi-clinical centers and compute novel statistics for disease classification. For this purpose, imaging datasets stored in the DSCI consisted of three-dimensional (3D) surface meshes of condyles from CBCT, clinical markers and biological markers in healthy and TMJ OA subjects. A clusterpost package was included in the web platform to be able to execute the jobs in remote computing grids. The DSCI application allowed runs of statistical packages, such as the Multivariate Functional Shape Data Analysis to compute global correlations between covariates and the morphological variability, as well as local p-values in the 3D condylar morphology. In conclusion, the DSCI allows interactive advanced statistical tools for non-statistical experts

Shape variation analyzer: A classifier for temporomandibular joint damaged by osteoarthritis

We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians. We improved the robustness of the training data using data augmentation, SMOTE, to alleviate over-fitting and to balance classes. We combined geometrical features and a shape descriptor, heat kernel signature, to describe every shape. The results were compared to nine different supervised machine learning algorithms. The deep learning neural network was the most accurate for classification of TMJ OA. In conclusion, SVA is a 3D Slicer extension that classifies pathology of the temporomandibular joint osteoarthritis cases based on 3D morphology

Diagnostic index: An open-source tool to classify TMJ OA condyles

Osteoarthritis (OA) of temporomandibular joints (TMJ) occurs in about 40% of the patients who present TMJ disorders. Despite its prevalence, OA diagnosis and treatment remain controversial since there are no clear symptoms of the disease, especially in early stages. Quantitative tools based on 3D imaging of the TMJ condyle have the potential to help characterize TMJ OA changes. The goals of the tools proposed in this study are to ultimately develop robust imaging markers for diagnosis and assessment of treatment efficacy. This work proposes to identify differences among asymptomatic controls and different clinical phenotypes of TMJ OA by means of Statistical Shape Modeling (SSM), obtained via clinical expert consensus. From three different grouping schemes (with 3, 5 and 7 groups), our best results reveal that that the majority (74.5%) of the classifications occur in agreement with the groups assigned by consensus between our clinical experts. Our findings suggest the existence of different disease-based phenotypic morphologies in TMJ OA. Our preliminary findings with statistical shape modeling based biomarkers may provide a quantitative staging of the disease. The methodology used in this study is included in an open source image analysis toolbox, to ensure reproducibility and appropriate distribution and dissemination of the solution proposed

Insulated gate and surface passivation structures for GaN-based power transistors

Recent years have witnessed GaN-based devices delivering their promise of unprecedented power and frequency levels and demonstrating their capability as an able replacement for Si-based devices. High-electron-mobility transistors (HEMTs), a key representative architecture of GaN-based devices, are well-suited for high-power and high frequency device applications, owing to highly desirable III-nitride physical properties. However, these devices are still hounded by issues not previously encountered in their more established Si- and GaAs-based devices counterparts. Metal–insulator–semiconductor (MIS) structures are usually employed with varying degrees of success in sidestepping the major problematic issues such as excessive leakage current and current instability. While different insulator materials have been applied to GaN-based transistors, the properties of insulator/III-N interfaces are still not fully understood. This is mainly due to the difficulty of characterizing insulator/AlGaN interfaces in a MIS HEMT because of the two resulting interfaces: insulator/AlGaN and AlGaN/GaN, making the potential modulation rather complicated. Although there have been many reports of low interface-trap densities in HEMT MIS capacitors, several papers have incorrectly evaluated their capacitance–voltage (C–V) characteristics. A HEMT MIS structure typically shows a 2-step C–V behavior. However, several groups reported C–V curves without the characteristic step at the forward bias regime, which is likely to the high-density states at the insulator/AlGaN interface impeding the potential control of the AlGaN surface by the gate bias. In this review paper, first we describe critical issues and problems including leakage current, current collapse and threshold voltage instability in AlGaN/GaN HEMTs. Then we present interface properties, focusing on interface states, of GaN MIS systems using oxides, nitrides and high-κ dielectrics. Next, the properties of a variety of AlGaN/GaN MIS structures as well as different characterization methods, including our own photo-assisted C–V technique, essential for understanding and developing successful surface passivation and interface control schemes, are given in the subsequent section. Finally we highlight the important progress in GaN MIS interfaces that have recently pushed the frontier of nitride-based device technology

Molecular testing in stage I–III non-small cell lung cancer : approaches and challenges

Precision medicine in non-small cell lung cancer (NSCLC) is a rapidly evolving area, with the development of targeted therapies for advanced disease and concomitant molecular testing to inform clinical decision-making. In contrast, routine molecular testing in stage I–III disease has not been required, where standard of care comprises surgery with or without adjuvant or neoadjuvant chemotherapy, or concurrent chemoradiotherapy for unresectable stage III disease, without the integration of targeted therapy. However, the phase 3 ADAURA trial has recently shown that the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI), osimertinib, reduces the risk of disease recurrence by 80% versus placebo in the adjuvant setting for patients with stage IB–IIIA EGFR mutation-positive NSCLC following complete tumor resection with or without adjuvant chemotherapy, according to physician and patient choice. Treatment with adjuvant osimertinib requires selection of patients based on the presence of an EGFR-TKI sensitizing mutation. Other targeted agents are currently being evaluated in the adjuvant and neoadjuvant settings. Approval of at least some of these other agents is highly likely in the coming years, bringing with it in parallel, a requirement for comprehensive molecular testing for stage I–III disease. In this review, we consider the implications of integrating molecular testing into practice when managing patients with stage I–III non-squamous NSCLC. We discuss best practices, approaches and challenges from pathology, surgical and oncology perspectives