Stability after bilateral sagittal split osteotomy advancement surgery with rigid internal fixation: a systematic review

PURPOSE: The purpose of this systematic review was to evaluate horizontal relapse and its causes in bilateral sagittal split advancement osteotomy (BSSO) with rigid internal fixation of different types. MATERIALS AND METHODS: A search of the literature was performed in the databases PubMed, Ovid, Cochrane Library, and Google Scholar Beta. From 488 articles identified, 24 articles were finally included. Six studies were prospective, and 18 were retrospective. The range of postoperative study records was 6 months to 12.7 years. RESULTS: The short-term relapse for bicortical screws was between 1.5% and 32.7%, for miniplates between 1.5% and 18.0%, and for bioresorbable bicortical screws between 10.4% and 17.4%, at point B. The long-term relapse for bicortical screws was between 2.0% and 50.3%, and for miniplates between 1.5% and 8.9%, at point B. CONCLUSIONS: BSSO for mandibular advancement is a good treatment option for skeletal Class II, but seems less stable than BSSO setback in the short and long terms. Bicortical screws of titanium, stainless steel, or bioresorbable material show little difference regarding skeletal stability compared with miniplates in the short term. A greater number of studies with larger skeletal long-term relapse rates were evident in patients treated with bicortical screws instead of miniplates. The etiology of relapse is multifactorial, involving the proper seating of the condyles, the amount of advancement, the soft tissue and muscles, the mandibular plane angle, the remaining growth and remodeling, the skill of the surgeon, and preoperative age. Patients with a low mandibular plane angle have increased vertical relapse, whereas patients with a high mandibular plane angle have more horizontal relapse. Advancements in the range of 6 to 7 mm or more predispose to horizontal relapse. To obtain reliable scientific evidence, further short-term and long-term research into BSSO advancement with rigid internal fixation should exclude additional surgery, ie, genioplasty or maxillary surgery, and include a prospective study or randomized clinical trial design with correlation statistics.

Interactive express statistical system /

"UIUCDCS-R-74-653"

Mathematical contributions to the theory of evolution

Mode of access: Internet.

Major ion chemistry of snow cores along a transect in central Dronning Maud Land, Antarctica

Among the large variety of particulates in the atmosphere, calcic mineral dust particles have highly reactive surfaces that undergo heterogeneous reactions with nitrogen oxides contiguously. The association between Ca2+, an important proxy indicator of mineral dust and NO3-, a dominant anion in the Antarctic snow pack was analysed. A total of 41 snow cores (~ 1 m each) that represent snow deposited during 2008-2009 were studied along coastal-inland transects from two different regions - the Princess Elizabeth Land (PEL) and central Dronning Maud Land (cDML) in East Antarctica. Correlation statistics showed a strong association (at 99 % significance level) between NO3- and Ca2+ at the near-coastal sections of both PEL (r = 0.72) and cDML (r = 0.76) transects. Similarly, a strong association between these ions was also observed in snow deposits at the inland sections of PEL (r = 0.8) and cDML (r = 0.85). Such systematic associations between Ca2+ and NO3- is attributed to the interaction between calcic mineral dust and nitrogen oxides in the atmosphere, leading to the possible formation of calcium nitrate (Ca(NO3)2). Forward and back trajectory analyses using HYSPLIT model v. 4 revealed that Southern South America (SSA) was an important dust emitting source to the study region, aided by the westerlies. Particle size distribution showed that over 90 % of the dust was in the range < 4 µm, indicating that these dust particles reached the Antarctic region via long range transport from the SSA region. We propose that the association between Ca2+ and NO3- occurs during the long range transport due to the formation of Ca(NO3)2. The Ca(NO3)2 thus formed in the atmosphere undergo deposition over Antarctica under the influence of anticyclonic polar easterlies. However, influence of local dust sources from the nunataks in cDML evidently mask such association in the mountainous region. The study indicates that the input of dust-bound NO3- may contribute a significant fraction of the total NO3- deposited in Antarctic snow.

