In this study, Klein and colleagues investigated the impact of minimal cancer sentinel lymph node spread and of increasing numbers of disseminated cancer cells on melanoma-specific survival. The authors found that cancer cell dissemination to the sentinel node is a quantitative risk factor for melanoma death and the best predictor of outcome was a model based on combined quantitative effects of DCCD, tumor thickness, and ulceration. Please see later in the article for the Editors' Summary

Objective. Despite observations that abnormal parietal lobe (PL) function is associated with psychotic-like experiences, our knowledge about the nature of PL involvement in schizophrenia is modest. The objective of this paper is to investigate the role of the PL in schizophrenia. Method. Medline databases were searched for English language publications using the following key words: parietal lobe, combined with schizophrenia, lesions, epilepsy, cognition, rare genetic disorders, MRI, fMRI, PET, and SPECT, respectively, followed by cross-checking of references. Results. Imaging studies in childhood onset schizophrenia suggest that grey matter abnormalities start in parietal and occipital lobes and proceed to frontal regions. Although, the findings are inconsistent, several studies with patients at risk to develop schizophrenia indicate early changes in the PL. Conclusions. We want to propose that in a proportion of individuals with emerging schizophrenia structural and functional alterations may start in the PL and progress to frontal regions.

AimsTo identify the relevance and impact of walking speed (WS) over a short distance on activities of daily living (ADLs) in patients with multiple sclerosis (MS).MethodsAn internet-administered survey of MS patients in four countries was distributed to 605 individuals in 2010. Participants had MS for > 5 years and must have reported difficulty walking as a result of MS. The impact of MS on walking and the effects of WS on ADLs were assessed based upon responses (scored on a scale of 1–10) to five questions and categorised post hoc as: high (8–10), moderate (4–7) or low (1–3) impact/importance.ResultsOf the participants who completed the survey (n = 112), 60% were female patients, 63% were aged ≥ 45 years, and 55% had relapsing-remitting MS. Approximately, half of participants reported a high impact of MS on their general walking ability (46%) and their ability to increase WS over a short distance (55%). Up to 53% of participants reported avoiding ADLs because of concerns about WS; within this cohort, older male patients and patients with secondary-progressive MS were highly represented.DiscussionThese results, which highlight the importance of WS to patients with MS and emphasise the impact of WS on health-related quality of life and ADLs, underscore the importance of clinical measures of WS, such as the timed 25-foot walk, in assessing walking in MS patients.ConclusionWalking speed over a short distance has a significant impact on ADLs for patients with MS.

In this study, a method was proposed in order to determine how well features extracted from the EEG signals for the purpose of sleep stage classification separate the sleep stages. The proposed method is based on the principle component analysis known also as the Karhunen-Loéve transform. Features frequently used in the sleep stage classification studies were divided into three main groups: (i) time-domain features, (ii) frequency-domain features, and (iii) hybrid features. That how well features in each group separate the sleep stages was determined by performing extensive simulations and it was seen that the results obtained are in agreement with those available in the literature. Considering the fact that sleep stage classification algorithms consist of two steps, namely feature extraction and classification, it will be possible to tell a priori whether the classification step will provide successful results or not without carrying out its realization thanks to the proposed method.

The compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index.