Possible Association of Periodontal Disease and Macular Degeneration: A Case-Control Study.

Oral pathogens have been identified in bioptic specimens from Age-Related Macular Degeneration (ARMD) patients, and alveolar bone loss has been related to ARMD. Therefore, the possible association between ARMD and periodontal disease was investigated in the present case-control study, evaluating clinical and radiographic periodontal parameters, primarily, in cases vs. controls and, secondarily, in relation to ARMD risk factors, in cases, to highlight a possible pathogenic link between the disorders.

Nonlinear autoregressive sieve bootstrap based on extreme learning machines.

The aim of the paper is to propose and discuss a sieve bootstrap scheme based on Extreme Learning Machines for non linear time series. The procedure is fully nonparametric in its spirit and retains the conceptual simplicity of the residual bootstrap. Using Extreme Learning Machines in the resampling scheme can dramatically reduce the computational burden of the bootstrap procedure, with performances comparable to the NN-Sieve bootstrap and computing time similar to the ARSieve bootstrap.

A 5-year longitudinal cohort study on crown to implant ratio effect on marginal bone level in single implants.

Background: A 5-year longitudinal cohort study was carried out to evaluate the influence of anatomical crown to implant ratio (CIR) on peri-implant marginal bone level (MBL) in single implants.

Materials And Methods: The longest possible implants, according to the availability of pristine bone, were inserted, one per patient, among periodontally healthy teeth in consecutively recruited subjects. CIR and MBL changes were measured on standardized radiographs.

Designing neural networks for modeling biological data: a statistical perspective.

In this paper, we propose a strategy for the selection of the hidden layer size in feedforward neural network models. The procedure herein presented is based on comparison of different models in terms of their out of sample predictive ability, for a specified loss function. To overcome the problem of data snooping, we extend the scheme based on the use of the reality check with modifications apt to compare nested models.