Communication: Transient Anion States Of Phenol…(h₂o)n (n = 1, 2) Complexes: Search For Microsolvation Signatures.

We report on the shape resonance spectra of phenol-water clusters, as obtained from elastic electron scattering calculations. Our results, along with virtual orbital analysis, indicate that the well-known indirect mechanism for hydrogen elimination in the gas phase is significantly impacted on by microsolvation, due to the competition between vibronic couplings on the solute and solvent molecules. This fact suggests how relevant the solvation effects could be for the electron-driven damage of biomolecules and the biomass delignification [E. M. de Oliveira et al., Phys. Rev. A 86, 020701(R) (2012)]. We also discuss microsolvation signatures in the differential cross sections that could help to identify the solvated complexes and access the composition of gaseous admixtures of these species.

[temporomandibular Disorders In Fibromyalgia Syndrome: A Short-communication.]

Fibromyalgia syndrome (FMS) is a chronic painful syndrome and the coexistence of a painful condition caused by Temporomandibular Disorders (TMD) and FMS has been frequently raised for several studies, however, more likely hypothesis is that a set of FMS characteristics may lead to the onset of TMD symptoms and they are not merely coexisting conditions. Therefore, our aim is presenting a review of literature about the relation between fibromyalgia and the signs and symptoms of temporomandibular disorders. For this purpose, a bibliographic search was performed of the period of 1990-2013, in the Medline, Pubmed, Lilacs and Scielo databases, using the keywords fibromyalgia, temporomandibular disorders and facial pain. Here we present a set of findings in the literature showing that fibromyalgia can lead to TMD symptoms. These studies demonstrated greater involvement of the stomatognathic system in FMS and myogenic disorders of masticatory system are the most commonly found in those patients. FMS appears to have a series of characteristics that constitute predisposing and triggering factors for TMD.

Sistema de sincronismo entre a colhedora de cana-de-açúcar e o veículo de transbordo

One of the problems found in mechanical harvest of sugar cane is the lack of synchronism between the harvest machine and the infield wagon, causing crop losses as well as operational capacity. The objective of the present research was to design a system capable of helping to synchronize the sugar cane harvest machine with the wagon. The communication between tractor and harvest machine is wireless. Two ultrasound sensors coupled to the elevator and a microprocessor manage such information, generating a correct synchronization among the machines. The system was tested in laboratory and on field performing its function adequately, maintaining the two machines in synchronization, indicating and alerting the operators their relative positions. The developed system reduced the sugar cane lost in 60 kg ha-1 comparing to the harvest with the system turned off.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.