How long does treatment with fixed orthodontic appliances last? A systematic review

INTRODUCTION There is little agreement on the expected duration of a course of orthodontic treatment; however, a consensus appears to have emerged that fixed appliance treatment is overly lengthy. This has spawned numerous novel approaches directed at reducing the duration of treatment, occasionally with an acceptance that occlusal outcomes may be compromised. The aim of this study was to determine the mean duration and the number of visits required for comprehensive orthodontic treatment involving fixed appliances. METHODS Multiple electronic databases were searched with no language restrictions, authors were contacted as required, and reference lists of potentially relevant studies were screened. Randomized controlled trials and nonrandomized prospective studies concerning fixed appliance treatment with treatment duration as an outcome measure were included. Data extraction and quality assessment were performed independently and in duplicate. RESULTS Twenty-five studies were included after screening: 20 randomized controlled trials and 5 controlled clinical trials. Twenty-two studies were eligible for meta-analysis after quality assessment. The mean treatment duration derived from the 22 included studies involving 1089 participants was 19.9 months (95% confidence interval, 19.58, 20.22 months). Sensitivity analyses were carried out including 3 additional studies, resulting in average duration of treatment of 20.02 months (95% confidence interval, 19.71, 20.32 months) based on data from 1211 participants. The mean number of required visits derived from 5 studies was 17.81 (95% confidence interval, 15.47, 20.15 visits). CONCLUSIONS Based on prospective studies carried out in university settings, comprehensive orthodontic treatment on average requires less than 2 years to complete

Practical Robust Control Using Self-regulation Nonlinear PID Controller for Pneumatic Positioning System

This paper investigates the robustness of the pneumatic positioning system controlled by Self-regulation Nonlinear PID (SNPID) controller. This controller is executed by utilizing the characteristic of rate variation of the nonlinear gain that are readily available in Nonlinear PID (NPID) controller. A Self-regulation Nonlinear Function (SNF) is used to reprocess the error signal with the purpose to generate the value of the rate variation, continuously. Simulation and experimental tests are conducted. The controller is implemented to a variably loads and pressures. The comparison with the other existing method i.e. NPID and conventional PID are performed and evaluated. The effectiveness of SNPID + Dead Zone Compensator (DZC) has been successfully demonstrated and proved through simulation and experimental studie

Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease

The joint analysis of biomedical data in Alzheimer's Disease (AD) is important for better clinical diagnosis and to understand the relationship between biomarkers. However, jointly accounting for heterogeneous measures poses important challenges related to the modeling of the variability and the interpretability of the results. These issues are here addressed by proposing a novel multi-channel stochastic generative model. We assume that a latent variable generates the data observed through different channels (e.g., clinical scores, imaging, ...) and describe an efficient way to estimate jointly the distribution of both latent variable and data generative process. Experiments on synthetic data show that the multi-channel formulation allows superior data reconstruction as opposed to the single channel one. Moreover, the derived lower bound of the model evidence represents a promising model selection criterion. Experiments on AD data show that the model parameters can be used for unsupervised patient stratification and for the joint interpretation of the heterogeneous observations. Because of its general and flexible formulation, we believe that the proposed method can find important applications as a general data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with MICCAI 2018, September 20, Granada, Spai

Compressible primitive equation: formal derivation and stability of weak solutions

We present a formal derivation of a simplified version of Compressible Primitive Equations (CPEs) for atmosphere modeling. They are obtained from $3$-D compressible Navier-Stokes equations with an \emph{anisotropic viscous stress tensor} where viscosity depends on the density. We then study the stability of the weak solutions of this model by using an intermediate model, called model problem, which is more simple and practical, to achieve the main result

Pengaruh Strategi Pembelajaran Numbered Heads Together (Nht) Terhadap Hasil Belajar Siswa SMA Negeri 1 Muara Badak