FOR-TSM: desarrollo de una herramienta de pronósticos con modelos de series de tiempo

Uno de los temas más complejos y necesarios en los cursos de Administración de Operaciones, es el uso de los pronósticos con modelos de series de tiempo (TSM por sus siglas en inglés) -- Para facilitar el entendimiento y ayudar a los estudiantes a comprender fácilmente los pronósticos de demanda, este proyecto presenta FOR TSM, una herramienta desarrollada en MS Excel VBA® -- La herramienta fue diseñada con una Interfaz gráfica de Usuario (GUI por sus siglas en inglés) para explicar conceptos fundamentales como la selección de los parámetros, los valores de inicialización, cálculo y análisis de medidas de desempeño y finalmente la selección de modelos

Modeling multivariate autoregressive conditional heteroskedasticity with the double smooth transition conditional correlation GARCH Model

In this paper, we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new double smooth transition conditional correlation (DSTCC) GARCH model extends the smooth transition conditional correlation (STCC) GARCH model of Silvennoinen and Teräsvirta (2005) by including another variable according to which the correlations change smoothly between states of constant correlations. A Lagrange multiplier test is derived to test the constancy of correlations against the DSTCC-GARCH model, and another one to test for another transition in the STCC-GARCH framework. In addition, other specification tests, with the aim of aiding the model building procedure, are considered. Analytical expressions for the test statistics and the required derivatives are provided. Applying the model to the stock and bond futures data, we discover that the correlation pattern between them has dramatically changed around the turn of the century. The model is also applied to a selection of world stock indices, and we find evidence for an increasing degree of integration in the capital markets.

Application of higher order statistics/spectra in biomedical signals : a review

For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations. Higher order spectra, also known as polyspectra, are spectral representations of higher order statistics, i.e. moments and cumulants of third order and beyond. HOS (higher order statistics or higher order spectra) can detect deviations from linearity, stationarity or Gaussianity in the signal. Most of the biomedical signals are non-linear, non-stationary and non-Gaussian in nature and therefore it can be more advantageous to analyze them with HOS compared to the use of second order correlations and power spectra. In this paper we have discussed the application of HOS for different bio-signals. HOS methods of analysis are explained using a typical heart rate variability (HRV) signal and applications to other signals are reviewed.

Learning detectors quickly with stationary statistics

Computer vision is increasingly becoming interested in the rapid estimation of object detectors. The canonical strategy of using Hard Negative Mining to train a Support Vector Machine is slow, since the large negative set must be traversed at least once per detector. Recent work has demonstrated that, with an assumption of signal stationarity, Linear Discriminant Analysis is able to learn comparable detectors without ever revisiting the negative set. Even with this insight, the time to learn a detector can still be on the order of minutes. Correlation filters, on the other hand, can produce a detector in under a second. However, this involves the unnatural assumption that the statistics are periodic, and requires the negative set to be re-sampled per detector size. These two methods differ chie y in the structure which they impose on the co- variance matrix of all examples. This paper is a comparative study which develops techniques (i) to assume periodic statistics without needing to revisit the negative set and (ii) to accelerate the estimation of detectors with aperiodic statistics. It is experimentally verified that periodicity is detrimental.

Intra-cluster correlation structure in longitudinal data analysis: Selection criteria and misspecification tests

Selection criteria and misspecification tests for the intra-cluster correlation structure (ICS) in longitudinal data analysis are considered. In particular, the asymptotical distribution of the correlation information criterion (CIC) is derived and a new method for selecting a working ICS is proposed by standardizing the selection criterion as the p-value. The CIC test is found to be powerful in detecting misspecification of the working ICS structures, while with respect to the working ICS selection, the standardized CIC test is also shown to have satisfactory performance. Some simulation studies and applications to two real longitudinal datasets are made to illustrate how these criteria and tests might be useful.

Quantile regression for longitudinal data with a working correlation model

This paper proposes a linear quantile regression analysis method for longitudinal data that combines the between- and within-subject estimating functions, which incorporates the correlations between repeated measurements. Therefore, the proposed method results in more efficient parameter estimation relative to the estimating functions based on an independence working model. To reduce computational burdens, the induced smoothing method is introduced to obtain parameter estimates and their variances. Under some regularity conditions, the estimators derived by the induced smoothing method are consistent and have asymptotically normal distributions. A number of simulation studies are carried out to evaluate the performance of the proposed method. The results indicate that the efficiency gain for the proposed method is substantial especially when strong within correlations exist. Finally, a dataset from the audiology growth research is used to illustrate the proposed methodology.