This research aims at investigating the effect of NHT learning strategy on students' learning outcomes. This is quasi experimental research. The design of this research was Pretest-Posttest Non-equivalent Control Design. The population of this research was all the students of class XI of public senior high school students in Muara Badak in the first semester of 2015/2016 academic year. The samples of this research were the students of class XI natural science 1 and XI natural science 2. There were 30 students in each of the class. The data were obtained from essay tests. The data were analyzed using Ancova. The results of the analysis showed that the learning strategy had an effect on the learning outcomes. It was observed from the comparison of the mean corrected score that the NHT learning strategy had a more significant effect as much as 21,56%, compared to the effect from the conventional learning.Penelitian ini bertujuan untuk mengetahui pengaruh strategi pembelajaran Numbered Head Together terhadap hasil belajar siswa. Jenis penelitian ini adalah quasy eksperimen. Desain penelitian yang digunakan adalah Pretest-Postest Non-equivalent Control Design. Populasi penelitian ini seluruh siswa kelas XI SMAN 1 di Muara Badak pada semester ganjil tahun pelajaran 2015/2016. Sampel penelitian ini adalah siswa kelas XI IPA 1 dan XI IPA 2. Dua kelas yang digunakan masing-masing berjumlah 30 siswa. Data diperoleh dari hasil belajar kognitif. Data penelitian ini dianalisis menggunakan Anakova. Berdasarkan hasil analisis menunjukkan bahwa strategi pembelajaran memberikan pengaruh terhadap hasil belajar kognitif siswa. Hal ini dilihat dari perbandingan rerata terkoreksi diketahui bahwa strategi pembelajaran NHT memberikan pengaruh lebih besar, yaitu sebesar 21,56%, dibandingkan pengaruh yang disebabkan oleh pembelajaran konvensional

Substrate-induced band gap opening in epitaxial graphene

Graphene has shown great application potentials as the host material for next generation electronic devices. However, despite its intriguing properties, one of the biggest hurdles for graphene to be useful as an electronic material is its lacking of an energy gap in the electronic spectra. This, for example, prevents the use of graphene in making transistors. Although several proposals have been made to open a gap in graphene's electronic spectra, they all require complex engineering of the graphene layer. Here we show that when graphene is epitaxially grown on the SiC substrate, a gap of ~ 0.26 is produced. This gap decreases as the sample thickness increases and eventually approaches zero when the number of layers exceeds four. We propose that the origin of this gap is the breaking of sublattice symmetry owing to the graphene-substrate interaction. We believe our results highlight a promising direction for band gap engineering of graphene.Comment: 10 pages, 4 figures; updated reference

An improvement in support vector machine classification model using grey relational analysis for cancer diagnosis

To further improve the accuracy of classifier for cancer diagnosis, a hybrid model called GRA-SVM which comprises Support Vector Machine classifier and filter feature selection Grey Relational Analysis is proposed and tested against Wisconsin Breast Cancer Dataset (WBCD) and BUPA Disorder Dataset. The performance of GRA-SVM is compared to SVM’s in terms of accuracy, sensitivity, specificity and Area under Curve (AUC). The experimental results reveal that GRA-SVM improves the SVM accuracy of about 0.48 by using only two features for the WBCD dataset. For BUPA dataset, GRA-SVM improves the SVM accuracy of about 0.97 by using four features. Besides improving the accuracy performance, GRA-SVM also produces a ranking scheme that provides information about the priority of each feature. Therefore, based on the benefits gained, GRA-SVM is recommended as a new approach to obtain a better and more accurate result for cancer diagnosis

UVB radiation induced effects on cells studied by FTIR spectroscopy

We have made a preliminary analysis of the results about the eVects on tumoral cell line (lymphoid T cell line Jurkat) induced by UVB radiation (dose of 310 mJ/cm^2) with and without a vegetable mixture. In the present study, we have used two techniques: Fourier transform infrared spectroscopy (FTIR) and flow cytometry. FTIR spectroscopy has the potential to provide the identiWcation of the vibrational modes of some of the major compounds (lipid, proteins and nucleic acids) without being invasive in the biomaterials. The second technique has allowed us to perform measurements of cytotoxicity and to assess the percentage of apoptosis. We already studied the induction of apoptotic process in the same cell line by UVB radiation; in particular, we looked for correspondences and correlations between FTIR spetroscopy and flow cytometry data finding three highly probable spectroscopic markers of apoptosis (Pozzi et al. in Radiat Res 168:698-705, 2007). In the present work, the results have shown significant changes in the absorbance and spectral pattern in the wavenumber protein and nucleic acids regions after the treatments

The Sunyaev-Zeldovich Effect and Its Cosmological Significance

Comptonization of the cosmic microwave background (CMB) radiation by hot gas in clusters of galaxies - the Sunyaev-Zeldovich (S-Z) effect - is of great astrophysical and cosmological significance. In recent years observations of the effect have improved tremendously; high signal-to-noise images of the effect (at low microwave frequencies) can now be obtained by ground-based interferometric arrays. In the near future, high frequency measurements of the effect will be made with bolomateric arrays during long duration balloon flights. Towards the end of the decade the PLANCK satellite will extensive S-Z surveys over a wide frequency range. Along with the improved observational capabilities, the theoretical description of the effect and its more precise use as a probe have been considerably advanced. I review the current status of theoretical and observational work on the effect, and the main results from its use as a cosmological probe.Comment: Invited review; in proceedings of the Erice NATO/ASI `Astrophysical Sources of High Energy Particles and Radiation'; 11 pages, 3 figure

Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available