Modeling strategies in longitudinal data analysis: Covariate, variance function and correlation structure selection

A modeling paradigm is proposed for covariate, variance and working correlation structure selection for longitudinal data analysis. Appropriate selection of covariates is pertinent to correct variance modeling and selecting the appropriate covariates and variance function is vital to correlation structure selection. This leads to a stepwise model selection procedure that deploys a combination of different model selection criteria. Although these criteria find a common theoretical root based on approximating the Kullback-Leibler distance, they are designed to address different aspects of model selection and have different merits and limitations. For example, the extended quasi-likelihood information criterion (EQIC) with a covariance penalty performs well for covariate selection even when the working variance function is misspecified, but EQIC contains little information on correlation structures. The proposed model selection strategies are outlined and a Monte Carlo assessment of their finite sample properties is reported. Two longitudinal studies are used for illustration.

Working-correlation-structure identification in generalized estimating equations

Selecting an appropriate working correlation structure is pertinent to clustered data analysis using generalized estimating equations (GEE) because an inappropriate choice will lead to inefficient parameter estimation. We investigate the well-known criterion of QIC for selecting a working correlation Structure. and have found that performance of the QIC is deteriorated by a term that is theoretically independent of the correlation structures but has to be estimated with an error. This leads LIS to propose a correlation information criterion (CIC) that substantially improves the QIC performance. Extensive simulation studies indicate that the CIC has remarkable improvement in selecting the correct correlation structures. We also illustrate our findings using a data set from the Madras Longitudinal Schizophrenia Study.

Implications of the recent high statistics determination of the pion electromagnetic form factor in the timelike region

The recently evaluated two-pion contribution to the muon g - 2 and the phase of the pion electromagnetic form factor in the elastic region, known from pi pi scattering by Fermi-Watson theorem, are exploited by analytic techniques for finding correlations between the coefficients of the Taylor expansion at t = 0 and the values of the form factor at several points in the spacelike region. We do not use specific parametrizations, and the results are fully independent of the unknown phase in the inelastic region. Using for instance, from recent determinations, < r(pi)(2)> = (0.435 +/- 0.005) fm(2) and F(-1.6 GeV2) = 0.243(-0.014)(+0.022), we obtain the allowed ranges 3.75 GeV-4 less than or similar to c less than or similar to 3.98 GeV-4 and 9.91 GeV-6 less than or similar to d less than or similar to 10.46 GeV-6 for the curvature and the next Taylor coefficient, with a strong correlation between them. We also predict a large region in the complex plane where the form factor cannot have zeros.

Effects Of Subgrid-Scale Modeling On Lagrangian Statistics In Large-Eddy Simulation

The application of large-eddy simulation (LES) to turbulent transport processes requires accurate prediction of the Lagrangian statistics of flow fields. However, in most existing SGS models, no explicit consideration is given to Lagrangian statistics. In this paper, we focus on the effects of SGS modeling on Lagrangian statistics in LES ranging from statistics determining single-particle dispersion to those of pair dispersion and multiparticle dispersion. Lagrangian statistics in homogeneous isotropic turbulence are extracted from direct numerical simulation (DNS) and the LES with a spectral eddy-viscosity model. For the case of longtime single-particle dispersion, it is shown that, compared to DNS, LES overpredicts the time scale of the Lagrangian velocity correlation but underpredicts the Lagrangian velocity fluctuation. These two effects tend to cancel one another leading to an accurate prediction of the longtime turbulent dispersion coefficient. Unlike the single-particle dispersion, LES tends to underestimate significantly the rate of relative dispersion of particle pairs and multiple-particles, when initial separation distances are less than the minimum resolved scale due to the lack of subgrid fluctuations. The overprediction of LES on the time scale of the Lagrangian velocity correlation is further confirmed by a theoretical analysis using a turbulence closure theory.